research topics in psychology with two variables

61 intriguing psychology research topics to explore

Last updated

11 January 2024

Reviewed by

Brittany Ferri, PhD, OTR/L

Psychology is an incredibly diverse, critical, and ever-changing area of study in the medical and health industries. Because of this, it’s a common area of study for students and healthcare professionals.

We’re walking you through picking the perfect topic for your upcoming paper or study. Keep reading for plenty of example topics to pique your interest and curiosity.

• How to choose a psychology research topic

Exploring a psychology-based topic for your research project? You need to pick a specific area of interest to collect compelling data. 

Use these tips to help you narrow down which psychology topics to research:

Focus on a particular area of psychology

The most effective psychological research focuses on a smaller, niche concept or disorder within the scope of a study. 

Psychology is a broad and fascinating area of science, including everything from diagnosed mental health disorders to sports performance mindset assessments. 

This gives you plenty of different avenues to explore. Having a hard time choosing? Check out our list of 61 ideas further down in this article to get started.

Read the latest clinical studies

Once you’ve picked a more niche topic to explore, you need to do your due diligence and explore other research projects on the same topic. 

This practice will help you learn more about your chosen topic, ask more specific questions, and avoid covering existing projects. 

For the best results, we recommend creating a research folder of associated published papers to reference throughout your project. This makes it much easier to cite direct references and find inspiration down the line.

Find a topic you enjoy and ask questions

Once you’ve spent time researching and collecting references for your study, you finally get to explore. 

Whether this research project is for work, school, or just for fun, having a passion for your research will make the project much more enjoyable. (Trust us, there will be times when that is the only thing that keeps you going.) 

Now you’ve decided on the topic, ask more nuanced questions you might want to explore. 

If you can, pick the direction that interests you the most to make the research process much more enjoyable.

• 61 psychology topics to research in 2024

Need some extra help starting your psychology research project on the right foot? Explore our list of 61 cutting-edge, in-demand psychology research topics to use as a starting point for your research journey.

• Psychology research topics for university students

As a university student, it can be hard to pick a research topic that fits the scope of your classes and is still compelling and unique. 

Here are a few exciting topics we recommend exploring for your next assigned research project:

Mental health in post-secondary students

Seeking post-secondary education is a stressful and overwhelming experience for most students, making this topic a great choice to explore for your in-class research paper. 

Examples of post-secondary mental health research topics include:

Student mental health status during exam season

Mental health disorder prevalence based on study major

The impact of chronic school stress on overall quality of life

The impacts of cyberbullying

Cyberbullying can occur at all ages, starting as early as elementary school and carrying through into professional workplaces. 

Examples of cyberbullying-based research topics you can study include:

The impact of cyberbullying on self-esteem

Common reasons people engage in cyberbullying 

Cyberbullying themes and commonly used terms

Cyberbullying habits in children vs. adults

The long-term effects of cyberbullying

• Clinical psychology research topics

If you’re looking to take a more clinical approach to your next project, here are a few topics that involve direct patient assessment for you to consider:

Chronic pain and mental health

Living with chronic pain dramatically impacts every aspect of a person’s life, including their mental and emotional health. 

Here are a few examples of in-demand pain-related psychology research topics:

The connection between diabetic neuropathy and depression

Neurological pain and its connection to mental health disorders

Efficacy of meditation and mindfulness for pain management

The long-term effects of insomnia

Insomnia is where you have difficulty falling or staying asleep. It’s a common health concern that impacts millions of people worldwide. 

This is an excellent topic because insomnia can have a variety of causes, offering many research possibilities. 

Here are a few compelling psychology research topics about insomnia you could investigate:

The prevalence of insomnia based on age, gender, and ethnicity

Insomnia and its impact on workplace productivity

The connection between insomnia and mental health disorders

Efficacy and use of melatonin supplements for insomnia

The risks and benefits of prescription insomnia medications

Lifestyle options for managing insomnia symptoms

The efficacy of mental health treatment options

Management and treatment of mental health conditions is an ever-changing area of study. If you can witness or participate in mental health therapies, this can make a great research project. 

Examples of mental health treatment-related psychology research topics include:

The efficacy of cognitive behavioral therapy (CBT) for patients with severe anxiety

The benefits and drawbacks of group vs. individual therapy sessions

Music therapy for mental health disorders

Electroconvulsive therapy (ECT) for patients with depression 

• Controversial psychology research paper topics

If you are looking to explore a more cutting-edge or modern psychology topic, you can delve into a variety of controversial and topical options:

The impact of social media and digital platforms

Ever since access to internet forums and video games became more commonplace, there’s been growing concern about the impact these digital platforms have on mental health. 

Examples of social media and video game-related psychology research topics include:

The effect of edited images on self-confidence

How social media platforms impact social behavior

Video games and their impact on teenage anger and violence

Digital communication and the rapid spread of misinformation

The development of digital friendships

Psychotropic medications for mental health

In recent years, the interest in using psychoactive medications to treat and manage health conditions has increased despite their inherently controversial nature. 

Examples of psychotropic medication-related research topics include:

The risks and benefits of using psilocybin mushrooms for managing anxiety

The impact of marijuana on early-onset psychosis

Childhood marijuana use and related prevalence of mental health conditions

Ketamine and its use for complex PTSD (C-PTSD) symptom management

The effect of long-term psychedelic use and mental health conditions

• Mental health disorder research topics

As one of the most popular subsections of psychology, studying mental health disorders and how they impact quality of life is an essential and impactful area of research. 

While studies in these areas are common, there’s always room for additional exploration, including the following hot-button topics:

Anxiety and depression disorders

Anxiety and depression are well-known and heavily researched mental health disorders. 

Despite this, we still don’t know many things about these conditions, making them great candidates for psychology research projects:

Social anxiety and its connection to chronic loneliness

C-PTSD symptoms and causes

The development of phobias

Obsessive-compulsive disorder (OCD) behaviors and symptoms

Depression triggers and causes

Self-care tools and resources for depression

The prevalence of anxiety and depression in particular age groups or geographic areas

Bipolar disorder

Bipolar disorder is a complex and multi-faceted area of psychology research. 

Use your research skills to learn more about this condition and its impact by choosing any of the following topics:

Early signs of bipolar disorder

The incidence of bipolar disorder in young adults

The efficacy of existing bipolar treatment options

Bipolar medication side effects

Cognitive behavioral therapy for people with bipolar 

Schizoaffective disorder

Schizoaffective disorder is often stigmatized, and less common mental health disorders are a hotbed for new and exciting research. 

Here are a few examples of interesting research topics related to this mental health disorder:

The prevalence of schizoaffective disorder by certain age groups or geographic locations

Risk factors for developing schizoaffective disorder

The prevalence and content of auditory and visual hallucinations

Alternative therapies for schizoaffective disorder

• Societal and systematic psychology research topics

Modern society’s impact is deeply enmeshed in our mental and emotional health on a personal and community level. 

Here are a few examples of societal and systemic psychology research topics to explore in more detail:

Access to mental health services

While mental health awareness has risen over the past few decades, access to quality mental health treatment and resources is still not equitable. 

This can significantly impact the severity of a person’s mental health symptoms, which can result in worse health outcomes if left untreated. 

Explore this crucial issue and provide information about the need for improved mental health resource access by studying any of the following topics:

Rural vs. urban access to mental health resources

Access to crisis lines by location

Wait times for emergency mental health services

Inequities in mental health access based on income and location

Insurance coverage for mental health services

Systemic racism and mental health

Societal systems and the prevalence of systemic racism heavily impact every aspect of a person’s overall health.

Researching these topics draws attention to existing problems and contributes valuable insights into ways to improve access to care moving forward.

Examples of systemic racism-related psychology research topics include: 

Access to mental health resources based on race

The prevalence of BIPOC mental health therapists in a chosen area

The impact of systemic racism on mental health and self-worth

Racism training for mental health workers

The prevalence of mental health disorders in discriminated groups

LGBTQIA+ mental health concerns

Research about LGBTQIA+ people and their mental health needs is a unique area of study to explore for your next research project. It’s a commonly overlooked and underserved community.

Examples of LGBTQIA+ psychology research topics to consider include:

Mental health supports for queer teens and children

The impact of queer safe spaces on mental health

The prevalence of mental health disorders in the LGBTQIA+ community

The benefits of queer mentorship and found family

Substance misuse in LQBTQIA+ youth and adults

• Collect data and identify trends with Dovetail

Psychology research is an exciting and competitive study area, making it the perfect choice for projects or papers.

Take the headache out of analyzing your data and instantly access the insights you need to complete your next psychology research project by teaming up with Dovetail today.

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7.2 Correlational Research

Learning objectives.

• Define correlational research and give several examples.
• Explain why a researcher might choose to conduct correlational research rather than experimental research or another type of nonexperimental research.

What Is Correlational Research?

Correlational research is a type of nonexperimental research in which the researcher measures two variables and assesses the statistical relationship (i.e., the correlation) between them with little or no effort to control extraneous variables. There are essentially two reasons that researchers interested in statistical relationships between variables would choose to conduct a correlational study rather than an experiment. The first is that they do not believe that the statistical relationship is a causal one. For example, a researcher might evaluate the validity of a brief extraversion test by administering it to a large group of participants along with a longer extraversion test that has already been shown to be valid. This researcher might then check to see whether participants’ scores on the brief test are strongly correlated with their scores on the longer one. Neither test score is thought to cause the other, so there is no independent variable to manipulate. In fact, the terms independent variable and dependent variable do not apply to this kind of research.

The other reason that researchers would choose to use a correlational study rather than an experiment is that the statistical relationship of interest is thought to be causal, but the researcher cannot manipulate the independent variable because it is impossible, impractical, or unethical. For example, Allen Kanner and his colleagues thought that the number of “daily hassles” (e.g., rude salespeople, heavy traffic) that people experience affects the number of physical and psychological symptoms they have (Kanner, Coyne, Schaefer, & Lazarus, 1981). But because they could not manipulate the number of daily hassles their participants experienced, they had to settle for measuring the number of daily hassles—along with the number of symptoms—using self-report questionnaires. Although the strong positive relationship they found between these two variables is consistent with their idea that hassles cause symptoms, it is also consistent with the idea that symptoms cause hassles or that some third variable (e.g., neuroticism) causes both.

A common misconception among beginning researchers is that correlational research must involve two quantitative variables, such as scores on two extraversion tests or the number of hassles and number of symptoms people have experienced. However, the defining feature of correlational research is that the two variables are measured—neither one is manipulated—and this is true regardless of whether the variables are quantitative or categorical. Imagine, for example, that a researcher administers the Rosenberg Self-Esteem Scale to 50 American college students and 50 Japanese college students. Although this “feels” like a between-subjects experiment, it is a correlational study because the researcher did not manipulate the students’ nationalities. The same is true of the study by Cacioppo and Petty comparing college faculty and factory workers in terms of their need for cognition. It is a correlational study because the researchers did not manipulate the participants’ occupations.

Figure 7.2 “Results of a Hypothetical Study on Whether People Who Make Daily To-Do Lists Experience Less Stress Than People Who Do Not Make Such Lists” shows data from a hypothetical study on the relationship between whether people make a daily list of things to do (a “to-do list”) and stress. Notice that it is unclear whether this is an experiment or a correlational study because it is unclear whether the independent variable was manipulated. If the researcher randomly assigned some participants to make daily to-do lists and others not to, then it is an experiment. If the researcher simply asked participants whether they made daily to-do lists, then it is a correlational study. The distinction is important because if the study was an experiment, then it could be concluded that making the daily to-do lists reduced participants’ stress. But if it was a correlational study, it could only be concluded that these variables are statistically related. Perhaps being stressed has a negative effect on people’s ability to plan ahead (the directionality problem). Or perhaps people who are more conscientious are more likely to make to-do lists and less likely to be stressed (the third-variable problem). The crucial point is that what defines a study as experimental or correlational is not the variables being studied, nor whether the variables are quantitative or categorical, nor the type of graph or statistics used to analyze the data. It is how the study is conducted.

Figure 7.2 Results of a Hypothetical Study on Whether People Who Make Daily To-Do Lists Experience Less Stress Than People Who Do Not Make Such Lists

Data Collection in Correlational Research

Again, the defining feature of correlational research is that neither variable is manipulated. It does not matter how or where the variables are measured. A researcher could have participants come to a laboratory to complete a computerized backward digit span task and a computerized risky decision-making task and then assess the relationship between participants’ scores on the two tasks. Or a researcher could go to a shopping mall to ask people about their attitudes toward the environment and their shopping habits and then assess the relationship between these two variables. Both of these studies would be correlational because no independent variable is manipulated. However, because some approaches to data collection are strongly associated with correlational research, it makes sense to discuss them here. The two we will focus on are naturalistic observation and archival data. A third, survey research, is discussed in its own chapter.

Naturalistic Observation

Naturalistic observation is an approach to data collection that involves observing people’s behavior in the environment in which it typically occurs. Thus naturalistic observation is a type of field research (as opposed to a type of laboratory research). It could involve observing shoppers in a grocery store, children on a school playground, or psychiatric inpatients in their wards. Researchers engaged in naturalistic observation usually make their observations as unobtrusively as possible so that participants are often not aware that they are being studied. Ethically, this is considered to be acceptable if the participants remain anonymous and the behavior occurs in a public setting where people would not normally have an expectation of privacy. Grocery shoppers putting items into their shopping carts, for example, are engaged in public behavior that is easily observable by store employees and other shoppers. For this reason, most researchers would consider it ethically acceptable to observe them for a study. On the other hand, one of the arguments against the ethicality of the naturalistic observation of “bathroom behavior” discussed earlier in the book is that people have a reasonable expectation of privacy even in a public restroom and that this expectation was violated.

Researchers Robert Levine and Ara Norenzayan used naturalistic observation to study differences in the “pace of life” across countries (Levine & Norenzayan, 1999). One of their measures involved observing pedestrians in a large city to see how long it took them to walk 60 feet. They found that people in some countries walked reliably faster than people in other countries. For example, people in the United States and Japan covered 60 feet in about 12 seconds on average, while people in Brazil and Romania took close to 17 seconds.

Because naturalistic observation takes place in the complex and even chaotic “real world,” there are two closely related issues that researchers must deal with before collecting data. The first is sampling. When, where, and under what conditions will the observations be made, and who exactly will be observed? Levine and Norenzayan described their sampling process as follows:

Male and female walking speed over a distance of 60 feet was measured in at least two locations in main downtown areas in each city. Measurements were taken during main business hours on clear summer days. All locations were flat, unobstructed, had broad sidewalks, and were sufficiently uncrowded to allow pedestrians to move at potentially maximum speeds. To control for the effects of socializing, only pedestrians walking alone were used. Children, individuals with obvious physical handicaps, and window-shoppers were not timed. Thirty-five men and 35 women were timed in most cities. (p. 186)

Precise specification of the sampling process in this way makes data collection manageable for the observers, and it also provides some control over important extraneous variables. For example, by making their observations on clear summer days in all countries, Levine and Norenzayan controlled for effects of the weather on people’s walking speeds.

The second issue is measurement. What specific behaviors will be observed? In Levine and Norenzayan’s study, measurement was relatively straightforward. They simply measured out a 60-foot distance along a city sidewalk and then used a stopwatch to time participants as they walked over that distance. Often, however, the behaviors of interest are not so obvious or objective. For example, researchers Robert Kraut and Robert Johnston wanted to study bowlers’ reactions to their shots, both when they were facing the pins and then when they turned toward their companions (Kraut & Johnston, 1979). But what “reactions” should they observe? Based on previous research and their own pilot testing, Kraut and Johnston created a list of reactions that included “closed smile,” “open smile,” “laugh,” “neutral face,” “look down,” “look away,” and “face cover” (covering one’s face with one’s hands). The observers committed this list to memory and then practiced by coding the reactions of bowlers who had been videotaped. During the actual study, the observers spoke into an audio recorder, describing the reactions they observed. Among the most interesting results of this study was that bowlers rarely smiled while they still faced the pins. They were much more likely to smile after they turned toward their companions, suggesting that smiling is not purely an expression of happiness but also a form of social communication.

Naturalistic observation has revealed that bowlers tend to smile when they turn away from the pins and toward their companions, suggesting that smiling is not purely an expression of happiness but also a form of social communication.

sieneke toering – bowling big lebowski style – CC BY-NC-ND 2.0.

When the observations require a judgment on the part of the observers—as in Kraut and Johnston’s study—this process is often described as coding . Coding generally requires clearly defining a set of target behaviors. The observers then categorize participants individually in terms of which behavior they have engaged in and the number of times they engaged in each behavior. The observers might even record the duration of each behavior. The target behaviors must be defined in such a way that different observers code them in the same way. This is the issue of interrater reliability. Researchers are expected to demonstrate the interrater reliability of their coding procedure by having multiple raters code the same behaviors independently and then showing that the different observers are in close agreement. Kraut and Johnston, for example, video recorded a subset of their participants’ reactions and had two observers independently code them. The two observers showed that they agreed on the reactions that were exhibited 97% of the time, indicating good interrater reliability.

Archival Data

Another approach to correlational research is the use of archival data , which are data that have already been collected for some other purpose. An example is a study by Brett Pelham and his colleagues on “implicit egotism”—the tendency for people to prefer people, places, and things that are similar to themselves (Pelham, Carvallo, & Jones, 2005). In one study, they examined Social Security records to show that women with the names Virginia, Georgia, Louise, and Florence were especially likely to have moved to the states of Virginia, Georgia, Louisiana, and Florida, respectively.

As with naturalistic observation, measurement can be more or less straightforward when working with archival data. For example, counting the number of people named Virginia who live in various states based on Social Security records is relatively straightforward. But consider a study by Christopher Peterson and his colleagues on the relationship between optimism and health using data that had been collected many years before for a study on adult development (Peterson, Seligman, & Vaillant, 1988). In the 1940s, healthy male college students had completed an open-ended questionnaire about difficult wartime experiences. In the late 1980s, Peterson and his colleagues reviewed the men’s questionnaire responses to obtain a measure of explanatory style—their habitual ways of explaining bad events that happen to them. More pessimistic people tend to blame themselves and expect long-term negative consequences that affect many aspects of their lives, while more optimistic people tend to blame outside forces and expect limited negative consequences. To obtain a measure of explanatory style for each participant, the researchers used a procedure in which all negative events mentioned in the questionnaire responses, and any causal explanations for them, were identified and written on index cards. These were given to a separate group of raters who rated each explanation in terms of three separate dimensions of optimism-pessimism. These ratings were then averaged to produce an explanatory style score for each participant. The researchers then assessed the statistical relationship between the men’s explanatory style as college students and archival measures of their health at approximately 60 years of age. The primary result was that the more optimistic the men were as college students, the healthier they were as older men. Pearson’s r was +.25.

This is an example of content analysis —a family of systematic approaches to measurement using complex archival data. Just as naturalistic observation requires specifying the behaviors of interest and then noting them as they occur, content analysis requires specifying keywords, phrases, or ideas and then finding all occurrences of them in the data. These occurrences can then be counted, timed (e.g., the amount of time devoted to entertainment topics on the nightly news show), or analyzed in a variety of other ways.

Key Takeaways

• Correlational research involves measuring two variables and assessing the relationship between them, with no manipulation of an independent variable.
• Correlational research is not defined by where or how the data are collected. However, some approaches to data collection are strongly associated with correlational research. These include naturalistic observation (in which researchers observe people’s behavior in the context in which it normally occurs) and the use of archival data that were already collected for some other purpose.

Discussion: For each of the following, decide whether it is most likely that the study described is experimental or correlational and explain why.

• An educational researcher compares the academic performance of students from the “rich” side of town with that of students from the “poor” side of town.
• A cognitive psychologist compares the ability of people to recall words that they were instructed to “read” with their ability to recall words that they were instructed to “imagine.”
• A manager studies the correlation between new employees’ college grade point averages and their first-year performance reports.
• An automotive engineer installs different stick shifts in a new car prototype, each time asking several people to rate how comfortable the stick shift feels.
• A food scientist studies the relationship between the temperature inside people’s refrigerators and the amount of bacteria on their food.
• A social psychologist tells some research participants that they need to hurry over to the next building to complete a study. She tells others that they can take their time. Then she observes whether they stop to help a research assistant who is pretending to be hurt.

Kanner, A. D., Coyne, J. C., Schaefer, C., & Lazarus, R. S. (1981). Comparison of two modes of stress measurement: Daily hassles and uplifts versus major life events. Journal of Behavioral Medicine, 4 , 1–39.

Kraut, R. E., & Johnston, R. E. (1979). Social and emotional messages of smiling: An ethological approach. Journal of Personality and Social Psychology, 37 , 1539–1553.

Levine, R. V., & Norenzayan, A. (1999). The pace of life in 31 countries. Journal of Cross-Cultural Psychology, 30 , 178–205.

Pelham, B. W., Carvallo, M., & Jones, J. T. (2005). Implicit egotism. Current Directions in Psychological Science, 14 , 106–110.

Peterson, C., Seligman, M. E. P., & Vaillant, G. E. (1988). Pessimistic explanatory style is a risk factor for physical illness: A thirty-five year longitudinal study. Journal of Personality and Social Psychology, 55 , 23–27.

Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Correlational Research

Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton

Learning Objectives

• Define correlational research and give several examples.
• Explain why a researcher might choose to conduct correlational research rather than experimental research or another type of non-experimental research.
• Interpret the strength and direction of different correlation coefficients.
• Explain why correlation does not imply causation.

What Is Correlational Research?

Correlational research is a type of non-experimental research in which the researcher measures two variables (binary or continuous) and assesses the statistical relationship (i.e., the correlation) between them with little or no effort to control extraneous variables. There are many reasons that researchers interested in statistical relationships between variables would choose to conduct a correlational study rather than an experiment. The first is that they do not believe that the statistical relationship is a causal one or are not interested in causal relationships. Recall two goals of science are to describe and to predict and the correlational research strategy allows researchers to achieve both of these goals. Specifically, this strategy can be used to describe the strength and direction of the relationship between two variables and if there is a relationship between the variables then the researchers can use scores on one variable to predict scores on the other (using a statistical technique called regression, which is discussed further in the section on Complex Correlation in this chapter).

Another reason that researchers would choose to use a correlational study rather than an experiment is that the statistical relationship of interest is thought to be causal, but the researcher  cannot manipulate the independent variable because it is impossible, impractical, or unethical. For example, while a researcher might be interested in the relationship between the frequency people use cannabis and their memory abilities they cannot ethically manipulate the frequency that people use cannabis. As such, they must rely on the correlational research strategy; they must simply measure the frequency that people use cannabis and measure their memory abilities using a standardized test of memory and then determine whether the frequency people use cannabis is statistically related to memory test performance. 

Correlation is also used to establish the reliability and validity of measurements. For example, a researcher might evaluate the validity of a brief extraversion test by administering it to a large group of participants along with a longer extraversion test that has already been shown to be valid. This researcher might then check to see whether participants’ scores on the brief test are strongly correlated with their scores on the longer one. Neither test score is thought to cause the other, so there is no independent variable to manipulate. In fact, the terms  independent variable  and dependent variabl e  do not apply to this kind of research.

Another strength of correlational research is that it is often higher in external validity than experimental research. Recall there is typically a trade-off between internal validity and external validity. As greater controls are added to experiments, internal validity is increased but often at the expense of external validity as artificial conditions are introduced that do not exist in reality. In contrast, correlational studies typically have low internal validity because nothing is manipulated or controlled but they often have high external validity. Since nothing is manipulated or controlled by the experimenter the results are more likely to reflect relationships that exist in the real world.

Finally, extending upon this trade-off between internal and external validity, correlational research can help to provide converging evidence for a theory. If a theory is supported by a true experiment that is high in internal validity as well as by a correlational study that is high in external validity then the researchers can have more confidence in the validity of their theory. As a concrete example, correlational studies establishing that there is a relationship between watching violent television and aggressive behavior have been complemented by experimental studies confirming that the relationship is a causal one (Bushman & Huesmann, 2001) [1] .

Does Correlational Research Always Involve Quantitative Variables?

A common misconception among beginning researchers is that correlational research must involve two quantitative variables, such as scores on two extraversion tests or the number of daily hassles and number of symptoms people have experienced. However, the defining feature of correlational research is that the two variables are measured—neither one is manipulated—and this is true regardless of whether the variables are quantitative or categorical. Imagine, for example, that a researcher administers the Rosenberg Self-Esteem Scale to 50 American college students and 50 Japanese college students. Although this “feels” like a between-subjects experiment, it is a correlational study because the researcher did not manipulate the students’ nationalities. The same is true of the study by Cacioppo and Petty comparing college faculty and factory workers in terms of their need for cognition. It is a correlational study because the researchers did not manipulate the participants’ occupations.

Figure 6.2 shows data from a hypothetical study on the relationship between whether people make a daily list of things to do (a “to-do list”) and stress. Notice that it is unclear whether this is an experiment or a correlational study because it is unclear whether the independent variable was manipulated. If the researcher randomly assigned some participants to make daily to-do lists and others not to, then it is an experiment. If the researcher simply asked participants whether they made daily to-do lists, then it is a correlational study. The distinction is important because if the study was an experiment, then it could be concluded that making the daily to-do lists reduced participants’ stress. But if it was a correlational study, it could only be concluded that these variables are statistically related. Perhaps being stressed has a negative effect on people’s ability to plan ahead (the directionality problem). Or perhaps people who are more conscientious are more likely to make to-do lists and less likely to be stressed (the third-variable problem). The crucial point is that what defines a study as experimental or correlational is not the variables being studied, nor whether the variables are quantitative or categorical, nor the type of graph or statistics used to analyze the data. What defines a study is how the study is conducted.

Data Collection in Correlational Research

Again, the defining feature of correlational research is that neither variable is manipulated. It does not matter how or where the variables are measured. A researcher could have participants come to a laboratory to complete a computerized backward digit span task and a computerized risky decision-making task and then assess the relationship between participants’ scores on the two tasks. Or a researcher could go to a shopping mall to ask people about their attitudes toward the environment and their shopping habits and then assess the relationship between these two variables. Both of these studies would be correlational because no independent variable is manipulated. 

Correlations Between Quantitative Variables

Correlations between quantitative variables are often presented using scatterplots . Figure 6.3 shows some hypothetical data on the relationship between the amount of stress people are under and the number of physical symptoms they have. Each point in the scatterplot represents one person’s score on both variables. For example, the circled point in Figure 6.3 represents a person whose stress score was 10 and who had three physical symptoms. Taking all the points into account, one can see that people under more stress tend to have more physical symptoms. This is a good example of a positive relationship , in which higher scores on one variable tend to be associated with higher scores on the other. In other words, they move in the same direction, either both up or both down. A negative relationship is one in which higher scores on one variable tend to be associated with lower scores on the other. In other words, they move in opposite directions. There is a negative relationship between stress and immune system functioning, for example, because higher stress is associated with lower immune system functioning.

The strength of a correlation between quantitative variables is typically measured using a statistic called  Pearson’s Correlation Coefficient (or Pearson's  r ) . As Figure 6.4 shows, Pearson’s r ranges from −1.00 (the strongest possible negative relationship) to +1.00 (the strongest possible positive relationship). A value of 0 means there is no relationship between the two variables. When Pearson’s  r  is 0, the points on a scatterplot form a shapeless “cloud.” As its value moves toward −1.00 or +1.00, the points come closer and closer to falling on a single straight line. Correlation coefficients near ±.10 are considered small, values near ± .30 are considered medium, and values near ±.50 are considered large. Notice that the sign of Pearson’s  r  is unrelated to its strength. Pearson’s  r  values of +.30 and −.30, for example, are equally strong; it is just that one represents a moderate positive relationship and the other a moderate negative relationship. With the exception of reliability coefficients, most correlations that we find in Psychology are small or moderate in size. The website http://rpsychologist.com/d3/correlation/ , created by Kristoffer Magnusson, provides an excellent interactive visualization of correlations that permits you to adjust the strength and direction of a correlation while witnessing the corresponding changes to the scatterplot.

There are two common situations in which the value of Pearson’s  r  can be misleading. Pearson’s  r  is a good measure only for linear relationships, in which the points are best approximated by a straight line. It is not a good measure for nonlinear relationships, in which the points are better approximated by a curved line. Figure 6.5, for example, shows a hypothetical relationship between the amount of sleep people get per night and their level of depression. In this example, the line that best approximates the points is a curve—a kind of upside-down “U”—because people who get about eight hours of sleep tend to be the least depressed. Those who get too little sleep and those who get too much sleep tend to be more depressed. Even though Figure 6.5 shows a fairly strong relationship between depression and sleep, Pearson’s  r  would be close to zero because the points in the scatterplot are not well fit by a single straight line. This means that it is important to make a scatterplot and confirm that a relationship is approximately linear before using Pearson’s  r . Nonlinear relationships are fairly common in psychology, but measuring their strength is beyond the scope of this book.

The other common situations in which the value of Pearson’s  r  can be misleading is when one or both of the variables have a limited range in the sample relative to the population. This problem is referred to as  restriction of range . Assume, for example, that there is a strong negative correlation between people’s age and their enjoyment of hip hop music as shown by the scatterplot in Figure 6.6. Pearson’s  r  here is −.77. However, if we were to collect data only from 18- to 24-year-olds—represented by the shaded area of Figure 6.6—then the relationship would seem to be quite weak. In fact, Pearson’s  r  for this restricted range of ages is 0. It is a good idea, therefore, to design studies to avoid restriction of range. For example, if age is one of your primary variables, then you can plan to collect data from people of a wide range of ages. Because restriction of range is not always anticipated or easily avoidable, however, it is good practice to examine your data for possible restriction of range and to interpret Pearson’s  r  in light of it. (There are also statistical methods to correct Pearson’s  r  for restriction of range, but they are beyond the scope of this book).

Correlation Does Not Imply Causation

You have probably heard repeatedly that “Correlation does not imply causation.” An amusing example of this comes from a 2012 study that showed a positive correlation (Pearson’s r = 0.79) between the per capita chocolate consumption of a nation and the number of Nobel prizes awarded to citizens of that nation [2] . It seems clear, however, that this does not mean that eating chocolate causes people to win Nobel prizes, and it would not make sense to try to increase the number of Nobel prizes won by recommending that parents feed their children more chocolate.

There are two reasons that correlation does not imply causation. The first is called the  directionality problem . Two variables,  X  and  Y , can be statistically related because X  causes  Y  or because  Y  causes  X . Consider, for example, a study showing that whether or not people exercise is statistically related to how happy they are—such that people who exercise are happier on average than people who do not. This statistical relationship is consistent with the idea that exercising causes happiness, but it is also consistent with the idea that happiness causes exercise. Perhaps being happy gives people more energy or leads them to seek opportunities to socialize with others by going to the gym. The second reason that correlation does not imply causation is called the  third-variable problem . Two variables,  X  and  Y , can be statistically related not because  X  causes  Y , or because  Y  causes  X , but because some third variable,  Z , causes both  X  and  Y . For example, the fact that nations that have won more Nobel prizes tend to have higher chocolate consumption probably reflects geography in that European countries tend to have higher rates of per capita chocolate consumption and invest more in education and technology (once again, per capita) than many other countries in the world. Similarly, the statistical relationship between exercise and happiness could mean that some third variable, such as physical health, causes both of the others. Being physically healthy could cause people to exercise and cause them to be happier. Correlations that are a result of a third-variable are often referred to as  spurious correlations .

Some excellent and amusing examples of spurious correlations can be found at http://www.tylervigen.com  (Figure 6.7  provides one such example).

“Lots of Candy Could Lead to Violence”

Although researchers in psychology know that correlation does not imply causation, many journalists do not. One website about correlation and causation, http://jonathan.mueller.faculty.noctrl.edu/100/correlation_or_causation.htm , links to dozens of media reports about real biomedical and psychological research. Many of the headlines suggest that a causal relationship has been demonstrated when a careful reading of the articles shows that it has not because of the directionality and third-variable problems.

One such article is about a study showing that children who ate candy every day were more likely than other children to be arrested for a violent offense later in life. But could candy really “lead to” violence, as the headline suggests? What alternative explanations can you think of for this statistical relationship? How could the headline be rewritten so that it is not misleading?

As you have learned by reading this book, there are various ways that researchers address the directionality and third-variable problems. The most effective is to conduct an experiment. For example, instead of simply measuring how much people exercise, a researcher could bring people into a laboratory and randomly assign half of them to run on a treadmill for 15 minutes and the rest to sit on a couch for 15 minutes. Although this seems like a minor change to the research design, it is extremely important. Now if the exercisers end up in more positive moods than those who did not exercise, it cannot be because their moods affected how much they exercised (because it was the researcher who used random assignment to determine how much they exercised). Likewise, it cannot be because some third variable (e.g., physical health) affected both how much they exercised and what mood they were in. Thus experiments eliminate the directionality and third-variable problems and allow researchers to draw firm conclusions about causal relationships.

Media Attributions

• Nicholas Cage and Pool Drownings  © Tyler Viegen is licensed under a  CC BY (Attribution)  license
• Bushman, B. J., & Huesmann, L. R. (2001). Effects of televised violence on aggression. In D. Singer & J. Singer (Eds.), Handbook of children and the media (pp. 223–254). Thousand Oaks, CA: Sage. ↵
• Messerli, F. H. (2012). Chocolate consumption, cognitive function, and Nobel laureates. New England Journal of Medicine, 367 , 1562-1564. ↵

A graph that presents correlations between two quantitative variables, one on the x-axis and one on the y-axis. Scores are plotted at the intersection of the values on each axis.

A relationship in which higher scores on one variable tend to be associated with higher scores on the other.

A relationship in which higher scores on one variable tend to be associated with lower scores on the other.

A statistic that measures the strength of a correlation between quantitative variables.

When one or both variables have a limited range in the sample relative to the population, making the value of the correlation coefficient misleading.

The problem where two variables, X  and  Y , are statistically related either because X  causes  Y, or because  Y  causes  X , and thus the causal direction of the effect cannot be known.

Two variables, X and Y, can be statistically related not because X causes Y, or because Y causes X, but because some third variable, Z, causes both X and Y.

Correlations that are a result not of the two variables being measured, but rather because of a third, unmeasured, variable that affects both of the measured variables.

Correlational Research Copyright © by Rajiv S. Jhangiani; I-Chant A. Chiang; Carrie Cuttler; and Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Research Topics & Ideas: Psychology

100+ Psychology Topic Ideas To Fast-Track Your Research

If you’re starting out on the dissertation or thesis journey for your psychology degree, the very first challenge you’ll face is finding a solid research topic . In this post, we’ll help get the topic ideation process started by providing a meaty list of research ideas, spanning a range of psychology sub-disciplines. We’ll also look at some examples from actual theses and dissertations to give you an idea of what these look like in the real world.

NB – This is just the start…

The topic ideation and evaluation process has multiple steps (which we’ll explain a little later). Therefore, it’s important to recognise that this post is only the first step in finding a high-quality psychology-centred research topic. To develop a research topic, you’ll need to identify a clear and convincing research gap , and a viable plan of action to fill that gap.

If this all sounds a bit intimidating, be sure to check out our free dissertation mini-course , which covers the process of writing a dissertation or thesis from A-Z. You can also sign up for our free webinar that explores how to find a high-quality research topic. Alternatively, if you’d like hands-on help, have a look at our 1-on-1 coaching service .

Overview: Psychology-Related Topics

• How to find a research topic (video)
• Behavioural psychology
• Clinical psychology
• Cognitive psychology
• Developmental psychology
• Educational psychology
• Forensic psychology
• Social psychology
• Sports psychology
• Examples of actual dissertation topics
• Free Webinar : Topic Ideation 101
• Where to get extra help

How To Find A Research Topic

In the video below, we explain how to find suitable research ideas (in psychology or any field), and how to then refine those into well-articulated potential topics for your dissertation or thesis. We also discuss a few important evaluation criteria to help you make the right choice for your project.

Below you’ll find a list of research ideas to get your thinking started. Please note that these research topic ideas are intentionally broad and generic, so you will need to refine them a fair deal using the techniques we discussed in the video above.

We’ve grouped the topic ideas based on a few popular areas of psychology to make it a little easier for you to find something in your particular field of interest. That said, there is naturally some overlap between topics, so keep this in mind.

Research Ideas: Behavioural Psychology

• Cultural variation in behaviour and mental health of adolescents during a disaster: a case study
• The impact of parental drug use and risky behaviour on early child development
• The effects of video game violence on aggressive behaviour among teenage boys in school
• The relationship between adverse childhood experiences and adult risk-seeking behaviour
• The impact of physical exercise on anxiety and health-harming behaviour
• The relationship between personality traits and addiction behaviour
• The effects of reinforcement schedules on decision-making and associative learning
• The effects of daily mindfulness practice on stress and anxiety in middle-aged women
• The use of behavioural interventions in the treatment of eating disorders in poorer communities
• Understanding implicit cognitive processes involved in the over-consumption of fast food
• The use of cognitive behavioural therapy for alcohol addiction treatment
• The impact of extensive technology use in children on long-term attention and focus
• The impact of social media on self-destructive behaviour and poor mental health outcomes
• Exploring the role of sleep and sleep deprivation on healthy behaviours

Research Ideas: Clinical Psychology

• The use of mindfulness-based approaches in the treatment of anxiety disorders among college students
• The use of technology in the delivery of psychological services in war-torn countries
• The effectiveness of dialectical behaviour therapy for borderline personality disorder
• The use of virtual reality technology in the treatment of phobias and PTSD among war veterans
• The role of childhood adversity on adult mental health in immigrant populations in the USA
• The role of genetics and epigenetics in the development of bipolar disorder in Pakistani women: an integrative review
• The effectiveness of pharmacotherapy in the treatment of social anxiety among hikikomori in Japan
• The perception of healthcare workers and patients on the use of teletherapy for the delivery of psychological services
• The impact of social support on mental health outcomes among single parents.
• The effectiveness of integrative therapeutic approaches in the treatment of schizophrenia
• The effectiveness of trauma-focused therapies on post-traumatic growth in domestic abuse survivors
• The role and use of cognitive-behavioural therapy for depression among first-generation students
• The effectiveness of family therapy in addressing childhood trauma and depression
• The impact of cultural mistrust on the diagnosis and treatment of mental health issues in culturally-diverse populations
• The effectiveness of group therapy on post-traumatic stress symptoms among survivors of sexual assault

Research Ideas: Cognitive Psychology

• The impact of lifelong aerobic exercise on cognitive function in old age
• The effects of evening screen use on cognitive development in preschool children
• The impact of sleep deprivation on decision-making among graduate students
• The use of neuroimaging to understand the neural basis of memory retrieval
• The effect of conservative religious homes on social functioning in LGBT+ adolescents
• The role of positive emotions in working memory among high school learners
• The neural basis of decision-making and problem-solving during undergraduate statistic assessments
• The neural basis of language processing among adults learning English as a second language
• The role of technological tools in improving working memory in older adults
• The role of attention in emotional face processing among adult males
• The impact of depression on cognitive function during ageing The impact of daily meditation and mindfulness practice on cognitive function
• The relationship between increased protein intake and improved cognitive function
• The effects of stress on cognitive function among final-year learners

Research Ideas: Developmental Psychology

• The impact of maternal affection on cognitive, social, and emotional development
• The effects of parenting styles on children’s executive function
• The impact of late-night screen use on child development
• The role of digital play on child development outcomes
• Exploring the impact of poverty on early child development in Brazil
• The trauma-informed care as moderating the impact of trauma on child development
• Evaluating the relationship between peer relationship quality and child social development
• The impact of child-targeted media and advertising on child behavioural development
• The role of parental attachment in child resilience
• The moderating impact of culture on bullying and child social development
• The impact of single-parenting on child development in India
• The impact of early educational interventions on child socio-emotional development
• The impact of digital technology use on adolescent development and mental health
• The impact of socioeconomic status on child executive function
• The role of genetics and epigenetics on child neurodevelopmental outcomes linked to depression

Need a helping hand?

Research Ideas: Educational Psychology

• The relationship between self-regulated learning and academic performance in asynchronous versus synchronous learning environments
• Exploring effective parental involvement strategies and their impact on student achievement
• The role of intrinsic motivation in formative assessment in the classroom
• The impact of classroom management and practice on student learning and behaviour
• University students’ preference regarding online learning environments
• The effects of gentrification on student achievement in traditionally poor neighbourhoods
• The impact of teacher expectations and academic self-concept on K12 student mathematics performance
• The use and effectiveness of game-based learning in a high school biology classroom
• The impact of prejudice on the relationship between student motivation and academic performance among Black university students
• The impact of culture on second language English student learning preferences
• The effects of student self-efficacy and engagement on academic performance in secondary school mathematics
• The role of metacognition in learning musicality in hip hop
• The role of small group instruction on teacher efficacy and stress in early childhood education
• The perception and use of multimedia among high school biology teachers in France
• The use of augmented reality applications and its impact on student learning, motivation and attitude

Research Ideas: Forensic Psychology

• The impact of trauma on the psychological functioning of police officers and first responders
• Understanding cultural considerations during forensic psychological assessment and treatment of trauma
• Ethical considerations of the use of AI in forensic psychology in the legal system
• The psychological factors related to recidivism among white collar female offenders in the USA
• The psychological factors related to false confessions among juveniles
• Understanding the use of psychological assessment in the evaluation of eyewitness testimony in criminal courts in England
• The impact of trauma on the reflective functioning of adult female sexual assault victims
• The use and effectiveness of psychological interventions in reducing recidivism among non-violent criminals
• The impact of domestic violence on the mental health and forensic evaluation of men
• Exploring the ethical considerations and use of behavioural analysis in the study of criminal behaviour
• The use and limitations of neuropsychological assessment in forensic evaluations
• The use of social media forensics in evaluating criminal behaviour in violent crimes
• The racialised use of psychological assessment in the evaluation of competency to stand trial in Canada
• Exploring the use and availability of virtual reality technologies in forensic psychology in Spain
• The impact of motivational interviewing-based interventions among criminalized drug users

Research Ideas: Social Psychology

• The impact of prejudice and discrimination on social behaviour among African immigrants in South Africa
• The impact of social networks on behaviour and well-being among young adult females
• The effects of social identity on non-conformity in University students
• The effects of group dynamics on risk-seeking behaviour in adult men
• The impact of social media on the quality of interpersonal relationships among high school learners
• The impact of parental emotional intelligence on pro-social behaviour in children and adolescents
• The effects of conformity and deviance on social attitudes and behaviour during a global recession
• The effects of Tik Tok on social comparison and self-esteem among teenage girls
• Understanding gendered differences in social influence and algorithms on impulsive decision-making
• The effects of social support on mental health among healthcare workers in the UK
• The effects of gender roles on social behaviour among trans teens
• The impact of perceived power and social status on the behaviour of social media influencers
• The impact of social norms on prosocial behaviour among women
• The effects of community participation on aggression and violence in middle-aged men
• The impact of culture and gender on social behaviour during the COVID-19 pandemic

Research Ideas: Sports Psychology

• The moderating role of cultural factors on the relationship between mental health and sports performance in team sports
• The role of mindfulness practice in addressing stress and anxiety in young national athletes
• The relationship between team cohesion and performance in cricket teams
• The effect of transformational leadership on female sports teams in Canada
• The effect of positive self-talk on athletic performance and motivation among Olympic athletes
• The use and perception of hypnosis in New Zealand team sports Understanding stress and burnout in University team athletes
• The efficacy of personalised nutrition and diet on athletic performance among sprinters
• Exploring mental preparation techniques and their effect on athletic motivation and resilience among team-sport athletes
• Exploring the perception and understanding of goal-setting characteristics on athletic performance among team coaches
• The effects of motivational feedback on the performance of female gymnasts
• The perception and use of visualization and imagery among coaches as a means to enhance sport performance
• The impact of sports injuries on mental health and recovery in high school athletes
• The moderating role of sleep on mental toughness and sports performance in Olympic athletes
• The use and perception of technology in sports training and performance in little league softball

Psychology-Related Dissertations & Theses

While the ideas we’ve presented above are a decent starting point for finding a research topic in psychology, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses to see how this all comes together in practice.

Below, we’ve included a selection of research projects from various psychology degree programs to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

• Effects of a Patient Question Prompt List on Outpatient Palliative Care Appointments (McDarby, 2022)
• The role of affect and exercise goals in physical activity engagement in younger and older adults (Stojanovic, 2022)
• Lay Theories about Whether Emotion Helps or Hinders Reasoning and Well-being (Karnaze, 2022)
• The effects of blast-induced traumatic brain injury on two transgenic models of Alzheimer’s Disease (Gann, 2020)
• Understanding the parental mind: Examining the stability of parental reflective functioning across the birth of a child and associations with maternal mind-mindedness (Pitzen, 2021)
• An investigation of ineffective ally behaviours (Collier, 2019)
• Response Inhibition-Related Beta Power: Distinguishing Cognitively Intact Elders by Risk for Alzheimer’s Disease (Evans, 2021)
• Recognition Memory of Extremely High-Frequency Words (Miller, 2019)
• The Relationship between Dementia Caregiver Burden and Caregiver Communications in a Memory Clinic Setting (Martin, 2021)
• Examination of Maternal Versus Paternal Ratings of Child Pre-Injury Functioning in Predicting Child Post-Traumatic Stress Symptoms (Sayer, 2021)
• Electromyography As A Means of Predicting The Rubber Hand Illusion (Teaford, 2021)
• Linking Diversity Climate and Feedback Seeking Through Interpersonal Processes and Race Effects (Flores, 2021)

Looking at these titles, you can probably pick up that the research topics here are far more specific and narrowly-focused , compared to the generic ones presented earlier. This is an important thing to keep in mind as you develop your own research topic. That is to say, to create a top-notch research topic, you must be precise and target a specific context with specific variables of interest . In other words, you need to identify a clear, well-justified research gap.

Fast-Track Your Topic Ideation

Still unsure about how to find the right topic for your research project? Check out our private coaching services , where we work with psychology students on a 1:1 basis to help them find the perfect topic.

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10 Comments

Great insight

A very interesting site that offers a variety of options regarding research topics.

You’re most welcome

A good platform to get information

Amazing and interesting options 👌

Very useful but had not any field of research in health psychology

I feel honored going through this lovely stuff put together. Thank you so much

I need counseling psychology research topics

very empowering and insightful presentations. Can I be assisted in crafting a school psychology-related research topic about African context

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Non-Experimental Research

29 Correlational Research

Learning objectives.

• Define correlational research and give several examples.
• Explain why a researcher might choose to conduct correlational research rather than experimental research or another type of non-experimental research.
• Interpret the strength and direction of different correlation coefficients.
• Explain why correlation does not imply causation.

What Is Correlational Research?

Correlational research is a type of non-experimental research in which the researcher measures two variables (binary or continuous) and assesses the statistical relationship (i.e., the correlation) between them with little or no effort to control extraneous variables. There are many reasons that researchers interested in statistical relationships between variables would choose to conduct a correlational study rather than an experiment. The first is that they do not believe that the statistical relationship is a causal one or are not interested in causal relationships. Recall two goals of science are to describe and to predict and the correlational research strategy allows researchers to achieve both of these goals. Specifically, this strategy can be used to describe the strength and direction of the relationship between two variables and if there is a relationship between the variables then the researchers can use scores on one variable to predict scores on the other (using a statistical technique called regression, which is discussed further in the section on Complex Correlation in this chapter).

Another reason that researchers would choose to use a correlational study rather than an experiment is that the statistical relationship of interest is thought to be causal, but the researcher  cannot manipulate the independent variable because it is impossible, impractical, or unethical. For example, while a researcher might be interested in the relationship between the frequency people use cannabis and their memory abilities they cannot ethically manipulate the frequency that people use cannabis. As such, they must rely on the correlational research strategy; they must simply measure the frequency that people use cannabis and measure their memory abilities using a standardized test of memory and then determine whether the frequency people use cannabis is statistically related to memory test performance. 

Correlation is also used to establish the reliability and validity of measurements. For example, a researcher might evaluate the validity of a brief extraversion test by administering it to a large group of participants along with a longer extraversion test that has already been shown to be valid. This researcher might then check to see whether participants’ scores on the brief test are strongly correlated with their scores on the longer one. Neither test score is thought to cause the other, so there is no independent variable to manipulate. In fact, the terms  independent variable  and dependent variabl e  do not apply to this kind of research.

Another strength of correlational research is that it is often higher in external validity than experimental research. Recall there is typically a trade-off between internal validity and external validity. As greater controls are added to experiments, internal validity is increased but often at the expense of external validity as artificial conditions are introduced that do not exist in reality. In contrast, correlational studies typically have low internal validity because nothing is manipulated or controlled but they often have high external validity. Since nothing is manipulated or controlled by the experimenter the results are more likely to reflect relationships that exist in the real world.

Finally, extending upon this trade-off between internal and external validity, correlational research can help to provide converging evidence for a theory. If a theory is supported by a true experiment that is high in internal validity as well as by a correlational study that is high in external validity then the researchers can have more confidence in the validity of their theory. As a concrete example, correlational studies establishing that there is a relationship between watching violent television and aggressive behavior have been complemented by experimental studies confirming that the relationship is a causal one (Bushman & Huesmann, 2001) [1] .

Does Correlational Research Always Involve Quantitative Variables?

A common misconception among beginning researchers is that correlational research must involve two quantitative variables, such as scores on two extraversion tests or the number of daily hassles and number of symptoms people have experienced. However, the defining feature of correlational research is that the two variables are measured—neither one is manipulated—and this is true regardless of whether the variables are quantitative or categorical. Imagine, for example, that a researcher administers the Rosenberg Self-Esteem Scale to 50 American college students and 50 Japanese college students. Although this “feels” like a between-subjects experiment, it is a correlational study because the researcher did not manipulate the students’ nationalities. The same is true of the study by Cacioppo and Petty comparing college faculty and factory workers in terms of their need for cognition. It is a correlational study because the researchers did not manipulate the participants’ occupations.

Figure 6.2 shows data from a hypothetical study on the relationship between whether people make a daily list of things to do (a “to-do list”) and stress. Notice that it is unclear whether this is an experiment or a correlational study because it is unclear whether the independent variable was manipulated. If the researcher randomly assigned some participants to make daily to-do lists and others not to, then it is an experiment. If the researcher simply asked participants whether they made daily to-do lists, then it is a correlational study. The distinction is important because if the study was an experiment, then it could be concluded that making the daily to-do lists reduced participants’ stress. But if it was a correlational study, it could only be concluded that these variables are statistically related. Perhaps being stressed has a negative effect on people’s ability to plan ahead (the directionality problem). Or perhaps people who are more conscientious are more likely to make to-do lists and less likely to be stressed (the third-variable problem). The crucial point is that what defines a study as experimental or correlational is not the variables being studied, nor whether the variables are quantitative or categorical, nor the type of graph or statistics used to analyze the data. What defines a study is how the study is conducted.

Data Collection in Correlational Research

Again, the defining feature of correlational research is that neither variable is manipulated. It does not matter how or where the variables are measured. A researcher could have participants come to a laboratory to complete a computerized backward digit span task and a computerized risky decision-making task and then assess the relationship between participants’ scores on the two tasks. Or a researcher could go to a shopping mall to ask people about their attitudes toward the environment and their shopping habits and then assess the relationship between these two variables. Both of these studies would be correlational because no independent variable is manipulated. 

Correlations Between Quantitative Variables

Correlations between quantitative variables are often presented using scatterplots . Figure 6.3 shows some hypothetical data on the relationship between the amount of stress people are under and the number of physical symptoms they have. Each point in the scatterplot represents one person’s score on both variables. For example, the circled point in Figure 6.3 represents a person whose stress score was 10 and who had three physical symptoms. Taking all the points into account, one can see that people under more stress tend to have more physical symptoms. This is a good example of a positive relationship , in which higher scores on one variable tend to be associated with higher scores on the other. In other words, they move in the same direction, either both up or both down. A negative relationship is one in which higher scores on one variable tend to be associated with lower scores on the other. In other words, they move in opposite directions. There is a negative relationship between stress and immune system functioning, for example, because higher stress is associated with lower immune system functioning.

The strength of a correlation between quantitative variables is typically measured using a statistic called  Pearson’s Correlation Coefficient (or Pearson's  r ) . As Figure 6.4 shows, Pearson’s r ranges from −1.00 (the strongest possible negative relationship) to +1.00 (the strongest possible positive relationship). A value of 0 means there is no relationship between the two variables. When Pearson’s  r  is 0, the points on a scatterplot form a shapeless “cloud.” As its value moves toward −1.00 or +1.00, the points come closer and closer to falling on a single straight line. Correlation coefficients near ±.10 are considered small, values near ± .30 are considered medium, and values near ±.50 are considered large. Notice that the sign of Pearson’s  r  is unrelated to its strength. Pearson’s  r  values of +.30 and −.30, for example, are equally strong; it is just that one represents a moderate positive relationship and the other a moderate negative relationship. With the exception of reliability coefficients, most correlations that we find in Psychology are small or moderate in size. The website http://rpsychologist.com/d3/correlation/ , created by Kristoffer Magnusson, provides an excellent interactive visualization of correlations that permits you to adjust the strength and direction of a correlation while witnessing the corresponding changes to the scatterplot.

There are two common situations in which the value of Pearson’s  r  can be misleading. Pearson’s  r  is a good measure only for linear relationships, in which the points are best approximated by a straight line. It is not a good measure for nonlinear relationships, in which the points are better approximated by a curved line. Figure 6.5, for example, shows a hypothetical relationship between the amount of sleep people get per night and their level of depression. In this example, the line that best approximates the points is a curve—a kind of upside-down “U”—because people who get about eight hours of sleep tend to be the least depressed. Those who get too little sleep and those who get too much sleep tend to be more depressed. Even though Figure 6.5 shows a fairly strong relationship between depression and sleep, Pearson’s  r  would be close to zero because the points in the scatterplot are not well fit by a single straight line. This means that it is important to make a scatterplot and confirm that a relationship is approximately linear before using Pearson’s  r . Nonlinear relationships are fairly common in psychology, but measuring their strength is beyond the scope of this book.

The other common situations in which the value of Pearson’s  r  can be misleading is when one or both of the variables have a limited range in the sample relative to the population. This problem is referred to as  restriction of range . Assume, for example, that there is a strong negative correlation between people’s age and their enjoyment of hip hop music as shown by the scatterplot in Figure 6.6. Pearson’s  r  here is −.77. However, if we were to collect data only from 18- to 24-year-olds—represented by the shaded area of Figure 6.6—then the relationship would seem to be quite weak. In fact, Pearson’s  r  for this restricted range of ages is 0. It is a good idea, therefore, to design studies to avoid restriction of range. For example, if age is one of your primary variables, then you can plan to collect data from people of a wide range of ages. Because restriction of range is not always anticipated or easily avoidable, however, it is good practice to examine your data for possible restriction of range and to interpret Pearson’s  r  in light of it. (There are also statistical methods to correct Pearson’s  r  for restriction of range, but they are beyond the scope of this book).

Correlation Does Not Imply Causation

You have probably heard repeatedly that “Correlation does not imply causation.” An amusing example of this comes from a 2012 study that showed a positive correlation (Pearson’s r = 0.79) between the per capita chocolate consumption of a nation and the number of Nobel prizes awarded to citizens of that nation [2] . It seems clear, however, that this does not mean that eating chocolate causes people to win Nobel prizes, and it would not make sense to try to increase the number of Nobel prizes won by recommending that parents feed their children more chocolate.

There are two reasons that correlation does not imply causation. The first is called the  directionality problem . Two variables,  X  and  Y , can be statistically related because X  causes  Y  or because  Y  causes  X . Consider, for example, a study showing that whether or not people exercise is statistically related to how happy they are—such that people who exercise are happier on average than people who do not. This statistical relationship is consistent with the idea that exercising causes happiness, but it is also consistent with the idea that happiness causes exercise. Perhaps being happy gives people more energy or leads them to seek opportunities to socialize with others by going to the gym. The second reason that correlation does not imply causation is called the  third-variable problem . Two variables,  X  and  Y , can be statistically related not because  X  causes  Y , or because  Y  causes  X , but because some third variable,  Z , causes both  X  and  Y . For example, the fact that nations that have won more Nobel prizes tend to have higher chocolate consumption probably reflects geography in that European countries tend to have higher rates of per capita chocolate consumption and invest more in education and technology (once again, per capita) than many other countries in the world. Similarly, the statistical relationship between exercise and happiness could mean that some third variable, such as physical health, causes both of the others. Being physically healthy could cause people to exercise and cause them to be happier. Correlations that are a result of a third-variable are often referred to as  spurious correlations .

Some excellent and amusing examples of spurious correlations can be found at http://www.tylervigen.com  (Figure 6.7  provides one such example).

“Lots of Candy Could Lead to Violence”

Although researchers in psychology know that correlation does not imply causation, many journalists do not. One website about correlation and causation, http://jonathan.mueller.faculty.noctrl.edu/100/correlation_or_causation.htm , links to dozens of media reports about real biomedical and psychological research. Many of the headlines suggest that a causal relationship has been demonstrated when a careful reading of the articles shows that it has not because of the directionality and third-variable problems.

One such article is about a study showing that children who ate candy every day were more likely than other children to be arrested for a violent offense later in life. But could candy really “lead to” violence, as the headline suggests? What alternative explanations can you think of for this statistical relationship? How could the headline be rewritten so that it is not misleading?

As you have learned by reading this book, there are various ways that researchers address the directionality and third-variable problems. The most effective is to conduct an experiment. For example, instead of simply measuring how much people exercise, a researcher could bring people into a laboratory and randomly assign half of them to run on a treadmill for 15 minutes and the rest to sit on a couch for 15 minutes. Although this seems like a minor change to the research design, it is extremely important. Now if the exercisers end up in more positive moods than those who did not exercise, it cannot be because their moods affected how much they exercised (because it was the researcher who used random assignment to determine how much they exercised). Likewise, it cannot be because some third variable (e.g., physical health) affected both how much they exercised and what mood they were in. Thus experiments eliminate the directionality and third-variable problems and allow researchers to draw firm conclusions about causal relationships.

Media Attributions

• Nicholas Cage and Pool Drownings  © Tyler Viegen is licensed under a  CC BY (Attribution)  license
• Bushman, B. J., & Huesmann, L. R. (2001). Effects of televised violence on aggression. In D. Singer & J. Singer (Eds.), Handbook of children and the media (pp. 223–254). Thousand Oaks, CA: Sage. ↵
• Messerli, F. H. (2012). Chocolate consumption, cognitive function, and Nobel laureates. New England Journal of Medicine, 367 , 1562-1564. ↵

A graph that presents correlations between two quantitative variables, one on the x-axis and one on the y-axis. Scores are plotted at the intersection of the values on each axis.

A relationship in which higher scores on one variable tend to be associated with higher scores on the other.

A relationship in which higher scores on one variable tend to be associated with lower scores on the other.

A statistic that measures the strength of a correlation between quantitative variables.

When one or both variables have a limited range in the sample relative to the population, making the value of the correlation coefficient misleading.

The problem where two variables, X  and  Y , are statistically related either because X  causes  Y, or because  Y  causes  X , and thus the causal direction of the effect cannot be known.

Two variables, X and Y, can be statistically related not because X causes Y, or because Y causes X, but because some third variable, Z, causes both X and Y.

Correlations that are a result not of the two variables being measured, but rather because of a third, unmeasured, variable that affects both of the measured variables.

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Experimental Psychology Research Paper Topics

This page provides a comprehensive list of experimental psychology research paper topics , tailored specifically for students aiming to explore and understand the intricacies of human psychological processes through empirical research. Experimental psychology serves as a cornerstone of psychological science, employing rigorous scientific methods to investigate and interpret the vast complexities of human behavior and mental functions. Through carefully designed experiments, researchers can isolate variables and establish causal relationships, paving the way for advancements in our understanding of perception, cognition, emotion, and other psychological phenomena. By delving into these topics, students will gain valuable insights into the experimental designs, methodologies, and ethical considerations that define this vibrant field. This resource is designed to inspire and facilitate impactful research endeavors, equipping students with the knowledge to contribute significantly to the expansion and refinement of psychological science.

100 Experimental Psychology Research Paper Topics

Experimental psychology stands as a pivotal branch of psychology that applies scientific methods to investigate and unravel the mechanisms behind human thought and behavior. This field allows researchers to design experiments that precisely manipulate variables to observe their effects on subjects, thereby providing clear, causal links between psychological phenomena. The selection of the right experimental psychology research paper topics is not merely academic—it is foundational to advancing our understanding of human psychology. By choosing insightful and challenging topics, students can push the boundaries of what is known and contribute valuable new insights to the scientific community.

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• The effects of color on mood and perception.
• Sensory deprivation and its impact on cognitive functions.
• The role of attention in perceptual processing.
• Multisensory integration and its effects on human perception.
• Perceptual illusions and what they reveal about the human brain.
• The influence of aging on sensory acuity.
• Cross-cultural differences in sensory perceptions.
• The impact of technology on visual and auditory perception.
• Neuropsychological insights into taste and smell.
• The perception of pain: mechanisms and modifiers.
• The impact of sleep on memory consolidation.
• Neuroplasticity and memory: how experiences rewire the brain.
• The effects of stress on memory retrieval.
• Comparative analysis of short-term and long-term memory.
• The role of repetition and spacing in learning effectiveness.
• Memory enhancement techniques: cognitive and pharmacological approaches.
• The reliability of eyewitness memory in different environments.
• Age-related differences in learning capacity and memory retention.
• The use of virtual reality in memory recall experiments.
• False memories: their creation and implications.
• Cognitive biases that influence decision making.
• The role of emotion in rational decision-making processes.
• The impact of cognitive overload on decision quality.
• Differences in decision making between genders.
• The effect of social influence on decision-making accuracy.
• Decision fatigue: causes and consequences.
• The use of heuristics in complex decision-making.
• Neurological underpinnings of spontaneous versus planned decisions.
• The role of intuition in cognitive processing.
• The impact of aging on decision-making abilities.
• The physiological basis of emotional responses.
• Emotional regulation and its effects on mental health.
• The impact of culture on emotional expression and recognition.
• The role of emotions in moral judgment.
• Emotional contagion in groups and crowds.
• The effects of music and art on emotional states.
• Gender differences in emotional processing.
• The relationship between emotional responses and psychopathologies.
• The development of emotional intelligence over the lifespan.
• Measuring emotions: methodologies and technologies.
• The influence of group dynamics on individual behavior.
• Conformity and obedience: experiments and explanations.
• The effects of social exclusion on psychological health.
• The role of social media in shaping public opinions.
• Stereotypes and prejudice: their formation and impacts.
• Altruism and prosocial behavior in controlled experiments.
• The psychology of persuasion and its mechanisms.
• Social loafing vs. social facilitation in work and sports.
• The impact of first impressions on subsequent interactions.
• Leadership styles and their psychological effects on group performance.
• The stages of cognitive development in children.
• The impact of parental styles on child behavior.
• Adolescence: risk factors and psychological resilience.
• Developmental disorders: early detection and intervention strategies.
• The role of play in social and cognitive development.
• Aging and cognitive decline: preventive strategies.
• Lifespan psychology: changes in aspirations and motivations.
• The effects of early educational interventions on developmental outcomes.
• The influence of genetics vs. environment in developmental trajectories.
• Social development and peer influences during childhood and adolescence.
• Brain injuries and their impact on personality and behavior.
• Neurological bases of addiction and substance abuse.
• The effects of neurological diseases on family dynamics.
• Cognitive rehabilitation techniques for stroke survivors.
• The relationship between brain structure and cognitive functions.
• Neuroethics: the implications of brain research.
• The use of neuroimaging to study thought processes.
• The impact of diet and physical health on neurological health.
• Sleep disorders and their psychological effects.
• The role of mirror neurons in empathy and learning.
• Conditioning and learning: classical and operant approaches.
• The effects of reinforcement schedules on behavior modification.
• Behavioral theories in marketing and consumer behavior.
• Animal models in behavioral research: ethics and insights.
• The use of behavior therapy techniques for psychological disorders.
• The psychology of habits: formation, maintenance, and change.
• The role of behavioral factors in obesity and other health issues.
• Behavioral genetics: separating nature from nurture.
• The impact of environmental factors on behavior.
• Behavioral adaptations to climate change and environmental stresses.
• Language acquisition in children and adults.
• The cognitive processes involved in reading and writing.
• The relationship between language and thought.
• Language disorders: dyslexia, aphasia, and others.
• The impact of bilingualism on cognitive development.
• Speech perception and processing mechanisms.
• The neuroanatomy of language production and comprehension.
• Social interactions and language use.
• The evolution of language: theories and evidence.
• Artificial intelligence and natural language processing.
• The psychological impact of chronic illness on individuals and families.
• The effectiveness of psychological interventions in physical health care.
• Stress and its effects on physical health.
• The role of psychology in pain management.
• Behavioral risk factors for heart disease and other illnesses.
• The impact of patient-practitioner communication on health outcomes.
• Psychological aspects of reproductive health.
• The role of motivation in health behavior change.
• Health disparities: the impact of socioeconomic status and race.
• Psychoneuroimmunology: the link between mental states and immune response.

The breadth and depth of experimental psychology research paper topics provide a robust platform for students to explore and contribute to various facets of psychological science. These topics not only allow students to apply scientific methodologies to real-world psychological issues but also offer opportunities to innovate and enhance the understanding of human behavior. Students are encouraged to delve deeply into these experimental psychology research paper topics, as doing so will enable them to produce significant scholarly work that has the potential to influence theoretical frameworks and practical applications in psychology.

The Range of Experimental Psychology Research Paper Topics

Research Methods in Experimental Psychology

One of the core components of experimental psychology is its focus on methodological rigor and precision. The common research methodologies used in experimental psychology include controlled experiments, observational studies, and case studies, each serving different but complementary purposes. In controlled experiments, variables are manipulated in a controlled environment to observe causation and effect, making it possible to draw conclusions about how different factors influence psychological outcomes.

The importance of experimental design, controls, and variables cannot be overstated in this context. Good experimental design ensures that the results are attributable solely to the manipulated variables, not to external factors. Controls help isolate the effects of interest by holding constant other potential influences, thereby increasing the validity of the experiment. A discussion of these elements highlights their role in minimizing biases and errors, thus enhancing the reliability and applicability of the research findings.

Analyzing case studies of successful experimental setups further illustrates these points. For instance, classic experiments in social psychology, such as the Stanford prison experiment or Milgram’s obedience study, though controversial, have provided deep insights into human social behavior and conformity. These case studies not only show effective experimental design but also underscore the ethical considerations and psychological impacts associated with experimental psychology.

Innovative Areas in Experimental Research

Experimental psychology continually evolves as new technologies and theoretical approaches emerge. Cutting-edge research areas within this field include neuropsychology, cognitive robotics, and virtual reality applications, each pushing the boundaries of traditional experimental methods. These innovations allow for more precise measurements and the simulation of complex psychological processes in controlled environments.

Emerging technologies like eye-tracking devices, EEG, and fMRI have revolutionized the way experiments are conducted in experimental psychology. These tools offer unprecedented views into the neural underpinnings of cognition and behavior, allowing for more detailed and accurate predictions about how these processes operate under various conditions. Additionally, the integration of experimental psychology with fields like genetics, neuroscience, and information technology facilitates interdisciplinary research that enriches our understanding of cognitive and behavioral sciences.

Ethical Considerations in Experimental Research

Ethical considerations form a significant pillar of research in experimental psychology. Because experimental methods often involve manipulating variables to observe effects on real participants, ethical guidelines are crucial to ensure the safety and well-being of subjects. Discussions on ethical issues in experimental psychology include considerations about informed consent, deception, and the potential psychological harm that could arise from participation in studies.

Exploring the guidelines and regulations that govern experimental research helps safeguard the interests of participants and maintain public trust in psychological research. For example, the APA’s ethical guidelines mandate that experiments involving humans or animals must adhere to strict ethical standards to minimize harm and discomfort. Case studies highlighting ethical dilemmas in past research, such as the ethical controversies surrounding the aforementioned Stanford prison experiment, serve as important learning tools for current and future psychologists to understand and navigate the complex ethical landscape of experimental research.

Reflecting on the breadth of experimental psychology research paper topics offers a window into the discipline’s vast potential to influence myriad aspects of modern life, from education and health to technology and beyond. The insights gained from rigorous experimental research provide a foundation for practical applications that improve psychological interventions, educational programs, and therapeutic practices, enhancing the quality of life across various settings. As experimental psychology continues to evolve, the fusion of innovative research methods, ethical consideration, and interdisciplinary collaboration holds the promise to further advance psychological science and its applications, ensuring its relevance and impact well into the future.

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Chapter 8: Complex Research Designs

Multiple Dependent Variables

Learning Objectives

• Explain why researchers often include multiple dependent variables in their studies.
• Explain what a manipulation check is and when it would be included in an experiment.

Imagine that you have made the effort to find a research topic, review the research literature, formulate a question, design an experiment, obtain research ethics board (REB) approval, recruit research participants, and manipulate an independent variable. It would seem almost wasteful to measure a single dependent variable. Even if you are primarily interested in the relationship between an independent variable and one primary dependent variable, there are usually several more questions that you can answer easily by including  multiple dependent variables .

Measures of Different Constructs

Often a researcher wants to know how an independent variable affects several distinct dependent variables. For example, Schnall and her colleagues were interested in how feeling disgusted affects the harshness of people’s moral judgments, but they were also curious about how disgust affects other variables, such as people’s willingness to eat in a restaurant. As another example, researcher Susan Knasko was interested in how different odours affect people’s behaviour (Knasko, 1992) [1] . She conducted an experiment in which the independent variable was whether participants were tested in a room with no odour or in one scented with lemon, lavender, or dimethyl sulfide (which has a cabbage-like smell). Although she was primarily interested in how the odours affected people’s creativity, she was also curious about how they affected people’s moods and perceived health—and it was a simple enough matter to measure these dependent variables too. Although she found that creativity was unaffected by the ambient odour, she found that people’s moods were lower in the dimethyl sulfide condition, and that their perceived health was greater in the lemon condition.

When an experiment includes multiple dependent variables, there is again a possibility of carryover effects. For example, it is possible that measuring participants’ moods before measuring their perceived health could affect their perceived health or that measuring their perceived health before their moods could affect their moods. So the order in which multiple dependent variables are measured becomes an issue. One approach is to measure them in the same order for all participants—usually with the most important one first so that it cannot be affected by measuring the others. Another approach is to counterbalance, or systematically vary, the order in which the dependent variables are measured.

Manipulation Checks

When the independent variable is a construct that can only be manipulated indirectly—such as emotions and other internal states—an additional measure of that independent variable is often included as a  manipulation check . This is done to confirm that the independent variable was, in fact, successfully manipulated. For example, Schnall and her colleagues had their participants rate their level of disgust to be sure that those in the messy room actually felt more disgusted than those in the clean room. Manipulation checks are usually done at the end of the procedure to be sure that the effect of the manipulation lasted throughout the entire procedure and to avoid calling unnecessary attention to the manipulation.

Manipulation checks become especially important when the manipulation of the independent variable turns out to have no effect on the dependent variable. Imagine, for example, that you exposed participants to happy or sad movie music—intending to put them in happy or sad moods—but you found that this had no effect on the number of happy or sad childhood events they recalled. This could be because being in a happy or sad mood has no effect on memories for childhood events. But it could also be that the music was ineffective at putting participants in happy or sad moods. A manipulation check—in this case, a measure of participants’ moods—would help resolve this uncertainty. If it showed that you had successfully manipulated participants’ moods, then it would appear that there is indeed no effect of mood on memory for childhood events. But if it showed that you did not successfully manipulate participants’ moods, then it would appear that you need a more effective manipulation to answer your research question.

Measures of the Same Construct

Another common approach to including multiple dependent variables is to operationally define and measure the same construct, or closely related ones, in different ways. Imagine, for example, that a researcher conducts an experiment on the effect of daily exercise on stress. The dependent variable, stress, is a construct that can be operationally defined in different ways. For this reason, the researcher might have participants complete the paper-and-pencil Perceived Stress Scale  and  measure their levels of the stress hormone cortisol. This is an example of the use of converging operations. If the researcher finds that the different measures are affected by exercise in the same way, then he or she can be confident in the conclusion that exercise affects the more general construct of stress.

When multiple dependent variables are different measures of the same construct—especially if they are measured on the same scale—researchers have the option of combining them into a single measure of that construct. Recall that Schnall and her colleagues were interested in the harshness of people’s moral judgments. To measure this construct, they presented their participants with seven different scenarios describing morally questionable behaviours and asked them to rate the moral acceptability of each one. Although they could have treated each of the seven ratings as a separate dependent variable, these researchers combined them into a single dependent variable by computing their mean.

When researchers combine dependent variables in this way, they are treating them collectively as a multiple-response measure of a single construct. The advantage of this is that multiple-response measures are generally more reliable than single-response measures. However, it is important to make sure the individual dependent variables are correlated with each other by computing an internal consistency measure such as Cronbach’s α. If they are not correlated with each other, then it does not make sense to combine them into a measure of a single construct. If they have poor internal consistency, then they should be treated as separate dependent variables.

Key Takeaways

• Researchers in psychology often include multiple dependent variables in their studies. The primary reason is that this easily allows them to answer more research questions with minimal additional effort.
• When an independent variable is a construct that is manipulated indirectly, it is a good idea to include a manipulation check. This is a measure of the independent variable typically given at the end of the procedure to confirm that it was successfully manipulated.
• Multiple measures of the same construct can be analyzed separately or combined to produce a single multiple-item measure of that construct. The latter approach requires that the measures taken together have good internal consistency.
• Practice: List three independent variables for which it would be good to include a manipulation check. List three others for which a manipulation check would be unnecessary. Hint: Consider whether there is any ambiguity concerning whether the manipulation will have its intended effect.
• Practice: Imagine a study in which the independent variable is whether the room where participants are tested is warm (30°) or cool (12°). List three dependent variables that you might treat as measures of separate variables. List three more that you might combine and treat as measures of the same underlying construct.
• Knasko, S. C. (1992). Ambient odour’s effect on creativity, mood, and perceived health. Chemical Senses, 17 , 27–35. ↵

When researchers examine the relationship between a single independent variable and more than one dependent variable.

A separate measure of the construct the researcher is trying to manipulate.

Research Methods in Psychology - 2nd Canadian Edition Copyright © 2015 by Paul C. Price, Rajiv Jhangiani, & I-Chant A. Chiang is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Science Education (Experimental Psychology)

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Applications and Summary

The simple experiment: two-group design.

Source: Laboratories of Gary Lewandowski , Dave Strohmetz, and Natalie Ciarocco—Monmouth University

A two-group design is the simplest way to establish a cause-effect relationship between two variables. This video demonstrates a simple experiment (two-group design).  In providing an overview of how a researcher conducts a simple experiment (two-group design), this video shows viewers the process of turning ideas into testable ideas and forming hypothesis, the identification and effect of experiment variables, the formation of experimental conditions and controls, the process of conducting the study, the collection of results, and the consideration their implications. This research technique is demonstration in the context of answering the research question: “How does physiological arousal/excitement influence perceived attraction?”

1. Introduction of topic/research question

• Research question: All research seeks to answer questions. Often those questions start out fairly broad ( e.g. , What leads to attraction?). The researcher then forms a hypothesis based on educated guesses about potential answers.
• Research hypothesis: Those who are experiencing high excitement will see others as more attractive than those who are experiencing low excitement.

2. Key variables

• Variable = anything that changes in a study
• Based on the hypothesis, excitement is the independent variable.
• Based on the hypothesis, perceived attractiveness is the dependent variable.

3. Defining the variables

• To manipulate the independent variable of excitement, have participants run on a treadmill.
• To measure the dependent variable of perceived attractiveness, show participants pictures.

4. Establishing conditions

• Ethical consideration: In using a manipulation that requires physical effort such as this, the researcher must be mindful of the pertinent ethical considerations ( i.e. , people should be in shape and cannot have them run too hard to too long)
• Control condition = The condition that does not have the key ingredient. This group serves as the baseline for comparison.  

5. Experimental control

• What it is: Keeping everything exactly identical across conditions except for the key piece that the researcher wants to manipulate/change
• It’s importance: This is the only way a researcher can isolate which piece or factor is responsible for the changes in the dependent variable.
• Application to study: In the present study the researcher wants to focus on how excitement/arousal influences attraction. As such, excitement/arousal should be the only piece that changes between conditions. Thus, if the experimental group (high arousal) runs on a treadmill at 6mph for 3 minutes in a lab, the control group should be as similar as possible. They should be on a treadmill in lab for 3 minutes, but should walk at 3mph.

6. Measuring the dependent variable (attraction)

• Key measurement considerations: shouldn’t be too attractive or unattractive, shouldn’t have piercings/tattoos; and should just be head shot
• 7-point Likert Scale: 1 = extremely unattractive; 7 = extremely attractive

7. Procedure/conducting the study

• Tell participants: “Here is the informed consent, which outlines what the study is basically about, any risks/benefits of participation, and lets you know that you are free to quit at any time.” 
• Randomly order the packets so that the participant’s condition (running or walking) is not based on anything other than chance. Otherwise, the researcher may subconsciously be more likely to assign certain participants ( e.g. , those who look physically fit) to certain conditions ( e.g. , running). 
• Set treadmill to 6 mph, explain to the participant what they need to do, and start the timer for 3 min.
• Show participants a series of pictures and ask them to rate on provided scale (1 = not at all attractive through 7 = extremely attractive).
• Set treadmill to 3 mph, explain to the participant what they need to do, and start the timer for 3 min.
• Explain the purpose of the study to the participant: “Thank you for participating. In this study I was trying to determine if excitement or arousal from exercise would lead participants to find a picture more attractive. To manipulate excitement/arousal there were two conditions; running vs. walking on the treadmill. Do you have any questions?”

Experimental design is the process by which a researcher plans a study. A two-group design is the simplest way to establish a cause-effect relationship between two variables.

Here, a two-group experimental design is used to answer the research question: “How does physiological arousal in the form of exercise influence perceived attraction? In other words, are people more attractive to you after a workout?”

This video demonstrates the process of turning concepts into testable ideas and forming hypotheses, how to design experimental conditions and controls as well as how to identify experimental variables, how to execute the study, and finally, analysis of the data and consideration of their implications.

All research seeks to answer questions. Often those questions start out fairly broad. The researcher then forms a hypothesis based on educated guesses about potential answers.

Here, the researcher forms the research hypothesis that those who are experiencing high excitement through exercise will see others as more attractive than those who are experiencing low excitement.

To test this hypothesis, the researcher organizes two groups of people: an experimental group and a control group. The experimental group is the one that receives the treatment, which in the case of today’s experiment is running on a treadmill. The treatment is the key ingredient that the researcher believes will influence the outcome.

The control group does not have the key ingredient. This group serves as the baseline for comparison. In the control group, everything must be kept exactly identical to the experimental group except for that key ingredient that the researcher wants to manipulate.

In the present study, the researcher wants to focus on how physical excitement influences attraction. As such, physical excitement should be the only piece that changes between experimental and control groups. Therefore, the control group will walk on the same treadmill for the same amount of time that the experimental group will run on the treadmill, in order to remove the excited state from the condition.

Now, consider the variables, which are things that change within the experiment. In a cause and effect scenario, the cause, or the condition manipulated to detect changes, is called the independent variable. The effect, or the outcome that the researcher measures, is called the dependent variable.

Based on the hypothesis, excitement is the independent variable and perceived attractiveness is the dependent variable.

As we’ve mentioned, in order to manipulate the independent variable of physical arousal, the experimental group will run on a treadmill.

Including a control group is the only way the researcher can determine if changing the independent variable is responsible for the observed changes in the dependent variable.

To measure the dependent variable of perceived attractiveness, participants in both groups will view pictures. It is important to consider factors that could complicate interpretation of the results. For example, in this case the subject in the picture shouldn’t have piercings or tattoos, and should only include the head.

Here, perceived attraction is quantified through use of the 7-point Likert Scale, where 1 is designated as “Extremely Unattractive” and 7 as “Extremely Attractive.” Now that the experimental design has been established, we can proceed to conducting the experiment.

To begin the experiment, the researcher needs to obtain the subject’s informed consent to participate in the study. The informed consent gives a synopsis of the study—any risks and benefits of participation—and lets the participant know that they are free to quit at any time.

Next, make random assignments to the groups, so that the participant’s group isn’t based on anything other than chance, and any subconscious assumptions on the part of the researcher are avoided.

To perform the experimental condition, bring the participant to the treadmill and explain to the participant what she needs to do. Then, allow the participant to set the treadmill to 6 miles per hour. When the participant begins, immediately start the timer for 3 min.

Afterwards, show the participant a series of pictures and ask her to rate on the provided scale.

For the control study, once again explain to the participant what she needs to do. Allow the participant to set the treadmill to 3 miles per hour, and start the timer for 3 min at the moment the participant begins.

The control subject then rates the attractiveness of the pictures in an identical manner to experimental group.

Following the experiment, give the subject a debriefing where the researcher explains the purpose of the study.

Researcher: Thank you for participating. In this study I was trying to determine if arousal from exercise would lead participants to find a picture of a person more attractive. To manipulate arousal there were two conditions: running vs. walking on the treadmill. Do you have any questions?

After collecting data from 122 people, a t-test was performed for independent means comparing the high arousal condition—achieved through running—to the low arousal condition—achieved through walking—to see how they influenced attraction.

The results reveal that those subjected to the high arousal condition found the pictures more attractive than those subjected to the low arousal condition.

The results of this study are similar to the famous “bridge study” performed by Donald Dutton and Arthur Aron in 1974. In this study, Dutton and Aron found that unaccompanied men who crossed a high shaky bridge were more likely to follow up with a female research assistant than other men who crossed a low sturdy bridge.

Now that you are familiar with setting up a simple experiment using two-group design, you can apply this approach to answer the specific questions of your research.

The two-group experimental design is commonly used in psychological experiments to determine a cause and effect relationship of the intervention in question.

For example, researchers used this type of experiment to determine the effectiveness of combined self-management and relaxation-breathing training for children with moderate-to-severe asthma.

In this study, the independent variable was the type of training provided to the children, and the dependent variables were made up of four physiological variables, including anxiety levels. The results revealed that a combination of self-management and relaxation-breathing training can reduce anxiety in asthmatic children.

In another study, the impact of a feeding log on breastfeeding duration and exclusivity was assessed. The experimental group completed a daily breastfeeding log while the control group did not. The log served to intervene with the participant in the self-regulation process.

The findings suggest that the breastfeeding log may be a valuable tool in self-regulating breastfeeding and promoting a longer duration of full breastfeeding.

You’ve just watched JoVE’s introduction on performing a simple experiment using two-group design. Now, you should have a good understanding of how to form a hypothesis, how to design experimental conditions and controls, as well as how to identify variables. You should also have a comprehension for how to perform a study, and how to assess the results.

And remember, considering the potential effects of arousal on attraction, a first date at the amusement park may be a better choice than a first date at a poetry reading.

Thanks for watching! 

After collecting data from 122 people, a t-test for independent means was performed comparing the high arousal (running) condition to the low arousal (walking) condition to see how they influenced attraction. As shown in Figure 1 , those in the running/high arousal condition, depicted with the red bar found the pictures more attractive than those in the walking/low arousal condition.

The results of this study are similar to the famous “bridge study” where researchers found that men who crossed a high shaky bridge were more attracted to a female, than other men who crossed a low sturdy bridge. 1

Considering the potential effects of arousal on attraction, it may be better to talk to someone you’re interested in while at the gym, instead of the library. It also suggests that a rock concert may be better first date than a poetry reading.

• Dutton, D. G., & Aron, A. P. Some evidence for heightened sexual attraction under conditions of high anxiety. Journal of Personality and Social Psychology. 30 (4), 510-517. doi:10.1037/h0037031 (1974).

Here, a two-group experimental design is used to answer the research question: “How does physiological arousal in the form of exercise influence perceived attraction? In other words, are people more attractive to you after a workout?”

To test this hypothesis, the researcher organizes two groups of people: an experimental group and a control group. The experimental group is the one that receives the treatment, which in the case of today’s experiment is running on a treadmill. The treatment is the key ingredient that the researcher believes will influence the outcome.

As we’ve mentioned, in order to manipulate the independent variable of physical arousal, the experimental group will run on a treadmill.

To measure the dependent variable of perceived attractiveness, participants in both groups will view pictures. It is important to consider factors that could complicate interpretation of the results. For example, in this case the subject in the picture shouldn’t have piercings or tattoos, and should only include the head.

Here, perceived attraction is quantified through use of the 7-point Likert Scale, where 1 is designated as “Extremely Unattractive” and 7 as “Extremely Attractive.” Now that the experimental design has been established, we can proceed to conducting the experiment.

To begin the experiment, the researcher needs to obtain the subject’s informed consent to participate in the study. The informed consent gives a synopsis of the study—any risks and benefits of participation—and lets the participant know that they are free to quit at any time.

Next, make random assignments to the groups, so that the participant’s group isn’t based on anything other than chance, and any subconscious assumptions on the part of the researcher are avoided.

After collecting data from 122 people, a t-test was performed for independent means comparing the high arousal condition—achieved through running—to the low arousal condition—achieved through walking—to see how they influenced attraction.

The results of this study are similar to the famous “bridge study” performed by Donald Dutton and Arthur Aron in 1974. In this study, Dutton and Aron found that unaccompanied men who crossed a high shaky bridge were more likely to follow up with a female research assistant than other men who crossed a low sturdy bridge.

You’ve just watched JoVE’s introduction on performing a simple experiment using two-group design. Now, you should have a good understanding of how to form a hypothesis, how to design experimental conditions and controls, as well as how to identify variables. You should also have a comprehension for how to perform a study, and how to assess the results.

Thanks for watching! 

JoVE Science Education Database. Experimental Psychology. The Simple Experiment: Two-group Design. JoVE, Cambridge, MA, (2024).

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2.2 Finding a Research Topic

Learning objectives.

• Learn some common sources of research ideas.
• Define the research literature in psychology and give examples of sources that are part of the research literature and sources that are not.
• Describe and use several methods for finding previous research on a particular research idea or question.

Good research must begin with a good research question. Yet coming up with good research questions is something that novice researchers often find difficult and stressful. One reason is that this is a creative process that can appear mysterious—even magical—with experienced researchers seeming to pull interesting research questions out of thin air. However, psychological research on creativity has shown that it is neither as mysterious nor as magical as it appears. It is largely the product of ordinary thinking strategies and persistence (Weisberg, 1993) [1] . This section covers some fairly simple strategies for finding general research ideas, turning those ideas into empirically testable research questions, and finally evaluating those questions in terms of how interesting they are and how feasible they would be to answer.

Finding Inspiration

Research questions often begin as more general research ideas—usually focusing on some behavior or psychological characteristic: talkativeness, learning, depression, bungee jumping, and so on. Before looking at how to turn such ideas into empirically testable research questions, it is worth looking at where such ideas come from in the first place. Three of the most common sources of inspiration are informal observations, practical problems, and previous research.

Informal observations include direct observations of our own and others’ behavior as well as secondhand observations from non-scientific sources such as newspapers, books, blogs, and so on. For example, you might notice that you always seem to be in the slowest moving line at the grocery store. Could it be that most people think the same thing? Or you might read in a local newspaper about people donating money and food to a local family whose house has burned down and begin to wonder about who makes such donations and why. Some of the most famous research in psychology has been inspired by informal observations. Stanley Milgram’s famous research on obedience to authority, for example, was inspired in part by journalistic reports of the trials of accused Nazi war criminals—many of whom claimed that they were only obeying orders. This led him to wonder about the extent to which ordinary people will commit immoral acts simply because they are ordered to do so by an authority figure (Milgram, 1963) [2] .

Practical problems can also inspire research ideas, leading directly to applied research in such domains as law, health, education, and sports. Does taking lecture notes by hand improve students’ exam performance? How effective is psychotherapy for depression compared to drug therapy? To what extent do cell phones impair people’s driving ability? How can we teach children to read more efficiently? What is the best mental preparation for running a marathon?

Reading in print? Scan this QR code to view the video on your mobile device. Or go to https://youtu.be/nXNztCLYgxc

Probably the most common inspiration for new research ideas, however, is previous research. Recall that science is a kind of large-scale collaboration in which many different researchers read and evaluate each other’s work and conduct new studies to build on it. Of course, experienced researchers are familiar with previous research in their area of expertise and probably have a long list of ideas. This suggests that novice researchers can find inspiration by consulting with a more experienced researcher (e.g., students can consult a faculty member). But they can also find inspiration by picking up a copy of almost any professional journal and reading the titles and abstracts. In one typical issue of  Psychological Science , for example, you can find articles on the perception of shapes, anti-Semitism, police lineups, the meaning of death, second-language learning, people who seek negative emotional experiences, and many other topics. If you can narrow your interests down to a particular topic (e.g., memory) or domain (e.g., health care), you can also look through more specific journals, such as  Memory & Cognition  or  Health Psychology .

Reviewing the Research Literature

Once again, one of the most common sources of inspiration is previous research. Therefore, it is important to review the literature early in the research process. Reviewing the research literature means finding, reading, and summarizing the published research relevant to your topic of interest. In addition to helping you discover new research questions, reviewing the literature early in the research process can help you in several other ways.

• It can tell you if a research question has already been answered.
• It can help you evaluate the interestingness of a research question.
• It can give you ideas for how to conduct your own study.
• It can tell you how your study fits into the research literature.

The  research literature  in any field is all the published research in that field. The research literature in psychology is enormous—including millions of scholarly articles and books dating to the beginning of the field—and it continues to grow. Although its boundaries are somewhat fuzzy, the research literature definitely does not include self-help and other pop psychology books, dictionary and encyclopedia entries, websites, and similar sources that are intended mainly for the general public. These are considered unreliable because they are not reviewed by other researchers and are often based on little more than common sense or personal experience. Wikipedia contains much valuable information, but the fact that its authors are anonymous and may not have any formal training or expertise in that subject area, and its content continually changes makes it unsuitable as a basis of sound scientific research. For our purposes, it helps to define the research literature as consisting almost entirely of two types of sources: articles in professional journals, and scholarly books in psychology and related fields.

Professional Journals

Professional journals  are periodicals that publish original research articles. There are thousands of professional journals that publish research in psychology and related fields. They are usually published monthly or quarterly in individual issues, each of which contains several articles. The issues are organized into volumes, which usually consist of all the issues for a calendar year. Some journals are published in hard copy only, others in both hard copy and electronic form, and still others in electronic form only.

Most articles in professional journals are one of two basic types: empirical research reports and review articles.  Empirical research reports  describe one or more new empirical studies conducted by the authors. They introduce a research question, explain why it is interesting, review previous research, describe their method and results, and draw their conclusions. Review articles  summarize previously published research on a topic and usually present new ways to organize or explain the results. When a review article is devoted primarily to presenting a new theory, it is often referred to as a theoretical article .

Figure 2.2 Small Sample of the Thousands of Professional Journals That Publish Research in Psychology and Related Fields

Most professional journals in psychology undergo a process of  double-blind peer review . Researchers who want to publish their work in the journal submit a manuscript to the editor—who is generally an established researcher too—who in turn sends it to two or three experts on the topic. Each reviewer reads the manuscript, writes a critical but constructive review, and sends the review back to the editor along with his or her recommendations. The editor then decides whether to accept the article for publication, ask the authors to make changes and resubmit it for further consideration, or reject it outright. In any case, the editor forwards the reviewers’ written comments to the researchers so that they can revise their manuscript accordingly. This entire process is double-blind, as the reviewers do not know the identity of the researcher(s) and vice versa. Double-blind peer review is helpful because it ensures that the work meets basic standards of the field before it can enter the research literature. However, in order to increase transparency and accountability, some newer open access journals (e.g., Frontiers in Psychology) utilize an open peer review process wherein the identities of the reviewers (which remain concealed during the peer review process) are published alongside the journal article.

Scholarly Books

Scholarly books  are books written by researchers and practitioners mainly for use by other researchers and practitioners. A  monograph  is written by a single author or a small group of authors and usually, gives a coherent presentation of a topic much like an extended review article.  Edited volumes have an editor or a small group of editors who recruit many authors to write separate chapters on different aspects of the same topic. Although edited volumes can also give a coherent presentation of the topic, it is not unusual for each chapter to take a different perspective or even for the authors of different chapters to openly disagree with each other. In general, scholarly books undergo a peer review process similar to that used by professional journals.

Literature Search Strategies

Using psycinfo and other databases.

The primary method used to search the research literature involves using one or more electronic databases. These include Academic Search Premier, JSTOR, and ProQuest for all academic disciplines, ERIC for education, and PubMed for medicine and related fields. The most important for our purposes, however, is PsycINFO, which is produced by the American Psychological Association (APA). PsycINFO is so comprehensive—covering thousands of professional journals and scholarly books going back more than 100 years—that for most purposes its content is synonymous with the research literature in psychology. Like most such databases, PsycINFO is usually available through your university library.

PsycINFO consists of individual records for each article, book chapter, or book in the database. Each record includes basic publication information, an abstract or summary of the work (like the one presented at the start of this chapter), and a list of other works cited by that work. A computer interface allows entering one or more search terms and returns any records that contain those search terms. (These interfaces are provided by different vendors and therefore can look somewhat different depending on the library you use.) Each record also contains lists of keywords that describe the content of the work and also a list of index terms. The index terms are especially helpful because they are standardized. Research on differences between women and men, for example, is always indexed under “Human Sex Differences.” Research on note-taking is always indexed under the term “Learning Strategies.” If you do not know the appropriate index terms, PsycINFO includes a thesaurus that can help you find them.

Given that there are nearly four million records in PsycINFO, you may have to try a variety of search terms in different combinations and at different levels of specificity before you find what you are looking for. Imagine, for example, that you are interested in the question of whether women and men differ in terms of their ability to recall experiences from when they were very young. If you were to enter “memory for early experiences” as your search term, PsycINFO would return only six records, most of which are not particularly relevant to your question. However, if you were to enter the search term “memory,” it would return 149,777 records—far too many to look through individually. This is where the thesaurus helps. Entering “memory” into the thesaurus provides several more specific index terms—one of which is “early memories.” While searching for “early memories” among the index terms returns 1,446 records—still too many to look through individually—combining it with “human sex differences” as a second search term returns 37 articles, many of which are highly relevant to the topic.

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Depending on the vendor that provides the interface to PsycINFO, you may be able to save, print, or e-mail the relevant PsycINFO records. The records might even contain links to full-text copies of the works themselves. (PsycARTICLES is a database that provides full-text access to articles in all journals published by the APA.) If not, and you want a copy of the work, you will have to find out if your library carries the journal or has the book and the hard copy on the library shelves. Be sure to ask a librarian if you need help.

Using Other Search Techniques

Reading in print? Scan this QR code to view the video on your mobile device. Or go to https://youtu.be/t1ZwgDeX2eQ

In addition to entering search terms into PsycINFO and other databases, there are several other techniques you can use to search the research literature. First, if you have one good article or book chapter on your topic—a recent review article is best—you can look through the reference list of that article for other relevant articles, books, and book chapters. In fact, you should do this with any relevant article or book chapter you find. You can also start with a classic article or book chapter on your topic, find its record in PsycINFO (by entering the author’s name or article’s title as a search term), and link from there to a list of other works in PsycINFO that cite that classic article. This works because other researchers working on your topic are likely to be aware of the classic article and cite it in their own work. You can also do a general Internet search using search terms related to your topic or the name of a researcher who conducts research on your topic. This might lead you directly to works that are part of the research literature (e.g., articles in open-access journals or posted on researchers’ own websites). The search engine Google Scholar is especially useful for this purpose. A general Internet search might also lead you to websites that are not part of the research literature but might provide references to works that are. Finally, you can talk to people (e.g., your instructor or other faculty members in psychology) who know something about your topic and can suggest relevant articles and book chapters.

What to Search For

When you do a literature review, you need to be selective. Not every article, book chapter, and book that relates to your research idea or question will be worth obtaining, reading, and integrating into your review. Instead, you want to focus on sources that help you do four basic things: (a) refine your research question, (b) identify appropriate research methods, (c) place your research in the context of previous research, and (d) write an effective research report. Several basic principles can help you find the most useful sources.

First, it is best to focus on recent research, keeping in mind that what counts as recent depends on the topic. For newer topics that are actively being studied, “recent” might mean published in the past year or two. For older topics that are receiving less attention right now, “recent” might mean within the past 10 years. You will get a feel for what counts as recent for your topic when you start your literature search. A good general rule, however, is to start with sources published in the past five years. The main exception to this rule would be classic articles that turn up in the reference list of nearly every other source. If other researchers think that this work is important, even though it is old, then, by all means, you should include it in your review.

Second, you should look for review articles on your topic because they will provide a useful overview of it—often discussing important definitions, results, theories, trends, and controversies—giving you a good sense of where your own research fits into the literature. You should also look for empirical research reports addressing your question or similar questions, which can give you ideas about how to operationally define your variables and collect your data. As a general rule, it is good to use methods that others have already used successfully unless you have good reasons not to. Finally, you should look for sources that provide information that can help you argue for the interestingness of your research question. For a study on the effects of cell phone use on driving ability, for example, you might look for information about how widespread cell phone use is, how frequent and costly motor vehicle crashes are, and so on.

How many sources are enough for your literature review? This is a difficult question because it depends on how extensively your topic has been studied and also on your own goals. One study found that across a variety of professional journals in psychology, the average number of sources cited per article was about 50 (Adair & Vohra, 2003) [3] . This gives a rough idea of what professional researchers consider to be adequate. As a student, you might be assigned a much lower minimum number of references to include, but the principles for selecting the most useful ones remain the same.

Key Takeaways

• The research literature in psychology is all the published research in psychology, consisting primarily of articles in professional journals and scholarly books.
• Early in the research process, it is important to conduct a review of the research literature on your topic to refine your research question, identify appropriate research methods, place your question in the context of other research, and prepare to write an effective research report.
• There are several strategies for finding previous research on your topic. Among the best is using PsycINFO, a computer database that catalogs millions of articles, books, and book chapters in psychology and related fields.
• Practice: Use the techniques discussed in this section to find 10 journal articles and book chapters on one of the following research ideas: memory for smells, aggressive driving, the causes of narcissistic personality disorder, the functions of the intraparietal sulcus, or prejudice against the physically handicapped.
• Watch the following video clip produced by UBCiSchool about how to read an academic paper (without losing your mind):

Reading in print? Scan this QR code to view the video on your mobile device. Or go to https://youtu.be/SKxm2HF_-k0

• Weisberg, R. W. (1993). Creativity: Beyond the myth of genius . New York, NY: Freeman. ↵
• Milgram, S. (1963). Behavioral study of obedience. Journal of Abnormal and Social Psychology, 67 , 371–378. ↵
• Adair, J. G., & Vohra, N. (2003). The explosion of knowledge, references, and citations: Psychology’s unique response to a crisis. American Psychologist, 58 , 15–23. ↵

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Research Methods In Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

Research methods in psychology are systematic procedures used to observe, describe, predict, and explain behavior and mental processes. They include experiments, surveys, case studies, and naturalistic observations, ensuring data collection is objective and reliable to understand and explain psychological phenomena.

Hypotheses are statements about the prediction of the results, that can be verified or disproved by some investigation.

There are four types of hypotheses :

• Null Hypotheses (H0 ) – these predict that no difference will be found in the results between the conditions. Typically these are written ‘There will be no difference…’
• Alternative Hypotheses (Ha or H1) – these predict that there will be a significant difference in the results between the two conditions. This is also known as the experimental hypothesis.
• One-tailed (directional) hypotheses – these state the specific direction the researcher expects the results to move in, e.g. higher, lower, more, less. In a correlation study, the predicted direction of the correlation can be either positive or negative.
• Two-tailed (non-directional) hypotheses – these state that a difference will be found between the conditions of the independent variable but does not state the direction of a difference or relationship. Typically these are always written ‘There will be a difference ….’

All research has an alternative hypothesis (either a one-tailed or two-tailed) and a corresponding null hypothesis.

Once the research is conducted and results are found, psychologists must accept one hypothesis and reject the other. 

So, if a difference is found, the Psychologist would accept the alternative hypothesis and reject the null.  The opposite applies if no difference is found.

Sampling techniques

Sampling is the process of selecting a representative group from the population under study.

A sample is the participants you select from a target population (the group you are interested in) to make generalizations about.

Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics.

Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.

• Volunteer sample : where participants pick themselves through newspaper adverts, noticeboards or online.
• Opportunity sampling : also known as convenience sampling , uses people who are available at the time the study is carried out and willing to take part. It is based on convenience.
• Random sampling : when every person in the target population has an equal chance of being selected. An example of random sampling would be picking names out of a hat.
• Systematic sampling : when a system is used to select participants. Picking every Nth person from all possible participants. N = the number of people in the research population / the number of people needed for the sample.
• Stratified sampling : when you identify the subgroups and select participants in proportion to their occurrences.
• Snowball sampling : when researchers find a few participants, and then ask them to find participants themselves and so on.
• Quota sampling : when researchers will be told to ensure the sample fits certain quotas, for example they might be told to find 90 participants, with 30 of them being unemployed.

Experiments always have an independent and dependent variable .

• The independent variable is the one the experimenter manipulates (the thing that changes between the conditions the participants are placed into). It is assumed to have a direct effect on the dependent variable.
• The dependent variable is the thing being measured, or the results of the experiment.

Operationalization of variables means making them measurable/quantifiable. We must use operationalization to ensure that variables are in a form that can be easily tested.

For instance, we can’t really measure ‘happiness’, but we can measure how many times a person smiles within a two-hour period. 

By operationalizing variables, we make it easy for someone else to replicate our research. Remember, this is important because we can check if our findings are reliable.

Extraneous variables are all variables which are not independent variable but could affect the results of the experiment.

It can be a natural characteristic of the participant, such as intelligence levels, gender, or age for example, or it could be a situational feature of the environment such as lighting or noise.

Demand characteristics are a type of extraneous variable that occurs if the participants work out the aims of the research study, they may begin to behave in a certain way.

For example, in Milgram’s research , critics argued that participants worked out that the shocks were not real and they administered them as they thought this was what was required of them. 

Extraneous variables must be controlled so that they do not affect (confound) the results.

Randomly allocating participants to their conditions or using a matched pairs experimental design can help to reduce participant variables. 

Situational variables are controlled by using standardized procedures, ensuring every participant in a given condition is treated in the same way

Experimental Design

Experimental design refers to how participants are allocated to each condition of the independent variable, such as a control or experimental group.

• Independent design ( between-groups design ): each participant is selected for only one group. With the independent design, the most common way of deciding which participants go into which group is by means of randomization. 
• Matched participants design : each participant is selected for only one group, but the participants in the two groups are matched for some relevant factor or factors (e.g. ability; sex; age).
• Repeated measures design ( within groups) : each participant appears in both groups, so that there are exactly the same participants in each group.
• The main problem with the repeated measures design is that there may well be order effects. Their experiences during the experiment may change the participants in various ways.
• They may perform better when they appear in the second group because they have gained useful information about the experiment or about the task. On the other hand, they may perform less well on the second occasion because of tiredness or boredom.
• Counterbalancing is the best way of preventing order effects from disrupting the findings of an experiment, and involves ensuring that each condition is equally likely to be used first and second by the participants.

If we wish to compare two groups with respect to a given independent variable, it is essential to make sure that the two groups do not differ in any other important way. 

Experimental Methods

All experimental methods involve an iv (independent variable) and dv (dependent variable)..

• Field experiments are conducted in the everyday (natural) environment of the participants. The experimenter still manipulates the IV, but in a real-life setting. It may be possible to control extraneous variables, though such control is more difficult than in a lab experiment.
• Natural experiments are when a naturally occurring IV is investigated that isn’t deliberately manipulated, it exists anyway. Participants are not randomly allocated, and the natural event may only occur rarely.

Case studies are in-depth investigations of a person, group, event, or community. It uses information from a range of sources, such as from the person concerned and also from their family and friends.

Many techniques may be used such as interviews, psychological tests, observations and experiments. Case studies are generally longitudinal: in other words, they follow the individual or group over an extended period of time. 

Case studies are widely used in psychology and among the best-known ones carried out were by Sigmund Freud . He conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

Case studies provide rich qualitative data and have high levels of ecological validity. However, it is difficult to generalize from individual cases as each one has unique characteristics.

Correlational Studies

Correlation means association; it is a measure of the extent to which two variables are related. One of the variables can be regarded as the predictor variable with the other one as the outcome variable.

Correlational studies typically involve obtaining two different measures from a group of participants, and then assessing the degree of association between the measures. 

The predictor variable can be seen as occurring before the outcome variable in some sense. It is called the predictor variable, because it forms the basis for predicting the value of the outcome variable.

Relationships between variables can be displayed on a graph or as a numerical score called a correlation coefficient.

• If an increase in one variable tends to be associated with an increase in the other, then this is known as a positive correlation .
• If an increase in one variable tends to be associated with a decrease in the other, then this is known as a negative correlation .
• A zero correlation occurs when there is no relationship between variables.

After looking at the scattergraph, if we want to be sure that a significant relationship does exist between the two variables, a statistical test of correlation can be conducted, such as Spearman’s rho.

The test will give us a score, called a correlation coefficient . This is a value between 0 and 1, and the closer to 1 the score is, the stronger the relationship between the variables. This value can be both positive e.g. 0.63, or negative -0.63.

A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables.

Correlation does not always prove causation, as a third variable may be involved. 

Interview Methods

Interviews are commonly divided into two types: structured and unstructured.

A fixed, predetermined set of questions is put to every participant in the same order and in the same way. 

Responses are recorded on a questionnaire, and the researcher presets the order and wording of questions, and sometimes the range of alternative answers.

The interviewer stays within their role and maintains social distance from the interviewee.

There are no set questions, and the participant can raise whatever topics he/she feels are relevant and ask them in their own way. Questions are posed about participants’ answers to the subject

Unstructured interviews are most useful in qualitative research to analyze attitudes and values.

Though they rarely provide a valid basis for generalization, their main advantage is that they enable the researcher to probe social actors’ subjective point of view. 

Questionnaire Method

Questionnaires can be thought of as a kind of written interview. They can be carried out face to face, by telephone, or post.

The choice of questions is important because of the need to avoid bias or ambiguity in the questions, ‘leading’ the respondent or causing offense.

• Open questions are designed to encourage a full, meaningful answer using the subject’s own knowledge and feelings. They provide insights into feelings, opinions, and understanding. Example: “How do you feel about that situation?”
• Closed questions can be answered with a simple “yes” or “no” or specific information, limiting the depth of response. They are useful for gathering specific facts or confirming details. Example: “Do you feel anxious in crowds?”

Its other practical advantages are that it is cheaper than face-to-face interviews and can be used to contact many respondents scattered over a wide area relatively quickly.

Observations

There are different types of observation methods :

• Covert observation is where the researcher doesn’t tell the participants they are being observed until after the study is complete. There could be ethical problems or deception and consent with this particular observation method.
• Overt observation is where a researcher tells the participants they are being observed and what they are being observed for.
• Controlled : behavior is observed under controlled laboratory conditions (e.g., Bandura’s Bobo doll study).
• Natural : Here, spontaneous behavior is recorded in a natural setting.
• Participant : Here, the observer has direct contact with the group of people they are observing. The researcher becomes a member of the group they are researching.  
• Non-participant (aka “fly on the wall): The researcher does not have direct contact with the people being observed. The observation of participants’ behavior is from a distance

Pilot Study

A pilot  study is a small scale preliminary study conducted in order to evaluate the feasibility of the key s teps in a future, full-scale project.

A pilot study is an initial run-through of the procedures to be used in an investigation; it involves selecting a few people and trying out the study on them. It is possible to save time, and in some cases, money, by identifying any flaws in the procedures designed by the researcher.

A pilot study can help the researcher spot any ambiguities (i.e. unusual things) or confusion in the information given to participants or problems with the task devised.

Sometimes the task is too hard, and the researcher may get a floor effect, because none of the participants can score at all or can complete the task – all performances are low.

The opposite effect is a ceiling effect, when the task is so easy that all achieve virtually full marks or top performances and are “hitting the ceiling”.

Research Design

In cross-sectional research , a researcher compares multiple segments of the population at the same time

Sometimes, we want to see how people change over time, as in studies of human development and lifespan. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time.

In cohort studies , the participants must share a common factor or characteristic such as age, demographic, or occupation. A cohort study is a type of longitudinal study in which researchers monitor and observe a chosen population over an extended period.

Triangulation means using more than one research method to improve the study’s validity.

Reliability

Reliability is a measure of consistency, if a particular measurement is repeated and the same result is obtained then it is described as being reliable.

• Test-retest reliability :  assessing the same person on two different occasions which shows the extent to which the test produces the same answers.
• Inter-observer reliability : the extent to which there is an agreement between two or more observers.

Meta-Analysis

A meta-analysis is a systematic review that involves identifying an aim and then searching for research studies that have addressed similar aims/hypotheses.

This is done by looking through various databases, and then decisions are made about what studies are to be included/excluded.

Strengths: Increases the conclusions’ validity as they’re based on a wider range.

Weaknesses: Research designs in studies can vary, so they are not truly comparable.

Peer Review

A researcher submits an article to a journal. The choice of the journal may be determined by the journal’s audience or prestige.

The journal selects two or more appropriate experts (psychologists working in a similar field) to peer review the article without payment. The peer reviewers assess: the methods and designs used, originality of the findings, the validity of the original research findings and its content, structure and language.

Feedback from the reviewer determines whether the article is accepted. The article may be: Accepted as it is, accepted with revisions, sent back to the author to revise and re-submit or rejected without the possibility of submission.

The editor makes the final decision whether to accept or reject the research report based on the reviewers comments/ recommendations.

Peer review is important because it prevent faulty data from entering the public domain, it provides a way of checking the validity of findings and the quality of the methodology and is used to assess the research rating of university departments.

Peer reviews may be an ideal, whereas in practice there are lots of problems. For example, it slows publication down and may prevent unusual, new work being published. Some reviewers might use it as an opportunity to prevent competing researchers from publishing work.

Some people doubt whether peer review can really prevent the publication of fraudulent research.

The advent of the internet means that a lot of research and academic comment is being published without official peer reviews than before, though systems are evolving on the internet where everyone really has a chance to offer their opinions and police the quality of research.

Types of Data

• Quantitative data is numerical data e.g. reaction time or number of mistakes. It represents how much or how long, how many there are of something. A tally of behavioral categories and closed questions in a questionnaire collect quantitative data.
• Qualitative data is virtually any type of information that can be observed and recorded that is not numerical in nature and can be in the form of written or verbal communication. Open questions in questionnaires and accounts from observational studies collect qualitative data.
• Primary data is first-hand data collected for the purpose of the investigation.
• Secondary data is information that has been collected by someone other than the person who is conducting the research e.g. taken from journals, books or articles.

Validity means how well a piece of research actually measures what it sets out to, or how well it reflects the reality it claims to represent.

Validity is whether the observed effect is genuine and represents what is actually out there in the world.

• Concurrent validity is the extent to which a psychological measure relates to an existing similar measure and obtains close results. For example, a new intelligence test compared to an established test.
• Face validity : does the test measure what it’s supposed to measure ‘on the face of it’. This is done by ‘eyeballing’ the measuring or by passing it to an expert to check.
• Ecological validit y is the extent to which findings from a research study can be generalized to other settings / real life.
• Temporal validity is the extent to which findings from a research study can be generalized to other historical times.

Features of Science

• Paradigm – A set of shared assumptions and agreed methods within a scientific discipline.
• Paradigm shift – The result of the scientific revolution: a significant change in the dominant unifying theory within a scientific discipline.
• Objectivity – When all sources of personal bias are minimised so not to distort or influence the research process.
• Empirical method – Scientific approaches that are based on the gathering of evidence through direct observation and experience.
• Replicability – The extent to which scientific procedures and findings can be repeated by other researchers.
• Falsifiability – The principle that a theory cannot be considered scientific unless it admits the possibility of being proved untrue.

Statistical Testing

A significant result is one where there is a low probability that chance factors were responsible for any observed difference, correlation, or association in the variables tested.

If our test is significant, we can reject our null hypothesis and accept our alternative hypothesis.

If our test is not significant, we can accept our null hypothesis and reject our alternative hypothesis. A null hypothesis is a statement of no effect.

In Psychology, we use p < 0.05 (as it strikes a balance between making a type I and II error) but p < 0.01 is used in tests that could cause harm like introducing a new drug.

A type I error is when the null hypothesis is rejected when it should have been accepted (happens when a lenient significance level is used, an error of optimism).

A type II error is when the null hypothesis is accepted when it should have been rejected (happens when a stringent significance level is used, an error of pessimism).

Ethical Issues

• Informed consent is when participants are able to make an informed judgment about whether to take part. It causes them to guess the aims of the study and change their behavior.
• To deal with it, we can gain presumptive consent or ask them to formally indicate their agreement to participate but it may invalidate the purpose of the study and it is not guaranteed that the participants would understand.
• Deception should only be used when it is approved by an ethics committee, as it involves deliberately misleading or withholding information. Participants should be fully debriefed after the study but debriefing can’t turn the clock back.
• All participants should be informed at the beginning that they have the right to withdraw if they ever feel distressed or uncomfortable.
• It causes bias as the ones that stayed are obedient and some may not withdraw as they may have been given incentives or feel like they’re spoiling the study. Researchers can offer the right to withdraw data after participation.
• Participants should all have protection from harm . The researcher should avoid risks greater than those experienced in everyday life and they should stop the study if any harm is suspected. However, the harm may not be apparent at the time of the study.
• Confidentiality concerns the communication of personal information. The researchers should not record any names but use numbers or false names though it may not be possible as it is sometimes possible to work out who the researchers were.

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8 Finding a Research Topic

Learning objectives.

• Learn some common sources of research ideas.
• Define the research literature in psychology and give examples of sources that are part of the research literature and sources that are not.
• Describe and use several methods for finding previous research on a particular research idea or question.

Good research must begin with a good research question. Yet coming up with good research questions is something that novice researchers often find difficult and stressful. One reason is that this is a creative process that can appear mysterious—even magical—with experienced researchers seeming to pull interesting research questions out of thin air. However, psychological research on creativity has shown that it is neither as mysterious nor as magical as it appears. It is largely the product of ordinary thinking strategies and persistence (Weisberg, 1993) [1] . This section covers some fairly simple strategies for finding general research ideas, turning those ideas into empirically testable research questions, and finally evaluating those questions in terms of how interesting they are and how feasible they would be to answer.

Finding Inspiration

Research questions often begin as more general research ideas—usually focusing on some behavior or psychological characteristic: talkativeness, learning, depression, bungee jumping, and so on. Before looking at how to turn such ideas into empirically testable research questions, it is worth looking at where such ideas come from in the first place. Three of the most common sources of inspiration are informal observations, practical problems, and previous research.

Informal observations include direct observations of our own and others’ behavior as well as secondhand observations from non-scientific sources such as newspapers, books, blogs, and so on. For example, you might notice that you always seem to be in the slowest moving line at the grocery store. Could it be that most people think the same thing? Or you might read in a local newspaper about people donating money and food to a local family whose house has burned down and begin to wonder about who makes such donations and why. Some of the most famous research in psychology has been inspired by informal observations. Stanley Milgram’s famous research on obedience to authority, for example, was inspired in part by journalistic reports of the trials of accused Nazi war criminals—many of whom claimed that they were only obeying orders. This led him to wonder about the extent to which ordinary people will commit immoral acts simply because they are ordered to do so by an authority figure (Milgram, 1963) [2] .

Practical problems can also inspire research ideas, leading directly to applied research in such domains as law, health, education, and sports. Does taking lecture notes by hand improve students’ exam performance? How effective is psychotherapy for depression compared to drug therapy? To what extent do cell phones impair people’s driving ability? How can we teach children to read more efficiently? What is the best mental preparation for running a marathon?

Probably the most common inspiration for new research ideas, however, is previous research. Recall that science is a kind of large-scale collaboration in which many different researchers read and evaluate each other’s work and conduct new studies to build on it. Of course, experienced researchers are familiar with previous research in their area of expertise and probably have a long list of ideas. This suggests that novice researchers can find inspiration by consulting with a more experienced researcher (e.g., students can consult a faculty member). But they can also find inspiration by picking up a copy of almost any professional journal and reading the titles and abstracts. In one typical issue of  Psychological Science , for example, you can find articles on the perception of shapes, anti-Semitism, police lineups, the meaning of death, second-language learning, people who seek negative emotional experiences, and many other topics. If you can narrow your interests down to a particular topic (e.g., memory) or domain (e.g., health care), you can also look through more specific journals, such as  Memory & Cognition  or  Health Psychology .

Reviewing the Research Literature

Once again, one of the most common sources of inspiration is previous research. Therefore, it is important to review the literature early in the research process. The  research literature  in any field is all the published research in that field. Reviewing the research literature means finding, reading, and summarizing the published research relevant to your topic of interest. In addition to helping you discover new research questions, reviewing the literature early in the research process can help you in several other ways.

• It can tell you if a research question has already been answered.
• It can help you evaluate the interestingness of a research question.
• It can give you ideas for how to conduct your own study.
• It can tell you how your study fits into the research literature.

The research literature in psychology is enormous—including millions of scholarly articles and books dating to the beginning of the field—and it continues to grow. Although its boundaries are somewhat fuzzy, the research literature definitely does not include self-help and other pop psychology books, dictionary and encyclopedia entries, websites, and similar sources that are intended mainly for the general public. These are considered unreliable because they are not reviewed by other researchers and are often based on little more than common sense or personal experience. Wikipedia contains much valuable information, but because its authors are anonymous and may not have any formal training or expertise in that subject area, and its content continually changes it is unsuitable as a basis of sound scientific research. For our purposes, it helps to define the research literature as consisting almost entirely of two types of sources: articles in professional journals, and scholarly books in psychology and related fields.

Professional Journals

Professional journals  are periodicals that publish original research articles. There are thousands of professional journals that publish research in psychology and related fields. They are usually published monthly or quarterly in individual issues, each of which contains several articles. The issues are organized into volumes, which usually consist of all the issues for a calendar year. Some journals are published in hard copy only, others in both hard copy and electronic form, and still others in electronic form only.

Most articles in professional journals are one of two basic types: empirical research reports and review articles.  Empirical research reports  describe one or more new empirical studies conducted by the authors. They introduce a research question, explain why it is interesting, review previous research, describe their method and results, and draw their conclusions. Review articles  summarize previously published research on a topic and usually present new ways to organize or explain the results. When a review article is devoted primarily to presenting a new theory, it is often referred to as a theoretical article . When a review article provides a statistical summary of all of the previous results it is referred to as a  meta-analysis .

Most professional journals in psychology undergo a process of  double-blind peer review . Researchers who want to publish their work in the journal submit a manuscript to the editor—who is generally an established researcher too—who in turn sends it to two or three experts on the topic. Each reviewer reads the manuscript, writes a critical but constructive review, and sends the review back to the editor along with recommendations about whether the manuscript should be published or not. The editor then decides whether to accept the article for publication, ask the authors to make changes and resubmit it for further consideration, or reject it outright. In any case, the editor forwards the reviewers’ written comments to the researchers so that they can revise their manuscript accordingly. This entire process is double-blind, as the reviewers do not know the identity of the researcher(s) and vice versa. Double-blind peer review is helpful because it ensures that the work meets basic standards of the field before it can enter the research literature. However, in order to increase transparency and accountability, some newer open access journals (e.g., Frontiers in Psychology ) utilize an open peer review process wherein the identities of the reviewers (which remain concealed during the peer review process) are published alongside the journal article.

Scholarly Books

Scholarly books  are books written by researchers and practitioners mainly for use by other researchers and practitioners. A  monograph  is written by a single author or a small group of authors and usually, gives a coherent presentation of a topic much like an extended review article.  Edited volumes have an editor or a small group of editors who recruit many authors to write separate chapters on different aspects of the same topic. Although edited volumes can also give a coherent presentation of the topic, it is not unusual for each chapter to take a different perspective or even for the authors of different chapters to openly disagree with each other. In general, scholarly books undergo a peer review process similar to that used by professional journals.

Literature Search Strategies

Using psycinfo and other databases.

The primary method used to search the research literature involves using one or more electronic databases. These include Academic Search Premier, JSTOR, and ProQuest for all academic disciplines, ERIC for education, and PubMed for medicine and related fields. The most important for our purposes, however, is PsycINFO, which is produced by the American Psychological Association (APA). PsycINFO is so comprehensive—covering thousands of professional journals and scholarly books going back more than 100 years—that for most purposes its content is synonymous with the research literature in psychology. Like most such databases, PsycINFO is usually available through your university library.

PsycINFO consists of individual records for each article, book chapter, or book in the database. Each record includes basic publication information, an abstract or summary of the work (like the one presented at the start of this chapter), and a list of other works cited by that work. A computer interface allows entering one or more search terms and returns any records that contain those search terms. (These interfaces are provided by different vendors and therefore can look somewhat different depending on the library you use.) Each record also contains lists of keywords that describe the content of the work and also a list of index terms. The index terms are especially helpful because they are standardized. Research on differences between females and males, for example, is always indexed under “Human Sex Differences.” Research on note-taking is always indexed under the term “Learning Strategies.” If you do not know the appropriate index terms, PsycINFO includes a thesaurus that can help you find them.

Given that there are nearly four million records in PsycINFO, you may have to try a variety of search terms in different combinations and at different levels of specificity before you find what you are looking for. Imagine, for example, that you are interested in the question of whether males and females differ in terms of their ability to recall experiences from when they were very young. If you were to enter the search term “memory,” it would return far too many records to look through individually. This is where the thesaurus helps. Entering “memory” into the thesaurus provides several more specific index terms—one of which is “early memories.” While searching for “early memories” among the index terms still returns too many to look through individually—combining it with “human sex differences” as a second search term returns fewer articles, many of which are highly relevant to the topic.

Depending on the vendor that provides the interface to PsycINFO, you may be able to save, print, or e-mail the relevant PsycINFO records. The records might even contain links to full-text copies of the works themselves. (PsycARTICLES is a database that provides full-text access to articles in all journals published by the APA.) If not, and you want a copy of the work, you will have to find out if your library carries the journal or has the book and the hard copy on the library shelves. Be sure to ask a librarian if you need help.

https://youtu.be/fhhctbaVXvk

Using Other Search Techniques

In addition to entering search terms into PsycINFO and other databases, there are several other techniques you can use to search the research literature. First, if you have one good article or book chapter on your topic—a recent review article is best—you can look through the reference list of that article for other relevant articles, books, and book chapters. In fact, you should do this with any relevant article or book chapter you find. You can also start with a classic article or book chapter on your topic, find its record in PsycINFO (by entering the author’s name or article’s title as a search term), and link from there to a list of other works in PsycINFO that cite that classic article. This works because other researchers working on your topic are likely to be aware of the classic article and cite it in their own work. You can also do a general Internet search using search terms related to your topic or the name of a researcher who conducts research on your topic. This might lead you directly to works that are part of the research literature (e.g., articles in open-access journals or posted on researchers’ own websites). The search engine Google Scholar is especially useful for this purpose. A general Internet search might also lead you to websites that are not part of the research literature but might provide references to works that are. Finally, you can talk to people (e.g., your instructor or other faculty members in psychology) who know something about your topic and can suggest relevant articles and book chapters.

What to Search For

When you do a literature review, you need to be selective. Not every article, book chapter, and book that relates to your research idea or question will be worth obtaining, reading, and integrating into your review. Instead, you want to focus on sources that help you do four basic things: (a) refine your research question, (b) identify appropriate research methods, (c) place your research in the context of previous research, and (d) write an effective research report. Several basic principles can help you find the most useful sources.

First, it is best to focus on recent research, keeping in mind that what counts as recent depends on the topic. For newer topics that are actively being studied, “recent” might mean published in the past year or two. For older topics that are receiving less attention right now, “recent” might mean within the past 10 years. You will get a feel for what counts as recent for your topic when you start your literature search. A good general rule, however, is to start with sources published in the past five years. The main exception to this rule would be classic articles that turn up in the reference list of nearly every other source. If other researchers think that this work is important, even though it is old, then, by all means, you should include it in your review.

Second, you should look for review articles on your topic because they will provide a useful overview of it—often discussing important definitions, results, theories, trends, and controversies—giving you a good sense of where your own research fits into the literature. You should also look for empirical research reports addressing your question or similar questions, which can give you ideas about how to measure your variables and collect your data. As a general rule, it is good to use methods that others have already used successfully unless you have good reasons not to. Finally, you should look for sources that provide information that can help you argue for the interestingness of your research question. For a study on the effects of cell phone use on driving ability, for example, you might look for information about how widespread cell phone use is, how frequent and costly motor vehicle crashes are, and so on.

How many sources are enough for your literature review? This is a difficult question because it depends on how extensively your topic has been studied and also on your own goals. One study found that across a variety of professional journals in psychology, the average number of sources cited per article was about 50 (Adair & Vohra, 2003) [3] . This gives a rough idea of what professional researchers consider to be adequate. As a student, you might be assigned a much lower minimum number of references to include, but the principles for selecting the most useful ones remain the same.

• Weisberg, R. W. (1993). Creativity: Beyond the myth of genius . New York, NY: Freeman. ↵
• Milgram, S. (1963). Behavioral study of obedience. Journal of Abnormal and Social Psychology, 67 , 371–378. ↵
• Adair, J. G., & Vohra, N. (2003). The explosion of knowledge, references, and citations: Psychology’s unique response to a crisis. American Psychologist, 58 , 15–23. ↵
• Define what survey research is, including its two important characteristics.
• Describe several different ways that survey research can be used and give some examples.

What Is Survey Research?

Survey research  is a quantitative and qualitative method with two important characteristics. First, the variables of interest are measured using self-reports (using questionnaires or interviews). In essence, survey researchers ask their participants (who are often called respondents  in survey research) to report directly on their own thoughts, feelings, and behaviors. Second, considerable attention is paid to the issue of sampling. In particular, survey researchers have a strong preference for large random samples because they provide the most accurate estimates of what is true in the population. In fact, survey research may be the only approach in psychology in which random sampling is routinely used. Beyond these two characteristics, almost anything goes in survey research. Surveys can be long or short. They can be conducted in person, by telephone, through the mail, or over the Internet. They can be about voting intentions, consumer preferences, social attitudes, health, or anything else that it is possible to ask people about and receive meaningful answers.  Although survey data are often analyzed using statistics, there are many questions that lend themselves to more qualitative analysis.

Most survey research is non-experimental. It is used to describe single variables (e.g., the percentage of voters who prefer one presidential candidate or another, the prevalence of schizophrenia in the general population, etc.) and also to assess statistical relationships between variables (e.g., the relationship between income and health). But surveys can also be used within experimental research. The study by Lerner and her colleagues is a good example. Their use of self-report measures and a large national sample identifies their work as survey research. But their manipulation of an independent variable (anger vs. fear) to assess its effect on a dependent variable (risk judgments) also identifies their work as experimental.

History and Uses of Survey Research

Survey research may have its roots in English and American “social surveys” conducted around the turn of the 20th century by researchers and reformers who wanted to document the extent of social problems such as poverty (Converse, 1987) [1] . By the 1930s, the US government was conducting surveys to document economic and social conditions in the country. The need to draw conclusions about the entire population helped spur advances in sampling procedures. At about the same time, several researchers who had already made a name for themselves in market research, studying consumer preferences for American businesses, turned their attention to election polling. A watershed event was the presidential election of 1936 between Alf Landon and Franklin Roosevelt. A magazine called  Literary Digest  conducted a survey by sending ballots (which were also subscription requests) to millions of Americans. Based on this “straw poll,” the editors predicted that Landon would win in a landslide. At the same time, the new pollsters were using scientific methods with much smaller samples to predict just the opposite—that Roosevelt would win in a landslide. In fact, one of them, George Gallup, publicly criticized the methods of Literary Digest before the election and all but guaranteed that his prediction would be correct. And of course, it was, demonstrating the effectiveness of careful survey methodology (We will consider the reasons that Gallup was right later in this chapter). Gallup's demonstration of the power of careful survey methods led later researchers to to local, and in 1948, the first national election survey by the Survey Research Center at the University of Michigan. This work eventually became the American National Election Studies ( https://electionstudies.org/ ) as a collaboration of Stanford University and the University of Michigan, and these studies continue today.

From market research and election polling, survey research made its way into several academic fields, including political science, sociology, and public health—where it continues to be one of the primary approaches to collecting new data. Beginning in the 1930s, psychologists made important advances in questionnaire design, including techniques that are still used today, such as the Likert scale. (See “What Is a Likert Scale?” in  Section 7.2 "Constructing Survey Questionnaires" .) Survey research has a strong historical association with the social psychological study of attitudes, stereotypes, and prejudice. Early attitude researchers were also among the first psychologists to seek larger and more diverse samples than the convenience samples of university students that were routinely used in psychology (and still are).

Survey research continues to be important in psychology today. For example, survey data have been instrumental in estimating the prevalence of various mental disorders and identifying statistical relationships among those disorders and with various other factors. The National Comorbidity Survey is a large-scale mental health survey conducted in the United States (see http://www.hcp.med.harvard.edu/ncs ). In just one part of this survey, nearly 10,000 adults were given a structured mental health interview in their homes in 2002 and 2003.  Table 7.1  presents results on the lifetime prevalence of some anxiety, mood, and substance use disorders. (Lifetime prevalence is the percentage of the population that develops the problem sometime in their lifetime.) Obviously, this kind of information can be of great use both to basic researchers seeking to understand the causes and correlates of mental disorders as well as to clinicians and policymakers who need to understand exactly how common these disorders are.

And as the opening example makes clear, survey research can even be used as a data collection method within experimental research to test specific hypotheses about causal relationships between variables. Such studies, when conducted on large and diverse samples, can be a useful supplement to laboratory studies conducted on university students. Survey research is thus a flexible approach that can be used to study a variety of basic and applied research questions.

• Describe the cognitive processes involved in responding to a survey item.
• Explain what a context effect is and give some examples.
• Create a simple survey questionnaire based on principles of effective item writing and organization.

The heart of any survey research project is the survey itself. Although it is easy to think of interesting questions to ask people, constructing a good survey is not easy at all. The problem is that the answers people give can be influenced in unintended ways by the wording of the items, the order of the items, the response options provided, and many other factors. At best, these influences add noise to the data. At worst, they result in systematic biases and misleading results. In this section, therefore, we consider some principles for constructing surveys to minimize these unintended effects and thereby maximize the reliability and validity of respondents’ answers.

Survey Responding as a Psychological Process

Before looking at specific principles of survey construction, it will help to consider survey responding as a psychological process.

A Cognitive Model

Figure 7.1  presents a model of the cognitive processes that people engage in when responding to a survey item (Sudman, Bradburn, & Schwarz, 1996) [2] . Respondents must interpret the question, retrieve relevant information from memory, form a tentative judgment, convert the tentative judgment into one of the response options provided (e.g., a rating on a 1-to-7 scale), and finally edit their response as necessary.

Consider, for example, the following questionnaire item:

How many alcoholic drinks do you consume in a typical day?

• _____ a lot more than average
• _____ somewhat more than average
• _____ average
• _____ somewhat fewer than average
• _____ a lot fewer than average

Although this item at first seems straightforward, it poses several difficulties for respondents. First, they must interpret the question. For example, they must decide whether “alcoholic drinks” include beer and wine (as opposed to just hard liquor) and whether a “typical day” is a typical weekday, typical weekend day, or both . Even though Chang and Krosnick (2003) [3] found that asking about “typical” behavior has been shown to be more valid than asking about “past” behavior, their study compared “typical week” to “past week” and may be different when considering typical weekdays or weekend days) . Once respondents have interpreted the question, they must retrieve relevant information from memory to answer it. But what information should they retrieve, and how should they go about retrieving it? They might think vaguely about some recent occasions on which they drank alcohol, they might carefully try to recall and count the number of alcoholic drinks they consumed last week, or they might retrieve some existing beliefs that they have about themselves (e.g., “I am not much of a drinker”). Then they must use this information to arrive at a tentative judgment about how many alcoholic drinks they consume in a typical day. For example, this  mental calculation  might mean dividing the number of alcoholic drinks they consumed last week by seven to come up with an average number per day. Then they must format this tentative answer in terms of the response options actually provided. In this case, the options pose additional problems of interpretation. For example, what does “average” mean, and what would count as “somewhat more” than average? Finally, they must decide whether they want to report the response they have come up with or whether they want to edit it in some way. For example, if they believe that they drink a lot more than average, they might not want to report that  for fear of looking bad in the eyes of the researcher, so instead, they may opt to select the "somewhat more than average" response option.

From this perspective, what at first appears to be a simple matter of asking people how much they drink (and receiving a straightforward answer from them) turns out to be much more complex.

Context Effects on Survey Responses

Again, this complexity can lead to unintended influences on respondents’ answers. These are often referred to as  context effects   because they are not related to the content of the item but to the context in which the item appears (Schwarz & Strack, 1990) [4] . For example, there is an  item-order effect  when the order in which the items are presented affects people’s responses. One item can change how participants interpret a later item or change the information that they retrieve to respond to later items. For example, researcher Fritz Strack and his colleagues asked college students about both their general life satisfaction and their dating frequency (Strack, Martin, & Schwarz, 1988) [5] . When the life satisfaction item came first, the correlation between the two was only −.12, suggesting that the two variables are only weakly related. But when the dating frequency item came first, the correlation between the two was +.66, suggesting that those who date more have a strong tendency to be more satisfied with their lives. Reporting the dating frequency first made that information more accessible in memory so that they were more likely to base their life satisfaction rating on it.

The response options provided can also have unintended effects on people’s responses (Schwarz, 1999) [6] . For example, when people are asked how often they are “really irritated” and given response options ranging from “less than once a year” to “more than once a month,” they tend to think of major irritations and report being irritated infrequently. But when they are given response options ranging from “less than once a day” to “several times a month,” they tend to think of minor irritations and report being irritated frequently. People also tend to assume that middle response options represent what is normal or typical. So if they think of themselves as normal or typical, they tend to choose middle response options. For example, people are likely to report watching more television when the response options are centered on a middle option of 4 hours than when centered on a middle option of 2 hours. To mitigate against order effects, rotate questions and response items when there is no natural order. Counterbalancing or randomizing the order of presentation of the questions in online surveys are good practices for survey questions and can reduce response order effects that show that among undecided voters, the first candidate listed in a ballot receives a 2.5% boost simply by virtue of being listed first [7] !

Writing Survey Items

Types of items.

Questionnaire items can be either open-ended or closed-ended.  Open-ended items  simply ask a question and allow participants to answer in whatever way they choose. The following are examples of open-ended questionnaire items.

• “What is the most important thing to teach children to prepare them for life?”
• “Please describe a time when you were discriminated against because of your age.”
• “Is there anything else you would like to tell us about?”

Open-ended items are useful when researchers do not know how participants might respond or when they want to avoid influencing their responses. Open-ended items are more qualitative in nature, so they tend to be used when researchers have more vaguely defined research questions—often in the early stages of a research project. Open-ended items are relatively easy to write because there are no response options to worry about. However, they take more time and effort on the part of participants, and they are more difficult for the researcher to analy z e because the answers must be transcribed, coded, and submitted to some form of qualitative analysis, such as content analysis. Another disadvantage is that respondents are more likely to skip open-ended items because they take longer to answer. It is best to use open-ended questions when the answer is unsure or for quantities which can easily be converted to categories later in the analysis.

Closed-ended items  ask a question and provide a set of response options for participants to choose from. The alcohol item just mentioned is an example, as are the following:

  How old are you?

• _____ Under 18
• _____ 18 to 34
• _____ 35 to 49
• _____ 50 to 70
• _____ Over 70

On a scale of 0 (no pain at all) to 10 (worst pain ever experienced), how much pain are you in right now?

Have you ever in your adult life been depressed for a period of 2 weeks or more? Yes    No

Closed-ended items are used when researchers have a good idea of the different responses that participants might make. They are more quantitative in nature, so they are also used when researchers are interested in a well-defined variable or construct such as participants’ level of agreement with some statement, perceptions of risk, or frequency of a particular behavior. Closed-ended items are more difficult to write because they must include an appropriate set of response options. However, they are relatively quick and easy for participants to complete. They are also much easier for researchers to analyze because the responses can be easily converted to numbers and entered into a spreadsheet. For these reasons, closed-ended items are much more common.

All closed-ended items include a set of response options from which a participant must choose. For categorical variables like sex, race, or political party preference, the categories are usually listed and participants choose the one (or ones) to which they belong. For quantitative variables, a rating scale is typically provided. A  rating scale  is an ordered set of responses that participants must choose from.  Figure 7.2  shows several examples. The number of response options on a typical rating scale ranges from three to 11—although five and seven are probably most common. Five-point scales are best for unipolar scales where only one construct is tested, such as frequency (Never, Rarely, Sometimes, Often, Always). Seven-point scales are best for bipolar scales where there is a dichotomous spectrum, such as liking (Like very much, Like somewhat, Like slightly, Neither like nor dislike, Dislike slightly, Dislike somewhat, Dislike very much). For bipolar questions, it is useful to offer an earlier question that branches them into an area of the scale; if asking about liking ice cream, first ask “Do you generally like or dislike ice cream?” Once the respondent chooses like or dislike, refine it by offering them relevant choices from the seven-point scale.  Branching improves both reliability and validity  (Krosnick & Berent, 1993) [8] .  Although you often see scales with numerical labels, it is best to only present verbal labels to the respondents but convert them to numerical values in the analyses. Avoid partial labels or length or overly specific labels. In some cases, the verbal labels can be supplemented with (or even replaced by) meaningful graphics. The last rating scale shown in  Figure 7.3  is a visual-analog scale, on which participants make a mark somewhere along the horizontal line to indicate the magnitude of their response.

What Is a Likert Scale?

In reading about psychological research, you are likely to encounter the term  Likert scale . Although this term is sometimes used to refer to almost any rating scale (e.g., a 0-to-10 life satisfaction scale), it has a much more precise meaning.

In the 1930s, researcher Rensis Likert (pronounced LICK-ert) created a new approach for measuring people’s attitudes (Likert, 1932) [9] . It involves presenting people with several statements—including both favorable and unfavorable statements—about some person, group, or idea. Respondents then express their agreement or disagreement with each statement on a 5-point scale:  Strongly Agree ,  Agree ,  Neither Agree nor Disagree ,  Disagree , Strongly Disagree . Numbers are assigned to each response a nd then summed across all items to produce a score representing the attitude toward the person, group, or idea. For items that are phrased in an opposite direction (e.g., negatively worded statements instead of positively worded statements), reverse coding is used so that the numerical scoring of statements also runs in the opposite direction. The entire set of items came to be called a Likert scale.

Thus unless you are measuring people’s attitude toward something by assessing their level of agreement with several statements about it, it is best to avoid calling it a Likert scale. You are probably just using a “rating scale.”

Writing Effective Items

We can now consider some principles of writing questionnaire items that minimize unintended context effects and maximize the reliability and validity of participants’ responses. A rough guideline for writing questionnaire items is provided by the BRUSO model (Peterson, 2000) [10] . An acronym,  BRUSO  stands for “brief,” “relevant,” “unambiguous,” “specific,” and “objective.” Effective questionnaire items are  brief  and to the point. They avoid long, overly technical, or unnecessary words. This brevity makes them easier for respondents to understand and faster for them to complete. Effective questionnaire items are also  relevant  to the research question. If a respondent’s sexual orientation, marital status, or income is not relevant, then items on them should probably not be included. Again, this makes the questionnaire faster to complete, but it also avoids annoying respondents with what they will rightly perceive as irrelevant or even “nosy” questions. Effective questionnaire items are also unambiguous ; they can be interpreted in only one way. Part of the problem with the alcohol item presented earlier in this section is that different respondents might have different ideas about what constitutes “an alcoholic drink” or “a typical day.” Effective questionnaire items are also  specific so that it is clear to respondents what their response  should  be about and clear to researchers what it  is  about. A common problem here is closed-ended items that are “double barrelled.” They ask about two conceptually separate issues but allow only one response. For example, “Please rate the extent to which you have been feeling anxious and depressed.” This item should probably be split into two separate items—one about anxiety and one about depression. Finally, effective questionnaire items are  objective  in the sense that they do not reveal the researcher’s own opinions or lead participants to answer in a particular way. Table 7.2  shows some examples of poor and effective questionnaire items based on the BRUSO criteria. The best way to know how people interpret the wording of the question is to conduct a pilot test and ask a few people to explain how they interpreted the question.

For closed-ended items, it is also important to create an appropriate response scale. For categorical variables, the categories presented should generally be mutually exclusive and exhaustive. Mutually exclusive categories do not overlap. For a religion item, for example, the categories of  Christian  and Catholic  are not mutually exclusive but  Protestant  and  Catholic  are mutually exclusive. Exhaustive categories cover all possible responses. Although  Protestant  and  Catholic  are mutually exclusive, they are not exhaustive because there are many other religious categories that a respondent might select:  Jewish ,  Hindu ,  Buddhist , and so on. In many cases, it is not feasible to include every possible category, in which case an  Other  category, with a space for the respondent to fill in a more specific response, is a good solution. If respondents could belong to more than one category (e.g., race), they should be instructed to choose all categories that apply.

For rating scales, five or seven response options generally allow about as much precision as respondents are capable of. However, numerical scales with more options can sometimes be appropriate. For dimensions such as attractiveness, pain, and likelihood, a 0-to-10 scale will be familiar to many respondents and easy for them to use. Regardless of the number of response options, the most extreme ones should generally be “balanced” around a neutral or modal midpoint. An example of an unbalanced rating scale measuring perceived likelihood might look like this:

Unlikely  |  Somewhat Likely  |  Likely  |  Very Likely  |  Extremely Likely

A balanced version might look like this:

Extremely Unlikely  |  Somewhat Unlikely  |  As Likely as Not  |  Somewhat Likely  | Extremely Likely

 Note, however, that a middle or neutral response option does not have to be included. Researchers sometimes choose to leave it out because they want to encourage respondents to think more deeply about their response and not simply choose the middle option by default. However, including middle alternatives on bipolar dimensions can be used to allow people to choose an option that is neither.

Formatting the Survey

Writing effective items is only one part of constructing a survey. For one thing, every survey should have a written or spoken introduction that serves two basic functions (Peterson, 2000) [11] . One is to encourage respondents to participate in the survey. In many types of research, such encouragement is not necessary either because participants do not know they are in a study (as in naturalistic observation) or because they are part of a subject pool and have already shown their willingness to participate by signing up and showing up for the study. Survey research usually catches respondents by surprise when they answer their phone, go to their mailbox, or check their e-mail—and the researcher must make a good case for why they should agree to participate. Thus the introduction should briefly explain the purpose of the survey and its importance, provide information about the sponsor of the survey (university-based surveys tend to generate higher response rates), acknowledge the importance of the respondent’s participation, and describe any incentives for participating.

The second function of the introduction is to establish informed consent. Remember that this involves describing to respondents everything that might affect their decision to participate. This includes the topics covered by the survey, the amount of time it is likely to take, the respondent’s option to withdraw at any time, confidentiality issues, and so on. Written consent forms are not always used in survey research (when the research is of minimal risk and completion of the survey instrument is often accepted by the IRB as evidence of consent to participate), so it is important that this part of the introduction be well documented and presented clearly and in its entirety to every respondent.

The introduction should be followed by the substantive questionnaire items. But first, it is important to present clear instructions for completing the questionnaire, including examples of how to use any unusual response scales. Remember that the introduction is the point at which respondents are usually most interested and least fatigued, so it is good practice to start with the most important items for purposes of the research and proceed to less important items. Items should also be grouped by topic or by type. For example, items using the same rating scale (e.g., a 5-point agreement scale) should be grouped together if possible to make things faster and easier for respondents. Demographic items are often presented last because they are least interesting to participants but also easy to answer in the event respondents have become tired or bored. Of course, any survey should end with an expression of appreciation to the respondent.

• Explain the difference between probability and non-probability sampling, and describe the major types of probability sampling.
• Define sampling bias in general and non-response bias in particular. List some techniques that can be used to increase the response rate and reduce non-response bias.
• List the four major ways to conduct a survey along with some pros and cons of each.

In this section, we consider how to go about conducting a survey. We first consider the issue of sampling, followed by some different methods of actually collecting survey data.

Essentially all psychological research involves sampling—selecting a sample to study from the population of interest. Sampling falls into two broad categories. The first category, Probability sampling , occurs when the researcher can specify the probability that each member of the population will be selected for the sample. The second is Non-probability sampling , which occurs when the researcher cannot specify these probabilities. Most psychological research involves non-probability sampling. For example, Convenience sampling —studying individuals who happen to be nearby and willing to participate—is a very common form of non-probability sampling used in psychological research. Other forms of non-probability sampling include snowball sampling (in which existing research participants help recruit additional participants for the study), quota sampling (in which subgroups in the sample are recruited to be proportional to those subgroups in the population), and self-selection sampling (in which individuals choose to take part in the research on their own accord, without being approached by the researcher directly).

Survey researchers, however, are much more likely to use some form of probability sampling. This  tendency  is because the goal of most survey research is to make accurate estimates about what is true in a particular population, and these estimates are most accurate when based on a probability sample. For example, it is important for survey researchers to base their estimates of election outcomes—which are often decided by only a few percentage points—on probability samples of likely registered voters.

Compared with non-probability sampling, probability sampling requires a very clear specification of the population, which of course depends on the research questions to be answered. The population might be all registered voters in Washington State , all American   consumers who have purchased a car in the past year, women in the Seattle  over 40 years old who have received a mammogram in the past decade, or all the alumni of a particular university. Once the population has been specified, probability sampling requires a sampling frame . This  sampling frame  is essentially a list of all the members of the population from which to select the respondents. Sampling frames can come from a variety of sources, including telephone directories, lists of registered voters, and hospital or insurance records. In some cases, a map can serve as a sampling frame, allowing for the selection of cities, streets, or households.

There are a variety of different probability sampling methods. Simple random sampling   is done in such a way that each individual in the population has an equal probability of being selected for the sample. This  type of sampling  could involve putting the names of all individuals in the sampling frame into a hat, mixing them up, and then drawing out the number needed for the sample. Given that most sampling frames take the form of computer files, random sampling is more likely to involve computerized sorting or selection of respondents. A common approach in telephone surveys is random-digit dialing, in which a computer randomly generates phone numbers from among the possible phone numbers within a given geographic area.

A common alternative to simple random sampling is stratified random sampling , in which the population is divided into different subgroups or “strata” (usually based on demographic characteristics) and then a random sample is taken from each “stratum.” Proportionate stratified random sampling can be used to select a sample in which the proportion of respondents in each of various subgroups matches the proportion in the population. For example, because about 12.6 % of the American population is African American , stratified random sampling can be used to ensure that a survey of 1,000 American adults includes about 126 African-American respondents. Disproportionate stratified random sampling can also be used to sample extra respondents from particularly small subgroups—allowing valid conclusions to be drawn about those subgroups. For example, because Asian Americans make up a relatively small percentage of the American population (about 5.6 %), a simple random sample of 1,000 American adults might include too few Asian Americans to draw any conclusions about them as distinct from any other subgroup. If representation is important to the research question, however, then disproportionate stratified random sampling could be used to ensure that enough Asian-American respondents are included in the sample to draw valid conclusions about Asian American s a whole.

Yet another type of probability sampling is  cluster sampling , in which larger clusters of individuals are randomly sampled and then individuals within each cluster are randomly sampled. This is the only probability sampling method that does not require a sampling frame. For example, to select a sample of small-town residents in Washington , a researcher might randomly select several small towns and then randomly select several individuals within each town. Cluster sampling is especially useful for surveys that involve face-to-face interviewing because it minimizes the amount of traveling that the interviewers must do. For example, instead of traveling to 200 small towns to interview 200 residents, a research team could travel to 10 small towns and interview 20 residents of each. The National Comorbidity Survey was done using a form of cluster sampling.

How large does a survey sample need to be? In general, this  estimate depends on two factors. One is the level of confidence in the result that the researcher wants. The larger the sample, the closer any statistic based on that sample will tend to be to the corresponding value in the population. The other factor is a practical constraint in the form of the budget of the study. Larger samples provide greater confidence, but they take more time, effort, and money to obtain. Taking these two factors into account, most survey research uses sample sizes that range from about 100 to about 1,000. Conducting a power analysis prior to launching the survey helps to guide the researcher in making this trade-off.

Sample Size and Population Size

Why is a sample of about 1,000 considered to be adequate for most survey research—even when the population is much larger than that? Consider, for example, that a sample of only 1,000 American adults is generally considered a good sample of the roughly 252 million adults in the American population—even though it includes only about 0.000004% of the population! The answer is a bit surprising.

One part of the answer is that a statistic based on a larger sample will tend to be closer to the population value and that this can be characterized mathematically. Imagine, for example, that in a sample of registered voters, exactly 50% say they intend to vote for the incumbent. If there are 100 voters in this sample, then there is a 95% chance that the true percentage in the population is between 40 and 60. But if there are 1,000 voters in the sample, then there is a 95% chance that the true percentage in the population is between 47 and 53. Although this “95% confidence interval” continues to shrink as the sample size increases, it does so at a slower rate. For example, if there are 2,000 voters in the sample, then this reduction only reduces the 95% confidence interval to 48 to 52. In many situations, the small increase in confidence beyond a sample size of 1,000 is not considered to be worth the additional time, effort, and money.

Another part of the answer—and perhaps the more surprising part—is that confidence intervals depend only on the size of the sample and not on the size of the population. So a sample of 1,000 would produce a 95% confidence interval of 47 to 53 regardless of whether the population size was a hundred thousand, a million, or a hundred million.

Sampling Bias

Probability sampling was developed in large part to address the issue of sampling bias.  Sampling bias  occurs when a sample is selected in such a way that it is not representative of the entire population and therefore produces inaccurate results. This  bias  was the reason that the  Literary Digest  straw poll was so far off in its prediction of the 1936 presidential election. The mailing lists used came largely from telephone directories and lists of registered automobile owners, which over-represented wealthier people, who were more likely to vote for Landon. Gallup was successful because he knew about this bias and found ways to sample less wealthy people as well.

There is one form of sampling bias that even careful random sampling is subject to. It is almost never the case that everyone selected for the sample actually responds to the survey. Some may have died or moved away, and others may decline to participate because they are too busy, are not interested in the survey topic, or do not participate in surveys on principle. If these survey non-responders differ from survey responders in systematic ways, then this  difference  can produce non-response bias . For example, in a mail survey on alcohol consumption, researcher Vivienne Lahaut and colleagues found that only about half the sample responded after the initial contact and two follow-up reminders (Lahaut, Jansen, van de Mheen, & Garretsen, 2002) [12] . The danger here is that the half who responded might have different patterns of alcohol consumption than the half who did not, which could lead to inaccurate conclusions on the part of the researchers. So to test for non-response bias, the researchers later made unannounced visits to the homes of a subset of the non-responders—coming back up to five times if they did not find them at home. They found that the original non-responders included an especially high proportion of abstainers (nondrinkers), which meant that their estimates of alcohol consumption based only on the original responders were too high.

Although there are methods for statistically correcting for non-response bias, they are based on assumptions about the non-responders—for example, that they are more similar to late responders than to early responders—which may not be correct. For this reason, the best approach to minimizing non-response bias is to minimize the number of non-responders—that is, to maximize the response rate. There is a large research literature on the factors that affect survey response rates (Groves et al., 2004) [13] . In general, in-person interviews have the highest response rates, followed by telephone surveys, and then mail and Internet surveys. Among the other factors that increase response rates are sending potential respondents a short pre-notification message informing them that they will be asked to participate in a survey in the near future and sending simple follow-up reminders to non-responders after a few weeks. The perceived length and complexity of the survey can also make a difference, which is why it is important to keep survey questionnaires as short, simple, and on topic as possible. Finally, offering an incentive—especially cash—is a reliable way to increase response rates.  However, ethically, there are limits to offering incentives that may be so large as to be considered coercive.

Conducting the Survey

The four main ways to conduct surveys are through in-person interviews, by telephone, through the mail, and over the internet. As with other aspects of survey design, the choice depends on both the researcher’s goals and the budget. In-person interviews have the highest response rates and provide the closest personal contact with respondents. Personal contact can be important, for example, when the interviewer must see and make judgments about respondents, as is the case with some mental health interviews. But in-person interviewing is by far the most costly approach. Telephone surveys have lower response rates and still provide some personal contact with respondents. They can also be costly but are generally less so than in-person interviews. Traditionally, telephone directories have provided fairly comprehensive sampling frames.  However, this trend is less true today as more people choose to only have cell phones and do not install land lines that would be included in telephone directories.  Mail surveys are less costly still but generally have even lower response rates—making them most susceptible to non-response bias.

Not surprisingly, internet surveys are becoming more common. They are increasingly easy to construct and use (see “Online Survey Creation”). Although initial contact can be made by mail with a link provided to the survey, this approach does not necessarily produce higher response rates than an ordinary mail survey. A better approach is to make initial contact by email with a link directly to the survey. This approach can work well when the population consists of the members of an organization who have known email addresses and regularly use them (e.g., a university community). For other populations, it can be difficult or impossible to find a comprehensive list of email addresses to serve as a sampling frame. Alternatively, a request to participate in the survey with a link to it can be posted on websites known to be visited by members of the population. But again it is very difficult to get anything approaching a random sample this way because the members of the population who visit the websites are likely to be different from the population as a whole. However, internet survey methods are in rapid development. Because of their low cost, and because more people are online than ever before, internet surveys are likely to become the dominant approach to survey data collection in the near future.

Finally, it is important to note that some of the concerns that people have about collecting data online (e.g., that internet-based findings differ from those obtained with other methods) have been found to be myths. Table 7.3 (adapted from Gosling, Vazire, Srivastava, & John, 2004) [14] addresses three such preconceptions about data collected in web-based studies:

Table 7.3 Some Preconceptions and Findings Pertaining to Web-based Studies

Online Survey Creation

There are now several online tools for creating online questionnaires. After a questionnaire is created, a link to it can then be emailed to potential respondents or embedded in a web page. The following websites are among those that offer free accounts. Although the free accounts limit the number of questionnaire items and the number of respondents, they can be useful for doing small-scale surveys and for practicing the principles of good questionnaire construction. Here are some commonly used online survey tools:

• SurveyMonkey— https://surveymonkey.com
• PsyToolkit— https://www.psytoolkit.org/ (free, noncommercial, and does many experimental paradigms)
• Qualtrics— https://www.qualtrics.com/
• PsycData— https://www.psychdata.com/

A small note of caution: the data from US survey software are held on US servers, and are subject to be seized as granted through the Patriot Act. To avoid infringing on any rights, the following is a list of online survey sites that are hosted in Canada:

• Fluid Surveys— http://fluidsurveys.com/
• Simple Survey— http://www.simplesurvey.com/
• Lime Survey— https://www.limesurvey.org

There are also survey sites hosted in other countries outside of North America.

Another new tool for survey researchers is Mechanical Turk (MTurk) created by Amazon.com https://www.mturk.com Originally created for simple usability testing, MTurk has a database of over 500,000 workers from over 190 countries [15] . You can put simple tasks (for example, different question wording to test your survey items), set parameters as your sample frame dictates and deploy your experiment at a very low cost (for example, a few cents for less than 5 minutes). MTurk has been lauded as an inexpensive way to gather high-quality data (Buhrmester, Kwang, & Gosling, 2011) [16] .

• Explain what quasi-experimental research is and distinguish it clearly from both experimental and correlational research.
• Describe three different types of one-group quasi-experimental designs.
• Identify the threats to internal validity associated with each of these designs. 

One-Group Posttest Only Design

In a  one-group posttest only design ,  a treatment is implemented (or an independent variable is manipulated) and then a dependent variable is measured once after the treatment is implemented. Imagine, for example, a researcher who is interested in the effectiveness of an anti-drug education program on elementary school students’ attitudes toward illegal drugs. The researcher could implement the anti-drug program, and then immediately after the program ends, the researcher could measure students' attitudes toward illegal drugs.

This is the weakest type of quasi-experimental design. A major limitation to this design is the lack of a control or comparison group. There is no way to determine what the attitudes of these students would have been if they hadn’t completed the anti-drug program. Despite this major limitation, results from this design are frequently reported in the media and are often misinterpreted by the general population. For instance, advertisers might claim that 80% of women noticed their skin looked bright after using Brand X cleanser for a month. If there is no comparison group, then this statistic means little to nothing.

One-Group Pretest-Posttest Design

In a one-group   pretest-posttest design , the dependent variable is measured once before the treatment is implemented and once after it is implemented. Let's return to the example of a researcher who is interested in the effectiveness of an anti-drug education program on elementary school students’ attitudes toward illegal drugs. The researcher could measure the attitudes of students at a particular elementary school during one week, implement the anti-drug program during the next week, and finally, measure their attitudes again the following week. The pretest-posttest design is much like a within-subjects experiment in which each participant is tested first under the control condition and then under the treatment condition. It is unlike a within-subjects experiment, however, in that the order of conditions is not counterbalanced because it typically is not possible for a participant to be tested in the treatment condition first and then in an “untreated” control condition.

If the average posttest score is better than the average pretest score (e.g., attitudes toward illegal drugs are more negative after the anti-drug educational program), then it makes sense to conclude that the treatment might be responsible for the improvement. Unfortunately, one often cannot conclude this with a high degree of certainty because there may be other explanations for why the posttest scores may have changed. These alternative explanations pose threats to internal validity.

One alternative explanation goes under the name of history . Other things might have happened between the pretest and the posttest that caused a change from pretest to posttest. Perhaps an anti-drug program aired on television and many of the students watched it, or perhaps a celebrity died of a drug overdose and many of the students heard about it.

Another alternative explanation goes under the name of maturation . Participants might have changed between the pretest and the posttest in ways that they were going to anyway because they are growing and learning. If it were a year long anti-drug program, participants might become less impulsive or better reasoners and this might be responsible for the change in their attitudes toward illegal drugs.

Another threat to the internal validity of one-group pretest-posttest designs is  testing , which refers to when the act of measuring the dependent variable during the pretest affects participants' responses at posttest. For instance, completing the measure of attitudes towards illegal drugs may have had an effect on those attitudes. Simply completing this measure may have inspired further thinking and conversations about illegal drugs that then produced a change in posttest scores.

Similarly, instrumentation  can be a threat to the internal validity of studies using this design. Instrumentation refers to when the basic characteristics of the measuring instrument change over time. When human observers are used to measure behavior, they may over time gain skill, become fatigued, or change the standards on which observations are based. So participants may have taken the measure of attitudes toward illegal drugs very seriously during the pretest when it was novel but then they may have become bored with the measure at posttest and been less careful in considering their responses.

Another alternative explanation for a change in the dependent variable in a pretest-posttest design is  regression to the mean . This refers to the statistical fact that an individual who scores extremely high or extremely low on a variable on one occasion will tend to score less extremely on the next occasion. For example, a bowler with a long-term average of 150 who suddenly bowls a 220 will almost certainly score lower in the next game. Her score will “regress” toward her mean score of 150. Regression to the mean can be a problem when participants are selected for further study because  of their extreme scores. Imagine, for example, that only students who scored especially high on the test of attitudes toward illegal drugs (those with extremely favorable attitudes toward drugs) were given the anti-drug program and then were retested. Regression to the mean all but guarantees that their scores will be lower at the posttest even if the training program has no effect.

A closely related concept—and an extremely important one in psychological research—is  spontaneous remission . This is the tendency for many medical and psychological problems to improve over time without any form of treatment. The common cold is a good example. If one were to measure symptom severity in 100 common cold sufferers today, give them a bowl of chicken soup every day, and then measure their symptom severity again in a week, they would probably be much improved. This does not mean that the chicken soup was responsible for the improvement, however, because they would have been much improved without any treatment at all. The same is true of many psychological problems. A group of severely depressed people today is likely to be less depressed on average in 6 months. In reviewing the results of several studies of treatments for depression, researchers Michael Posternak and Ivan Miller found that participants in waitlist control conditions improved an average of 10 to 15% before they received any treatment at all (Posternak & Miller, 2001) [17] . Thus one must generally be very cautious about inferring causality from pretest-posttest designs.

A common approach to ruling out the threats to internal validity described above is by revisiting the research design to include a control group, one that does not receive the treatment effect. A control group would be subject to the same threats from history, maturation, testing, instrumentation, regression to the mean, and spontaneous remission and so would allow the researcher to measure the actual effect of the treatment (if any). Of course, including a control group would mean that this is no longer a one-group design.

Does Psychotherapy Work?

Early studies on the effectiveness of psychotherapy tended to use pretest-posttest designs. In a classic 1952 article, researcher Hans Eysenck summarized the results of 24 such studies showing that about two thirds of patients improved between the pretest and the posttest (Eysenck, 1952) [18] . But Eysenck also compared these results with archival data from state hospital and insurance company records showing that similar patients recovered at about the same rate  without  receiving psychotherapy. This parallel suggested to Eysenck that the improvement that patients showed in the pretest-posttest studies might be no more than spontaneous remission. Note that Eysenck did not conclude that psychotherapy was ineffective. He merely concluded that there was no evidence that it was, and he wrote of “the necessity of properly planned and executed experimental studies into this important field” (p. 323). You can read the entire article here:

http://psychclassics.yorku.ca/Eysenck/psychotherapy.htm

Fortunately, many other researchers took up Eysenck’s challenge, and by 1980 hundreds of experiments had been conducted in which participants were randomly assigned to treatment and control conditions, and the results were summarized in a classic book by Mary Lee Smith, Gene Glass, and Thomas Miller (Smith, Glass, & Miller, 1980) [19] . They found that overall psychotherapy was quite effective, with about 80% of treatment participants improving more than the average control participant. Subsequent research has focused more on the conditions under which different types of psychotherapy are more or less effective.

Interrupted Time Series Design

A variant of the pretest-posttest design is the  interrupted time-series design . A time series is a set of measurements taken at intervals over a period of time. For example, a manufacturing company might measure its workers’ productivity each week for a year. In an interrupted time series-design, a time series like this one is “interrupted” by a treatment. In one classic example, the treatment was the reduction of the work shifts in a factory from 10 hours to 8 hours (Cook & Campbell, 1979) [20] . Because productivity increased rather quickly after the shortening of the work shifts, and because it remained elevated for many months afterward, the researcher concluded that the shortening of the shifts caused the increase in productivity. Notice that the interrupted time-series design is like a pretest-posttest design in that it includes measurements of the dependent variable both before and after the treatment. It is unlike the pretest-posttest design, however, in that it includes multiple pretest and posttest measurements.

Figure 8.1  shows data from a hypothetical interrupted time-series study. The dependent variable is the number of student absences per week in a research methods course. The treatment is that the instructor begins publicly taking attendance each day so that students know that the instructor is aware of who is present and who is absent. The top panel of  Figure 8.1  shows how the data might look if this treatment worked. There is a consistently high number of absences before the treatment, and there is an immediate and sustained drop in absences after the treatment. The bottom panel of  Figure 8.1  shows how the data might look if this treatment did not work. On average, the number of absences after the treatment is about the same as the number before. This figure also illustrates an advantage of the interrupted time-series design over a simpler pretest-posttest design. If there had been only one measurement of absences before the treatment at Week 7 and one afterward at Week 8, then it would have looked as though the treatment were responsible for the reduction. The multiple measurements both before and after the treatment suggest that the reduction between Weeks 7 and 8 is nothing more than normal week-to-week variation.

In Chapter 1 we briefly described a study conducted by Simone Schnall and her colleagues, in which they found that washing one’s hands leads people to view moral transgressions as less wrong (Schnall, Benton, & Harvey, 2008) [21] . In a different but related study, Schnall and her colleagues investigated whether feeling physically disgusted causes people to make harsher moral judgments (Schnall, Haidt, Clore, & Jordan, 2008) [22] . In this experiment, they manipulated participants’ feelings of disgust by testing them in either a clean room or a messy room that contained dirty dishes, an overflowing wastebasket, and a chewed-up pen. They also used a self-report questionnaire to measure the amount of attention that people pay to their own bodily sensations. They called this “private body consciousness.” They measured their primary dependent variable, the harshness of people’s moral judgments, by describing different behaviors (e.g., eating one’s dead dog, failing to return a found wallet) and having participants rate the moral acceptability of each one on a scale of 1 to 7. Finally, the researchers asked participants to rate their current level of disgust and other emotions. The primary results of this study were that participants in the messy room were, in fact, more disgusted and made harsher moral judgments than participants in the clean room—but only if they scored relatively high in private body consciousness.

The research designs we have considered so far have been simple—focusing on a question about one variable or about a relationship between two variables. But in many ways, the complex design of this experiment undertaken by Schnall and her colleagues is more typical of research in psychology. Fortunately, we have already covered the basic elements of such designs in previous chapters. In this chapter, we look closely at how and why researchers use factorial designs, which are experiments that include more than one independent variable.

• Use frequency tables and histograms to display and interpret the distribution of a variable.
• Compute and interpret the mean, median, and mode of a distribution and identify situations in which the mean, median, or mode is the most appropriate measure of central tendency.
• Compute and interpret the range and standard deviation of a distribution.
• Compute and interpret percentile ranks and  z  scores.

Descriptive statistics  refers to a set of techniques for summarizing and displaying data. Let us assume here that the data are quantitative and consist of scores on one or more variables for each of several study participants. Although in most cases the primary research question will be about one or more statistical relationships between variables, it is also important to describe each variable individually. For this reason, we begin by looking at some of the most common techniques for describing single variables.

The Distribution of a Variable

Every variable has a  distribution , which is the way the scores are distributed across the levels of that variable. For example, in a sample of 100 university students, the distribution of the variable “number of siblings” might be such that 10 of them have no siblings, 30 have one sibling, 40 have two siblings, and so on. In the same sample, the distribution of the variable “sex” might be such that 44 have a score of “male” and 56 have a score of “female.”

Frequency Tables

One way to display the distribution of a variable is in a  frequency table . Table 12.1, for example, is a frequency table showing a hypothetical distribution of scores on the Rosenberg Self-Esteem Scale for a sample of 40 college students. The first column lists the values of the variable—the possible scores on the Rosenberg scale—and the second column lists the frequency of each score. This table shows that there were three students who had self-esteem scores of 24, five who had self-esteem scores of 23, and so on. From a frequency table like this, one can quickly see several important aspects of a distribution, including the range of scores (from 15 to 24), the most and least common scores (22 and 17, respectively), and any extreme scores that stand out from the rest.

There are a few other points worth noting about frequency tables. First, the levels listed in the first column usually go from the highest at the top to the lowest at the bottom, and they usually do not extend beyond the highest and lowest scores in the data. For example, although scores on the Rosenberg scale can vary from a high of 30 to a low of 0, Table 12.1 only includes levels from 24 to 15 because that range includes all the scores in this particular data set. Second, when there are many different scores across a wide range of values, it is often better to create a grouped frequency table, in which the first column lists ranges of values and the second column lists the frequency of scores in each range. Table 12.2, for example, is a grouped frequency table showing a hypothetical distribution of simple reaction times for a sample of 20 participants. In a grouped frequency table, the ranges must all be of equal width, and there are usually between five and 15 of them. Finally, frequency tables can also be used for categorical variables, in which case the levels are category labels. The order of the category labels is somewhat arbitrary, but they are often listed from the most frequent at the top to the least frequent at the bottom.

A  histogram  is a graphical display of a distribution. It presents the same information as a frequency table but in a way that is even quicker and easier to grasp. The histogram in Figure 12.1 presents the distribution of self-esteem scores in Table 12.1. The  x- axis of the histogram represents the variable and the  y- axis represents frequency. Above each level of the variable on the  x- axis is a vertical bar that represents the number of individuals with that score. When the variable is quantitative, as in this example, there is usually no gap between the bars. When the variable is categorical, however, there is usually a small gap between them. (The gap at 17 in this histogram reflects the fact that there were no scores of 17 in this data set.)

Distribution Shapes

When the distribution of a quantitative variable is displayed in a histogram, it has a shape. The shape of the distribution of self-esteem scores in Figure 12.1 is typical. There is a peak somewhere near the middle of the distribution and “tails” that taper in either direction from the peak. The distribution of Figure 12.1 is unimodal, meaning it has one distinct peak, but distributions can also be bimodal, meaning they have two distinct peaks. Figure 12.2, for example, shows a hypothetical bimodal distribution of scores on the Beck Depression Inventory. Distributions can also have more than two distinct peaks, but these are relatively rare in psychological research.

Another characteristic of the shape of a distribution is whether it is symmetrical or skewed. The distribution in the center of Figure 12.3 is symmetrical . Its left and right halves are mirror images of each other. The distribution on the left is negatively  skewed , with its peak shifted toward the upper end of its range and a relatively long negative tail. The distribution on the right is positively skewed, with its peak toward the lower end of its range and a relatively long positive tail.

An  outlier  is an extreme score that is much higher or lower than the rest of the scores in the distribution. Sometimes outliers represent truly extreme scores on the variable of interest. For example, on the Beck Depression Inventory, a single clinically depressed person might be an outlier in a sample of otherwise happy and high-functioning peers. However, outliers can also represent errors or misunderstandings on the part of the researcher or participant, equipment malfunctions, or similar problems. We will say more about how to interpret outliers and what to do about them later in this chapter.

Measures of Central Tendency and Variability

It is also useful to be able to describe the characteristics of a distribution more precisely. Here we look at how to do this in terms of two important characteristics: their central tendency and their variability.

Central Tendency

The  central tendency  of a distribution is its middle—the point around which the scores in the distribution tend to cluster. (Another term for central tendency is  average .) Looking back at Figure 12.1, for example, we can see that the self-esteem scores tend to cluster around the values of 20 to 22. Here we will consider the three most common measures of central tendency: the mean, the median, and the mode.

The  mean  of a distribution (symbolized  M ) is the sum of the scores divided by the number of scores. It is an average. As a formula, it looks like this:

In this formula, the symbol Σ (the Greek letter sigma) is the summation sign and means to sum across the values of the variable  X .  N  represents the number of scores. The mean is by far the most common measure of central tendency, and there are some good reasons for this. It usually provides a good indication of the central tendency of a distribution, and it is easily understood by most people. In addition, the mean has statistical properties that make it especially useful in doing inferential statistics.

An alternative to the mean is the median . The median is the middle score in the sense that half the scores in the distribution are less than it and half are greater than it. The simplest way to find the median is to organize the scores from lowest to highest and locate the score in the middle. Consider, for example, the following set of seven scores:

8 4 12 14 3 2 3

To find the median, simply rearrange the scores from lowest to highest and locate the one in the middle.

2 3 3  4  8 12 14

In this case, the median is 4 because there are three scores lower than 4 and three scores higher than 4. When there is an even number of scores, there are two scores in the middle of the distribution, in which case the median is the value halfway between them. For example, if we were to add a score of 15 to the preceding data set, there would be two scores (both 4 and 8) in the middle of the distribution, and the median would be halfway between them (6).

One final measure of central tendency is the mode. The  mode  is the most frequent score in a distribution. In the self-esteem distribution presented in Table 12.1 and Figure 12.1, for example, the mode is 22. More students had that score than any other. The mode is the only measure of central tendency that can also be used for categorical variables.

In a distribution that is both unimodal and symmetrical, the mean, median, and mode will be very close to each other at the peak of the distribution. In a bimodal or asymmetrical distribution, the mean, median, and mode can be quite different. In a bimodal distribution, the mean and median will tend to be between the peaks, while the mode will be at the tallest peak. In a skewed distribution, the mean will differ from the median in the direction of the skew (i.e., the direction of the longer tail). For highly skewed distributions, the mean can be pulled so far in the direction of the skew that it is no longer a good measure of the central tendency of that distribution. Imagine, for example, a set of four simple reaction times of 200, 250, 280, and 250 milliseconds (ms). The mean is 245 ms. But the addition of one more score of 5,000 ms—perhaps because the participant was not paying attention—would raise the mean to 1,445 ms. Not only is this measure of central tendency greater than 80% of the scores in the distribution, but it also does not seem to represent the behavior of anyone in the distribution very well. This is why researchers often prefer the median for highly skewed distributions (such as distributions of reaction times).

Keep in mind, though, that you are not required to choose a single measure of central tendency in analyzing your data. Each one provides slightly different information, and all of them can be useful.

Measures of Variability

The  variability  of a distribution is the extent to which the scores vary around their central tendency. Consider the two distributions in Figure 12.4, both of which have the same central tendency. The mean, median, and mode of each distribution are 10. Notice, however, that the two distributions differ in terms of their variability. The top one has relatively low variability, with all the scores relatively close to the center. The bottom one has relatively high variability, with the scores are spread across a much greater range.

One simple measure of variability is the  range , which is simply the difference between the highest and lowest scores in the distribution. The range of the self-esteem scores in Table 12.1, for example, is the difference between the highest score (24) and the lowest score (15). That is, the range is 24 − 15 = 9. Although the range is easy to compute and understand, it can be misleading when there are outliers. Imagine, for example, an exam on which all the students scored between 90 and 100. It has a range of 10. But if there was a single student who scored 20, the range would increase to 80—giving the impression that the scores were quite variable when in fact only one student differed substantially from the rest.

By far the most common measure of variability is the standard deviation. The standard deviation  of a distribution is the average distance between the scores and the mean. For example, the standard deviations of the distributions in Figure 12.4 are 1.69 for the top distribution and 4.30 for the bottom one. That is, while the scores in the top distribution differ from the mean by about 1.69 units on average, the scores in the bottom distribution differ from the mean by about 4.30 units on average.

Computing the standard deviation involves a slight complication. Specifically, it involves finding the difference between each score and the mean, squaring each difference, finding the mean of these squared differences, and finally finding the square root of that mean. The formula looks like this:

The computations for the standard deviation are illustrated for a small set of data in Table 12.3. The first column is a set of eight scores that has a mean of 5. The second column is the difference between each score and the mean. The third column is the square of each of these differences. Notice that although the differences can be negative, the squared differences are always positive—meaning that the standard deviation is always positive. At the bottom of the third column is the mean of the squared differences, which is also called the  variance  (symbolized  SD 2 ). Although the variance is itself a measure of variability, it generally plays a larger role in inferential statistics than in descriptive statistics. Finally, below the variance is the square root of the variance, which is the standard deviation.

N   or   N   − 1

If you have already taken a statistics course, you may have learned to divide the sum of the squared differences by  N  − 1 rather than by  N  when you compute the variance and standard deviation. Why is this?

By definition, the standard deviation is the square root of the mean of the squared differences. This implies dividing the sum of squared differences by N , as in the formula just presented. Computing the standard deviation this way is appropriate when your goal is simply to describe the variability in a sample. And learning it this way emphasizes that the variance is in fact the mean  of the squared differences—and the standard deviation is the square root of this  mean .

However, most calculators and software packages divide the sum of squared differences by  N  − 1. This is because the standard deviation of a sample tends to be a bit lower than the standard deviation of the population the sample was selected from. Dividing the sum of squares by  N  − 1 corrects for this tendency and results in a better estimate of the population standard deviation. Because researchers generally think of their data as representing a sample selected from a larger population—and because they are generally interested in drawing conclusions about the population—it makes sense to routinely apply this correction.

Percentile Ranks and  z  Scores

In many situations, it is useful to have a way to describe the location of an individual score within its distribution. One approach is the percentile rank. The percentile rank  of a score is the percentage of scores in the distribution that are lower than that score. Consider, for example, the distribution in Table 12.1. For any score in the distribution, we can find its percentile rank by counting the number of scores in the distribution that are lower than that score and converting that number to a percentage of the total number of scores. Notice, for example, that five of the students represented by the data in Table 12.1 had self-esteem scores of 23. In this distribution, 32 of the 40 scores (80%) are lower than 23. Thus each of these students has a percentile rank of 80. (It can also be said that they scored “at the 80th percentile.”) Percentile ranks are often used to report the results of standardized tests of ability or achievement. If your percentile rank on a test of verbal ability were 40, for example, this would mean that you scored higher than 40% of the people who took the test.

Another approach is the  z  score. The  z score  for a particular individual is the difference between that individual’s score and the mean of the distribution, divided by the standard deviation of the distribution:

z = ( X − M ) / SD

A  z  score indicates how far above or below the mean a raw score is, but it expresses this in terms of the standard deviation. For example, in a distribution of intelligence quotient (IQ) scores with a mean of 100 and a standard deviation of 15, an IQ score of 110 would have a  z  score of (110 − 100) / 15 = +0.67. In other words, a score of 110 is 0.67 standard deviations (approximately two thirds of a standard deviation) above the mean. Similarly, a raw score of 85 would have a  z  score of (85 − 100) / 15 = −1.00. In other words, a score of 85 is one standard deviation below the mean.

There are several reasons that  z  scores are important. Again, they provide a way of describing where an individual’s score is located within a distribution and are sometimes used to report the results of standardized tests. They also provide one way of defining outliers. For example, outliers are sometimes defined as scores that have  z  scores less than −3.00 or greater than +3.00. In other words, they are defined as scores that are more than three standard deviations from the mean. Finally,  z  scores play an important role in understanding and computing other statistics, as we will see shortly.

Online Descriptive Statistics

Although many researchers use commercially available software such as SPSS and Excel to analyze their data, there are several free online analysis tools that can also be extremely useful. Many allow you to enter or upload your data and then make one click to conduct several descriptive statistical analyses. Among them are the following.

Rice Virtual Lab in Statistics

http://onlinestatbook.com/stat_analysis/index.html

VassarStats

http://faculty.vassar.edu/lowry/VassarStats.html

Bright Stat

http://www.brightstat.com

For a more complete list, see  http://statpages.org/index.html .

This is where you can write your introduction.

Research Methods in Psychology Copyright © 2020 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, Dana C. Leighton & Molly A. Metz is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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2.3 Analyzing Findings

Learning objectives.

By the end of this section, you will be able to:

• Explain what a correlation coefficient tells us about the relationship between variables
• Recognize that correlation does not indicate a cause-and-effect relationship between variables
• Discuss our tendency to look for relationships between variables that do not really exist
• Explain random sampling and assignment of participants into experimental and control groups
• Discuss how experimenter or participant bias could affect the results of an experiment
• Identify independent and dependent variables

Did you know that as sales in ice cream increase, so does the overall rate of crime? Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone? There is no question that a relationship exists between ice cream and crime (e.g., Harper, 2013), but it would be pretty foolish to decide that one thing actually caused the other to occur.

It is much more likely that both ice cream sales and crime rates are related to the temperature outside. When the temperature is warm, there are lots of people out of their houses, interacting with each other, getting annoyed with one another, and sometimes committing crimes. Also, when it is warm outside, we are more likely to seek a cool treat like ice cream. How do we determine if there is indeed a relationship between two things? And when there is a relationship, how can we discern whether it is attributable to coincidence or causation?

Correlational Research

Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between variables. The correlation coefficient is usually represented by the letter r .

The number portion of the correlation coefficient indicates the strength of the relationship. The closer the number is to 1 (be it negative or positive), the more strongly related the variables are, and the more predictable changes in one variable will be as the other variable changes. The closer the number is to zero, the weaker the relationship, and the less predictable the relationship between the variables becomes. For instance, a correlation coefficient of 0.9 indicates a far stronger relationship than a correlation coefficient of 0.3. If the variables are not related to one another at all, the correlation coefficient is 0. The example above about ice cream and crime is an example of two variables that we might expect to have no relationship to each other.

The sign—positive or negative—of the correlation coefficient indicates the direction of the relationship ( Figure 2.12 ). A positive correlation means that the variables move in the same direction. Put another way, it means that as one variable increases so does the other, and conversely, when one variable decreases so does the other. A negative correlation means that the variables move in opposite directions. If two variables are negatively correlated, a decrease in one variable is associated with an increase in the other and vice versa.

The example of ice cream and crime rates is a positive correlation because both variables increase when temperatures are warmer. Other examples of positive correlations are the relationship between an individual’s height and weight or the relationship between a person’s age and number of wrinkles. One might expect a negative correlation to exist between someone’s tiredness during the day and the number of hours they slept the previous night: the amount of sleep decreases as the feelings of tiredness increase. In a real-world example of negative correlation, student researchers at the University of Minnesota found a weak negative correlation ( r = -0.29) between the average number of days per week that students got fewer than 5 hours of sleep and their GPA (Lowry, Dean, & Manders, 2010). Keep in mind that a negative correlation is not the same as no correlation. For example, we would probably find no correlation between hours of sleep and shoe size.

As mentioned earlier, correlations have predictive value. Imagine that you are on the admissions committee of a major university. You are faced with a huge number of applications, but you are able to accommodate only a small percentage of the applicant pool. How might you decide who should be admitted? You might try to correlate your current students’ college GPA with their scores on standardized tests like the SAT or ACT. By observing which correlations were strongest for your current students, you could use this information to predict relative success of those students who have applied for admission into the university.

Link to Learning

Manipulate this interactive scatterplot to practice your understanding of positive and negative correlation.

Correlation Does Not Indicate Causation

Correlational research is useful because it allows us to discover the strength and direction of relationships that exist between two variables. However, correlation is limited because establishing the existence of a relationship tells us little about cause and effect . While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable , is actually causing the systematic movement in our variables of interest. In the ice cream/crime rate example mentioned earlier, temperature is a confounding variable that could account for the relationship between the two variables.

Even when we cannot point to clear confounding variables, we should not assume that a correlation between two variables implies that one variable causes changes in another. This can be frustrating when a cause-and-effect relationship seems clear and intuitive. Think back to our discussion of the research done by the American Cancer Society and how their research projects were some of the first demonstrations of the link between smoking and cancer. It seems reasonable to assume that smoking causes cancer, but if we were limited to correlational research , we would be overstepping our bounds by making this assumption.

Unfortunately, people mistakenly make claims of causation as a function of correlations all the time. Such claims are especially common in advertisements and news stories. For example, research found that people who eat certain breakfast cereal may have a reduced risk of heart disease (Anderson, Hanna, Peng, & Kryscio, 2000). Cereal companies are likely to share this information in a way that maximizes and perhaps overstates the positive aspects of eating cereal. But does cereal really cause better health, or are there other possible explanations for the health of those who eat cereal? While correlational research is invaluable in identifying relationships among variables, a major limitation is the inability to establish causality. Psychologists want to make statements about cause and effect, but the only way to do that is to conduct an experiment to answer a research question. The next section describes how scientific experiments incorporate methods that eliminate, or control for, alternative explanations, which allow researchers to explore how changes in one variable cause changes in another variable.

Illusory Correlations

The temptation to make erroneous cause-and-effect statements based on correlational research is not the only way we tend to misinterpret data. We also tend to make the mistake of illusory correlations, especially with unsystematic observations. Illusory correlations , or false correlations, occur when people believe that relationships exist between two things when no such relationship exists. One well-known illusory correlation is the supposed effect that the moon’s phases have on human behavior. Many people passionately assert that human behavior is affected by the phase of the moon, and specifically, that people act strangely when the moon is full ( Figure 2.14 ).

There is no denying that the moon exerts a powerful influence on our planet. The ebb and flow of the ocean’s tides are tightly tied to the gravitational forces of the moon. Many people believe, therefore, that it is logical that we are affected by the moon as well. After all, our bodies are largely made up of water. A meta-analysis of nearly 40 studies consistently demonstrated, however, that the relationship between the moon and our behavior does not exist (Rotton & Kelly, 1985). While we may pay more attention to odd behavior during the full phase of the moon, the rates of odd behavior remain constant throughout the lunar cycle.

Why are we so apt to believe in illusory correlations like this? Often we read or hear about them and simply accept the information as valid. Or, we have a hunch about how something works and then look for evidence to support that hunch, ignoring evidence that would tell us our hunch is false; this is known as confirmation bias . Other times, we find illusory correlations based on the information that comes most easily to mind, even if that information is severely limited. And while we may feel confident that we can use these relationships to better understand and predict the world around us, illusory correlations can have significant drawbacks. For example, research suggests that illusory correlations—in which certain behaviors are inaccurately attributed to certain groups—are involved in the formation of prejudicial attitudes that can ultimately lead to discriminatory behavior (Fiedler, 2004).

Causality: Conducting Experiments and Using the Data

As you’ve learned, the only way to establish that there is a cause-and-effect relationship between two variables is to conduct a scientific experiment . Experiment has a different meaning in the scientific context than in everyday life. In everyday conversation, we often use it to describe trying something for the first time, such as experimenting with a new hair style or a new food. However, in the scientific context, an experiment has precise requirements for design and implementation.

The Experimental Hypothesis

In order to conduct an experiment, a researcher must have a specific hypothesis to be tested. As you’ve learned, hypotheses can be formulated either through direct observation of the real world or after careful review of previous research. For example, if you think that the use of technology in the classroom has negative impacts on learning, then you have basically formulated a hypothesis—namely, that the use of technology in the classroom should be limited because it decreases learning. How might you have arrived at this particular hypothesis? You may have noticed that your classmates who take notes on their laptops perform at lower levels on class exams than those who take notes by hand, or those who receive a lesson via a computer program versus via an in-person teacher have different levels of performance when tested ( Figure 2.15 ).

These sorts of personal observations are what often lead us to formulate a specific hypothesis, but we cannot use limited personal observations and anecdotal evidence to rigorously test our hypothesis. Instead, to find out if real-world data supports our hypothesis, we have to conduct an experiment.

Designing an Experiment

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested (in this case, the use of technology)—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.

In our example of how the use of technology should be limited in the classroom, we have the experimental group learn algebra using a computer program and then test their learning. We measure the learning in our control group after they are taught algebra by a teacher in a traditional classroom. It is important for the control group to be treated similarly to the experimental group, with the exception that the control group does not receive the experimental manipulation.

We also need to precisely define, or operationalize, how we measure learning of algebra. An operational definition is a precise description of our variables, and it is important in allowing others to understand exactly how and what a researcher measures in a particular experiment. In operationalizing learning, we might choose to look at performance on a test covering the material on which the individuals were taught by the teacher or the computer program. We might also ask our participants to summarize the information that was just presented in some way. Whatever we determine, it is important that we operationalize learning in such a way that anyone who hears about our study for the first time knows exactly what we mean by learning. This aids peoples’ ability to interpret our data as well as their capacity to repeat our experiment should they choose to do so.

Once we have operationalized what is considered use of technology and what is considered learning in our experiment participants, we need to establish how we will run our experiment. In this case, we might have participants spend 45 minutes learning algebra (either through a computer program or with an in-person math teacher) and then give them a test on the material covered during the 45 minutes.

Ideally, the people who score the tests are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which child was in which group, it might influence how they interpret ambiguous responses, such as sloppy handwriting or minor computational mistakes. By being blind to which child is in which group, we protect against those biases. This situation is a single-blind study , meaning that one of the groups (participants) are unaware as to which group they are in (experiment or control group) while the researcher who developed the experiment knows which participants are in each group.

In a double-blind study , both the researchers and the participants are blind to group assignments. Why would a researcher want to run a study where no one knows who is in which group? Because by doing so, we can control for both experimenter and participant expectations. If you are familiar with the phrase placebo effect , you already have some idea as to why this is an important consideration. The placebo effect occurs when people's expectations or beliefs influence or determine their experience in a given situation. In other words, simply expecting something to happen can actually make it happen.

The placebo effect is commonly described in terms of testing the effectiveness of a new medication. Imagine that you work in a pharmaceutical company, and you think you have a new drug that is effective in treating depression. To demonstrate that your medication is effective, you run an experiment with two groups: The experimental group receives the medication, and the control group does not. But you don’t want participants to know whether they received the drug or not.

Why is that? Imagine that you are a participant in this study, and you have just taken a pill that you think will improve your mood. Because you expect the pill to have an effect, you might feel better simply because you took the pill and not because of any drug actually contained in the pill—this is the placebo effect.

To make sure that any effects on mood are due to the drug and not due to expectations, the control group receives a placebo (in this case a sugar pill). Now everyone gets a pill, and once again neither the researcher nor the experimental participants know who got the drug and who got the sugar pill. Any differences in mood between the experimental and control groups can now be attributed to the drug itself rather than to experimenter bias or participant expectations ( Figure 2.16 ).

Independent and Dependent Variables

In a research experiment, we strive to study whether changes in one thing cause changes in another. To achieve this, we must pay attention to two important variables, or things that can be changed, in any experimental study: the independent variable and the dependent variable. An independent variable is manipulated or controlled by the experimenter. In a well-designed experimental study, the independent variable is the only important difference between the experimental and control groups. In our example of how technology use in the classroom affects learning, the independent variable is the type of learning by participants in the study ( Figure 2.17 ). A dependent variable is what the researcher measures to see how much effect the independent variable had. In our example, the dependent variable is the learning exhibited by our participants.

We expect that the dependent variable will change as a function of the independent variable. In other words, the dependent variable depends on the independent variable. A good way to think about the relationship between the independent and dependent variables is with this question: What effect does the independent variable have on the dependent variable? Returning to our example, what is the effect of being taught a lesson through a computer program versus through an in-person instructor?

Selecting and Assigning Experimental Participants

Now that our study is designed, we need to obtain a sample of individuals to include in our experiment. Our study involves human participants so we need to determine whom to include. Participants are the subjects of psychological research, and as the name implies, individuals who are involved in psychological research actively participate in the process. Often, psychological research projects rely on college students to serve as participants. In fact, the vast majority of research in psychology subfields has historically involved students as research participants (Sears, 1986; Arnett, 2008). But are college students truly representative of the general population? College students tend to be younger, more educated, more liberal, and less diverse than the general population. Although using students as test subjects is an accepted practice, relying on such a limited pool of research participants can be problematic because it is difficult to generalize findings to the larger population.

Our hypothetical experiment involves high school students, and we must first generate a sample of students. Samples are used because populations are usually too large to reasonably involve every member in our particular experiment ( Figure 2.18 ). If possible, we should use a random sample (there are other types of samples, but for the purposes of this chapter, we will focus on random samples). A random sample is a subset of a larger population in which every member of the population has an equal chance of being selected. Random samples are preferred because if the sample is large enough we can be reasonably sure that the participating individuals are representative of the larger population. This means that the percentages of characteristics in the sample—sex, ethnicity, socioeconomic level, and any other characteristics that might affect the results—are close to those percentages in the larger population.

In our example, let’s say we decide our population of interest is algebra students. But all algebra students is a very large population, so we need to be more specific; instead we might say our population of interest is all algebra students in a particular city. We should include students from various income brackets, family situations, races, ethnicities, religions, and geographic areas of town. With this more manageable population, we can work with the local schools in selecting a random sample of around 200 algebra students who we want to participate in our experiment.

In summary, because we cannot test all of the algebra students in a city, we want to find a group of about 200 that reflects the composition of that city. With a representative group, we can generalize our findings to the larger population without fear of our sample being biased in some way.

Now that we have a sample, the next step of the experimental process is to split the participants into experimental and control groups through random assignment. With random assignment , all participants have an equal chance of being assigned to either group. There is statistical software that will randomly assign each of the algebra students in the sample to either the experimental or the control group.

Random assignment is critical for sound experimental design . With sufficiently large samples, random assignment makes it unlikely that there are systematic differences between the groups. So, for instance, it would be very unlikely that we would get one group composed entirely of males, a given ethnic identity, or a given religious ideology. This is important because if the groups were systematically different before the experiment began, we would not know the origin of any differences we find between the groups: Were the differences preexisting, or were they caused by manipulation of the independent variable? Random assignment allows us to assume that any differences observed between experimental and control groups result from the manipulation of the independent variable.

Use this online random number generator to learn more about random sampling and assignments.

Issues to Consider

While experiments allow scientists to make cause-and-effect claims, they are not without problems. True experiments require the experimenter to manipulate an independent variable, and that can complicate many questions that psychologists might want to address. For instance, imagine that you want to know what effect sex (the independent variable) has on spatial memory (the dependent variable). Although you can certainly look for differences between males and females on a task that taps into spatial memory, you cannot directly control a person’s sex. We categorize this type of research approach as quasi-experimental and recognize that we cannot make cause-and-effect claims in these circumstances.

Experimenters are also limited by ethical constraints. For instance, you would not be able to conduct an experiment designed to determine if experiencing abuse as a child leads to lower levels of self-esteem among adults. To conduct such an experiment, you would need to randomly assign some experimental participants to a group that receives abuse, and that experiment would be unethical.

Interpreting Experimental Findings

Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely it is that any difference found is due to chance (and thus not meaningful). For example, if an experiment is done on the effectiveness of a nutritional supplement, and those taking a placebo pill (and not the supplement) have the same result as those taking the supplement, then the experiment has shown that the nutritional supplement is not effective. Generally, psychologists consider differences to be statistically significant if there is less than a five percent chance of observing them if the groups did not actually differ from one another. Stated another way, psychologists want to limit the chances of making “false positive” claims to five percent or less.

The greatest strength of experiments is the ability to assert that any significant differences in the findings are caused by the independent variable. This occurs because random selection, random assignment, and a design that limits the effects of both experimenter bias and participant expectancy should create groups that are similar in composition and treatment. Therefore, any difference between the groups is attributable to the independent variable, and now we can finally make a causal statement. If we find that watching a violent television program results in more violent behavior than watching a nonviolent program, we can safely say that watching violent television programs causes an increase in the display of violent behavior.

Reporting Research

When psychologists complete a research project, they generally want to share their findings with other scientists. The American Psychological Association (APA) publishes a manual detailing how to write a paper for submission to scientific journals. Unlike an article that might be published in a magazine like Psychology Today, which targets a general audience with an interest in psychology, scientific journals generally publish peer-reviewed journal articles aimed at an audience of professionals and scholars who are actively involved in research themselves.

The Online Writing Lab (OWL) at Purdue University can walk you through the APA writing guidelines.

A peer-reviewed journal article is read by several other scientists (generally anonymously) with expertise in the subject matter. These peer reviewers provide feedback—to both the author and the journal editor—regarding the quality of the draft. Peer reviewers look for a strong rationale for the research being described, a clear description of how the research was conducted, and evidence that the research was conducted in an ethical manner. They also look for flaws in the study's design, methods, and statistical analyses. They check that the conclusions drawn by the authors seem reasonable given the observations made during the research. Peer reviewers also comment on how valuable the research is in advancing the discipline’s knowledge. This helps prevent unnecessary duplication of research findings in the scientific literature and, to some extent, ensures that each research article provides new information. Ultimately, the journal editor will compile all of the peer reviewer feedback and determine whether the article will be published in its current state (a rare occurrence), published with revisions, or not accepted for publication.

Peer review provides some degree of quality control for psychological research. Poorly conceived or executed studies can be weeded out, and even well-designed research can be improved by the revisions suggested. Peer review also ensures that the research is described clearly enough to allow other scientists to replicate it, meaning they can repeat the experiment using different samples to determine reliability. Sometimes replications involve additional measures that expand on the original finding. In any case, each replication serves to provide more evidence to support the original research findings. Successful replications of published research make scientists more apt to adopt those findings, while repeated failures tend to cast doubt on the legitimacy of the original article and lead scientists to look elsewhere. For example, it would be a major advancement in the medical field if a published study indicated that taking a new drug helped individuals achieve better health without changing their behavior. But if other scientists could not replicate the results, the original study’s claims would be questioned.

In recent years, there has been increasing concern about a “replication crisis” that has affected a number of scientific fields, including psychology. Some of the most well-known studies and scientists have produced research that has failed to be replicated by others (as discussed in Shrout & Rodgers, 2018). In fact, even a famous Nobel Prize-winning scientist has recently retracted a published paper because she had difficulty replicating her results (Nobel Prize-winning scientist Frances Arnold retracts paper, 2020 January 3). These kinds of outcomes have prompted some scientists to begin to work together and more openly, and some would argue that the current “crisis” is actually improving the ways in which science is conducted and in how its results are shared with others (Aschwanden, 2018).

The Vaccine-Autism Myth and Retraction of Published Studies

Some scientists have claimed that routine childhood vaccines cause some children to develop autism, and, in fact, several peer-reviewed publications published research making these claims. Since the initial reports, large-scale epidemiological research has indicated that vaccinations are not responsible for causing autism and that it is much safer to have your child vaccinated than not. Furthermore, several of the original studies making this claim have since been retracted.

A published piece of work can be rescinded when data is called into question because of falsification, fabrication, or serious research design problems. Once rescinded, the scientific community is informed that there are serious problems with the original publication. Retractions can be initiated by the researcher who led the study, by research collaborators, by the institution that employed the researcher, or by the editorial board of the journal in which the article was originally published. In the vaccine-autism case, the retraction was made because of a significant conflict of interest in which the leading researcher had a financial interest in establishing a link between childhood vaccines and autism (Offit, 2008). Unfortunately, the initial studies received so much media attention that many parents around the world became hesitant to have their children vaccinated ( Figure 2.19 ). Continued reliance on such debunked studies has significant consequences. For instance, between January and October of 2019, there were 22 measles outbreaks across the United States and more than a thousand cases of individuals contracting measles (Patel et al., 2019). This is likely due to the anti-vaccination movements that have risen from the debunked research. For more information about how the vaccine/autism story unfolded, as well as the repercussions of this story, take a look at Paul Offit’s book, Autism’s False Prophets: Bad Science, Risky Medicine, and the Search for a Cure.

Reliability and Validity

Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways. There are a number of different types of reliability. Some of these include inter-rater reliability (the degree to which two or more different observers agree on what has been observed), internal consistency (the degree to which different items on a survey that measure the same thing correlate with one another), and test-retest reliability (the degree to which the outcomes of a particular measure remain consistent over multiple administrations).

Unfortunately, being consistent in measurement does not necessarily mean that you have measured something correctly. To illustrate this concept, consider a kitchen scale that would be used to measure the weight of cereal that you eat in the morning. If the scale is not properly calibrated, it may consistently under- or overestimate the amount of cereal that’s being measured. While the scale is highly reliable in producing consistent results (e.g., the same amount of cereal poured onto the scale produces the same reading each time), those results are incorrect. This is where validity comes into play. Validity refers to the extent to which a given instrument or tool accurately measures what it’s supposed to measure, and once again, there are a number of ways in which validity can be expressed. Ecological validity (the degree to which research results generalize to real-world applications), construct validity (the degree to which a given variable actually captures or measures what it is intended to measure), and face validity (the degree to which a given variable seems valid on the surface) are just a few types that researchers consider. While any valid measure is by necessity reliable, the reverse is not necessarily true. Researchers strive to use instruments that are both highly reliable and valid.

Everyday Connection

How valid are the sat and act.

Standardized tests like the SAT and ACT are supposed to measure an individual’s aptitude for a college education, but how reliable and valid are such tests? Research conducted by the College Board suggests that scores on the SAT have high predictive validity for first-year college students’ GPA (Kobrin, Patterson, Shaw, Mattern, & Barbuti, 2008). In this context, predictive validity refers to the test’s ability to effectively predict the GPA of college freshmen. Given that many institutions of higher education require the SAT or ACT for admission, this high degree of predictive validity might be comforting.

However, the emphasis placed on SAT or ACT scores in college admissions is changing based on a number of factors. For one, some researchers assert that these tests are biased, and students from historically marginalized populations are at a disadvantage that unfairly reduces the likelihood of being admitted into a college (Santelices & Wilson, 2010). Additionally, some research has suggested that the predictive validity of these tests is grossly exaggerated in how well they are able to predict the GPA of first-year college students. In fact, it has been suggested that the SAT’s predictive validity may be overestimated by as much as 150% (Rothstein, 2004). Many institutions of higher education are beginning to consider de-emphasizing the significance of SAT scores in making admission decisions (Rimer, 2008).

Recent examples of high profile cheating scandals both domestically and abroad have only increased the scrutiny being placed on these types of tests, and as of March 2019, more than 1000 institutions of higher education have either relaxed or eliminated the requirements for SAT or ACT testing for admissions (Strauss, 2019, March 19).

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Psychology Dissertation Topics

Published by Carmen Troy at January 10th, 2023 , Revised On May 2, 2024

Introduction

Psychology entails the study of mental processes and behaviour. Over the last several years, the demand for psychology graduates has continuously risen due to the growing number of people with psychic problems.

As a psychology student, you can explore one of the many areas of psychology as part of your dissertation project. You can specialise in industrial physiology, mental health, behavioural psychology, cognitive psychology, developmental psychology, personality psychology, social psychology, biological psychology, and psychosocial psychology.

While there are many topic options for psychology students, make sure that you choose one where there is a gap in the literature and more work needs to be done.

To help you get started with brainstorming for psychology topic ideas, we have developed a list of the latest topics that can be used for writing your psychology dissertation.

You may also want to start your dissertation by requesting  a brief research proposal  from our writers on any of these topics, which includes an  introduction  to the topic,  research question ,  aim and objectives ,  literature review  along with the proposed  methodology  of research to be conducted.  Let us know  if you need any help in getting started.

Check our  dissertation examples  to get an idea of  how to structure your dissertation .

Review the full list of  dissertation topics here.

Psychology Research Topics

Impact of automation in the manufacturing sector on employee distress and happiness in the uk- an exploratory study finding the psychoeconomic factors.

Research Aim: This study intends to find the impact of automation in the manufacturing sector on employee distress and happiness in the UK. It will explore the moderating Psychoeconomic (Psychological and Economic) factors affected by the increasing automation in the manufacturing industry, affecting the employees’ distress and happiness levels. Furthermore, it will examine the strategies implemented by the manufacturing companies to prevent their employees from the anxiety and unhappiness induced by automation after the technological revolution.

Impact of Sleep Deprivation on Cognitive Performance Among Adults Working from Home during COVID-19 in the UK

Research Aim: This research aims to analyse the impact of sleep deprivation on cognitive performance among adults working from home during COVID-19 in the UK. It will identify how sleep deprivation during COVID-19 affected various mental abilities of employees who were forced to work from home. It will also see how these abilities affect the employees’ productivity during COVID-19. Additionally, it will show the policies adopted by the companies to prevent their employees from working overtime to have proper sleep. And how does it improve their cognitive performance and productivity?

Effects of Bad Incidents on Children’s Intelligence- A Critical Assessment through a Clinical Psychology Lens

Research Aim: This research shows the effects of bad incidents on children’s intelligence. It will use a clinical psychology lens to show how clinicians see the relationship between bad incidents in childhood and their impact on children’s intelligence in later life. And in which was these incidents shape the intelligence of children while growing up. Furthermore, it will present a wide range of clinical procedures to overcome the lingering effects of bad incidents on children’s intelligence in later life.

Impact of Marriage Satisfaction on Job Performance in High-Stress Jobs- A Case of Individuals Working in Investment Firms in the UK

Research Aim: This research analyses the impact of marriage satisfaction on job performance in high-stress jobs. It will use investment firms in the UK as a case study to analyse how marriage satisfaction affects the performance of men and women working in high-stress jobs such as trading and investments. Moreover, it will explore various psychological parts of the job affected by the problems in a marriage. Lastly, it will recommend ways to offset the bad effects of unstable marriage to improve job performance.

The Role of Educational-Psychological Counseling in Career Selection among Immigrant Children in the UK

Research Aim: This research investigates the role of educational-psychological counselling in career selection among immigrant children in the UK. It will show how educational-psychological counselling different aspects of their academic life and help them decide what to pursue in later life. It will also show how this counselling can help them believe that despite coming from outside of the UK, they still have a chance to succeed.

The Effectiveness of Mindfulness-Based Interventions (MBIs) on Reducing Symptoms of Anxiety and Depression

Research Aim: This study investigates the effectiveness of Mindfulness-Based Interventions (MBIs) in reducing symptoms of anxiety and depression, It focuses on exploring the comparative efficacy of different types of MBIs and their potential mechanisms of action.

Investigate the impact of introducing mindfulness programs in school curricula to enhance mental well-being among adolescents.

Research Aim: This study aims to examine the impact of integrating mindfulness programs into school curricula to enhance mental well-being among adolescents. Through empirical investigation, it seeks to assess the effectiveness of mindfulness interventions in reducing stress, anxiety, and depression levels, as well as promoting overall psychological resilience and positive emotional regulation. Additionally, the research aims to explore potential factors influencing program efficacy.

Exploring the Link Between Mood and Innovation in Entrepreneurship

Research Aim: This research investigates the intricate relationship between mood and innovation within the context of entrepreneurship. By exploring how varying moods influence the generation, adoption, and implementation of innovative ideas by entrepreneurs, the study seeks to uncover potential patterns and mechanisms that drive entrepreneurial creativity. The research further explores how understanding this link is crucial for informing strategies to foster innovation within entrepreneurial ventures.

An Examination of the Interplay Between Depression and Creative Writing: Case Studies in Literature

Research Aim: This research examines the complex interplay between depression and creative writing through case studies in literature. It focuses on the experiences of writers who have battled depression and analysing how their mental health condition intersects with their creative process and output, this study seeks to shed light on the relationship between mood disorders and literary creativity. 

Investigating the neurobiological basis of ADHD: brain structure, neurotransmitter function, and genetics.

Research Aim: The study explores the interplay between brain structure, neurotransmitter function, and genetic factors in individuals with ADHD. It focuses on elucidating the neurobiological mechanisms underlying the disorder.

Examine the relationship between ADHD and comorbid mental health conditions, such as anxiety, depression, and substance abuse.

Research Aim: This study explores the complex relationship between ADHD and comorbid mental health conditions, including anxiety, depression, and substance abuse. It discusses the underlying mechanisms, common risk factors, and potential therapeutic implications for effective management and treatment strategies.

Covid-19 Psychology Research Topics

Topic 1: impacts of coronavirus on the mental health of various age groups.

Research Aim: This study will reveal the impacts of coronavirus on the mental health of various age groups

Topic 2: Mental health and psychological resilience during COVID-19

Research Aim: Social distancing has made people isolated and affected their mental health. This study will highlight various measures to overcome the stress and mental health of people during coronavirus.

Topic 3: The mental health of children and families during COVID-19

Research Aim: This study will address the challenging situations faced by children and families during lockdown due to COVID-19. It will also discuss various ways to overcome the fear of disease and stay positive.

Topic 4: Mental wellbeing of patients during Coronavirus pandemic

Research Aim: This study will focus on the measures taken by the hospital management, government, and families, to ensure the mental wellbeing of patients, especially COVID-19 patients.

Psychology Dissertation Topics in Social Sciences

Topic 1: kids and their relatives with cancer: psychological challenges.

Research Aim: In cancer diagnoses and therapies, children often don’t know what happens. Many have psychosocial problems, including rage, terror, depression, disturbing sleep, inexpiable guilt, and panic. Therefore, this study identifies and treats the child and its family members’ psychological issues.

Topic 2: Hematopoietic device reaction in ophthalmology patient’s radiation therapy

Research Aim: This research is based on the analysis of hematopoietic devices’ reactions to ophthalmology radiation.

Topic 3: Psychological effects of cyberbullying Vs. physical bullying: A counter study

Research Aim: This research will focus on the effects of cyberbullying and physical bullying and their consequences on the victim’s mental health. The most significant part is the counter effects on our society’s environment and human behaviour, particularly youth.

Topic 4: Whether or not predictive processing is a theory of perceptual consciousness?

Research Aim: This research aims to identify whether predictive processing is a theory of perceptual consciousness or not.

Topic 5: Importance of communication in a relationship

Research Aim: This research aims to address the importance of communication in relationships and the communication gap consequences.

Topic 6: Eating and personality disorders

Research Aim: This research aims to focus on eating and personality disorders

Topic 7: Analysis of teaching, assessment, and evaluation of students and learning differences

Research Aim: This research aims to analyse teaching methods, assessment, and evaluation systems of students and their learning differences

Topic 8: Social and psychological effects of virtual networks

Research Aim: This research aims to study the social and psychological effects of virtual networks

Topic 9: The role of media in provoking aggression

Research Aim: This research aims to address the role of media in provoking aggression among people

Psychology Dissertation Topics Behavioral Sciences

Topic 1: assessing the advantages and disadvantages of positive reinforcement in special education.

Research Aim: The strength and importance of praise in the workplace can have a significant impact on employees and move them from apathy to more happiness and satisfaction. Positive reinforcement motivates and encourages people for their respective tasks. This research aims to assess the advantages and disadvantages of positive reinforcement in special education.

Topic 2: Assessing the relationship between depression and anxiety from the perspective of student academic performance

Research Aim: Emotional disturbance is considered to be a psychological element that can lead to the deterioration of the daily activities of students. Since academic achievements are an integral dimension of students’ lives, depression, anxiety, and other emotional disturbance might lead to poor academic performance. Therefore, this research aims to assess the relationship between depression and anxiety on student academic performance.

Topic 3: How cognitive behaviour therapy helps in dealing with depressed adolescents

Research Aim: Cognitive behavioural theory is regarded as a well-established therapy for depression and other various mental illnesses in children and adolescents. It might be because CBT can reduce suicidal behaviour and thoughts among adolescents. The main purpose of this research is to identify how cognitive behaviour therapy can help in dealing with depressed adolescents.

Topic 4: Analysing the psychological impact of bullying on children’s personality and development

Research Aim: Any public humiliation can result in a child’s misconceptions, confusion and misunderstanding about their own personality and the surrounding world. Public humiliation can damage the psychology of children and hinder their overall physical and mental development. The key purpose of this study is to analyse the psychological impact of bullying on children’s personalities and development.

Topic 5: Assessing the impact of psychological pricing on consumer purchase intention

Research Aim: Psychological pricing, also known as charm pricing and price ending, is a market pricing strategy in which certain prices can have a psychological impact on consumers. This strategy also includes a slightly less than round number, e.g. 2.99, which could incline consumers to make purchase decisions in favour of the seller. Hence, this research aims to assess the impact of psychological pricing on consumer purchase intention.

Topic 6: Borderline Personality Disorder and Self-Cutting Behaviors – Are they Inter Related?

Research Aim: Borderline Personality Disorder is a mental health disorder that impacts the thinking process of an individual. This disorder impacts the way you think and feel about yourself and others. Relationships are unstable. There are extreme emotions and distorted self-image when a person is suffering from a borderline personality disorder. This research will discuss this disorder in detail and evaluate whether self-cutting behaviours are a result of this disorder or not.

Topic 7: Depression and its risk factors – How can it be prevented?

Research Aim: Depression is a psychological issue that needs immediate attention. There are a lot of factors that lead to depression. This research will talk about the various risk factors that contribute to depression in an individual. The research will also discuss ways and strategies through which depression can be managed and eliminated in some cases. Case studies will be a part of this research.

Topic 8: Childhood trauma and its long-lasting impacts on individuals in adulthood

Research Aim: This research will talk about an important issue i.e. childhood trauma. This includes emotional and physical trauma that a child had experienced in his childhood. This research will discuss whether this trauma will impact the individual further in his life or not. If an adult’s future life is likely to be affected by childhood trauma, then in what ways will it change the individual, and how will it shape his personality? All these questions will be answered with this research.

Organisational Psychology Dissertation Topics

The role of industrial psychologists, also known as organisational psychologists, is to apply the principles of psychology to marketing, sales, management, administration, and human resources problems that organisations face.

Typical tasks that organisational psychologists perform include but are not limited to organisational development and analysis, training and development, employee evaluation and selection, policymaking, and more. The following dissertation topics are developed with respect to organisational psychology:

Topic 1: Research in industrial and organisational psychology from 1980 to 2015: Changes, choices, and trends

Research Aim: This research will compare the choices, trends, and changes in industrial and organisational psychology. The years compared will be 1990-2000, 2001-2010, and 2011-2020.

Topic 2: Computerized adaptive testing in industrial and organisational psychology

Research Aim: This research will explore advanced techniques, i.e., computerised adaptive testing, in organisational and industrial psychology.

Topic 3: Leader-member exchange as a moderating variable in the relationship between well-being and job security

Research Aim: This research will analyse the leader-member exchange as a variable that moderates the relationship between job security and well-being.

Topic 4: Intelligent leadership and leadership competencies – Developing a leadership framework for intelligent organizations

Research Aim: This research will understand leadership competencies and intelligent leadership by analysing a leadership framework for intelligent organisations.

Topic 5: Burnout amongst executive staff: What are the main predictors? A review of literature from the UK and Europe.

Research Aim: This research will talk about the most pressing issue at workplaces right now, i.e. burnout, The study will include predictors of burnout by analysing literature from Europe and the UK.

Topic 6: Interior design and Industrial psychology – Investigating the role of employees' reward and motivation in shaping up the look of the factory or office

Research Aim: This research will understand the role of employee reward and motivation in shaping workplaces with a focus on how interior design can create a working environment for employees that enhances their motivation levels.

Topic 7: Investigating the impact of strategic business partnering for business organisations – A case study of any UK based company

Research Aim: This research will talk about the impact of strategic business partnering for business organisations. You can provide us with the name of the company you would want to base your research on.

Topic 8: Social science strategies for managing diversity: Industrial and organisational opportunities to enhance inclusion

Research Aim: This research will interrogate an extremely important issue of psychology, i.e., diversity and inclusion in the workplace. The study will be conducted with respect to social science strategies.

Topic 9: Studying Influencing Factors in Effective Training Programs in Organisations

Research Aim: This research will talk about the various psychological factors that influence training programs organised by companies.

Topic 10: To understand international branding in light of the concept of Hofstede’s cultural dimensions

Research Aim: This research will aim to understand international branding in light of the concept of Hofstede’s cultural dimensions. The research will be descriptive in nature and make use of secondary data.

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Clinical Psychology Dissertation Topics

Clinical psychology can be defined as integrating clinical knowledge, theory, and science to understand and prevent psychologically based dysfunction and distress. Another aim of this branch of psychology is to promote personal development and behavioural well-being.

Clinical psychologists’ job responsibilities include conducting research, teaching, drug and alcohol treatment, assessing disorders, testifying in legal settings, and creating and managing programs to prevent and treat social problems.

A well-written dissertation in this area of psychology can help students to fetch a high academic grade. Here are some interesting topics in this area:

Topic 1: Which clinical and demographic factors predict poor insight in individuals with obsessions and compulsions?

Research Aim: This research will discuss the clinical and demographic factors that predict poor insight within individuals with compulsions and obsessions.

Topic 2: Anger beliefs and behaviour; An Investigation of associations with Hypomania in a non-clinical sample

Research Aim: This research will investigate anger, behaviour, and beliefs concerning hypomania in a non-clinical sample.

Topic 3: Clinical psychologists’ experiences of accessing personal therapy during training: A narrative analysis

Research Aim: This research will discuss clinical psychologists’ experiences of accessing personal therapy during training. This will be a narrative analysis.

Topic 4: Exploring body image and identity in people who have had a heart or lung transplant

Research Aim: This research will help explore the identity and body image of people who have had a heart or lung transplant. All related issues will be discussed in this study.

Topic 5: Psychosocial adjustment to renal failure and consequent dialysis

Research Aim: This research will explore the psychosocial adjustment required during renal failure. The study will also discuss dialysis, which will result in renal failure.

Topic 6: Experiences of psychosocial formulation within a biopsychosocial model of care for psychosis

Research Aim: This research will talk about psychosocial formulation experiences within a biopsychosocial model of care for psychosis.

Topic 7: Experiences and their association with eating behaviour in adulthood

Research Aim: This research will investigate the relationship between individual experiences and eating behaviour in adulthood. The study will furthermore present suggestions as to how these conditions can be improved.

Topic 8: Barriers to communicating about sexual dysfunction following heart trauma

Research Aim: This research will talk about an important issue i.e. sexual dysfunction. However, the study will be conducted concerning the issue being developed due to heart trauma.

Topic 9: Validation of a new scale assessing the use of strategies to change another person’s mood or emotional state

Research Aim: This research will investigate and try to validate a new scale that will be used to assess strategies for changing another person’s emotional state or mood.

Topic 10: Examining Major Depressive Disorder (MDD) within a cognitive framework

Research Aim: This research will investigate an important psychological issue, i.e. depression. Major Depressive Disorder (MDD) will be assessed with a cognitive framework.

Also Read: Construction Engineering Dissertation Topics

Cognitive Psychology Dissertation Topics

Cognitive Psychology can be defined as the study of mental processes such as thinking, creativity, problem solving, perception, memory, language use, and attention through neuropsychology, computer modeling, and experimentation.

Cognitive psychologists are primarily responsible for investigating how the human brain absorbs and interprets information at micro and macro levels. This area of psychology is broad. Therefore you will have many topic options to choose from. Please see below some titles if you are looking to base your dissertation on the field of cognitive psychology.

Topic 1: Adolescent perceptions and beliefs of proactive-reactive aggression explored through the social information processing model of aggression

Research Aim: This research will talk about various perceptions and beliefs of adolescents with respect to proactive-reactive aggression. These will be explored through the social information processing model of aggression.

Topic 2: Analysing how cognitive flexibility is influenced by emotions

Research Aim: This research will analyse how emotions influence the cognitive flexibility of individuals.

Topic 3: Tractable cognition: The role of complexity theory in cognitive psychology

Research Aim: This research will discuss tractable cognition. The study will discuss the role of complexity theory in cognitive psychology.

Topic 4: Conflict monitoring across sensory modalities

Research Aim: This research will discuss conflict monitoring during sensory modalities. The study will talk about various conflict monitoring methods.

Topic 5: Familiarity and its effect on facial expression recognition?

Research Aim: This research will discuss the concept of familiarity and its impact on facial expression recognition.

Topic 6: Investigating the relationship between cognitive vulnerability and depression

Research Aim: This research will investigate the relationship between depression and cognitive vulnerability.

Topic 7: Effectiveness of mindfulness training on ratings of perceived stress, mindfulness, and well-being of adolescents enrolled in an international baccalaureate diploma program

Research Aim: This research will discuss the effectiveness of mindfulness training on ratings of well-being and perceived stress in adolescents. The participants of this research will be international baccalaureate diploma students.

Topic 8: Assessing the development of implicit intergroup cognition in relation to in-groups and out-groups: social learning or pre-specified?

Research Aim: This research will assess the development of implicit intergroup cognition with respect to out-groups and in-groups. The study will conclude whether this development classifies as social learning or is pre-specified.

Topic 9: Assessing the relationship between impaired social cognition, emotion, and anxiety disorders.

Research Aim: This research will discuss the relationship between emotion, anxiety disorders, and impaired social cognition.

Topic 10: Investigating the relationship between episodic memory and emotional memory

Research Aim: This research will investigate the relationship between emotional memory and episodic memory and the underlying causes.

Also Read : Project Management Dissertation Topics

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• Social Psychology Dissertation Topics

This branch of psychology has gained tremendous importance in the world of academia in recent times. Essentially, it deals with social interactions, including their influence on the individuals and their origin.

According to Baron, Byrne, and Sulls (1989), “the scientific field seeks to understand the nature and causes of individual behaviour in social situations.”

Therefore, it would not be wrong to say that social psychology primarily investigates how human behaviour can influence other people and the surrounding social environment. Some relevant social psychology dissertation topics are listed below:

Topic 1: Cognitive, affective, and social psychological correlates of psychopathic personality traits in offenders and non-offenders

Research Aim: This research will address cognitive, affective, and social-psychological correlations of psychopathic personality traits in offenders and non-offenders.

Topic 2: A social-psychological exploration of word-of-mouth traveller information in the digital age

Research Aim: This research will explore the word of mouth exchange of traveller information in today’s age with a social-psychological perspective.

Topic 3: Investigating the concept of contemporary social and cultural psychology

Research Aim: This research will investigate the concept of contemporary social and cultural psychology.

Topic 4: Methods for social psychological research: fundamental qualitative and fundamental quantitative methods.

Research Aim: This will be an interesting study. The research will explore two major social psychological research methods; the fundamental qualitative method and the fundamental quantitative method.

Topic 5: The impact of gender mistakes on various individual attitudes and behaviours that contribute to gender inequality

Research Aim: This research will explore the impact of gender issues on different individual attitudes and behaviours. Moreover, the study will assess their impact and contribution to increasing gender inequality.

Topic 6: Personality, passion, self-esteem and psychological well-being among junior elite athletes in the UK

Research Aim: This research will study the psychological well-being of junior athletes in the UK. This includes assessing their personality, passion, and self-esteem.

Topic 7: Mad, bad, or dangerous? Assessing changing social attitudes to mental illness through a study of magazine and TV advertising.

Research Aim: This research will assess the changing social attitudes to mental illness by studying TV and magazine advertising. The study will focus on the impact of these advertisements on the mental health of the audience.

Topic 8: Use of images of women in corporate website branding – The role of gender, marketing, and internet presence

Research Aim: This research will assess the use of women’s images in website branding. The study will evaluate and analyse the role of gender, marketing, and internet presence.

Topic 9: How the use of music can help to reduce crime rate – A quantitative study of underground tube stations in London

Research Aim: The study will focus on an ignored socio-psychological aspect i.e. music. The research will assess how music helps to reduce the crime rate. A quantitative study covering underground tube stations will be conducted.

Topic 10: The enduring legacy of cognitive dissonance

Research Aim: This research will talk about the history of cognitive dissonance. It will also discuss its enduring legacy.

Also Read: Sociology Dissertation Topics

Abnormal Psychology Dissertation Topics

The abnormal patterns of thoughts, emotions, and behaviour that may lead to mental disorders are studied under the abnormal psychology branch of psychology. But what is an abnormality, and who decides what abnormal behaviour is? Historically, societies have been quick to observe and tag individuals as abnormal when they encounter situations that they cannot understand.

Abnormal psychologists are responsible for identifying the human characteristics that deviate from the norm. This branch of psychology can interest students who wish to explore unusual human behaviour and unusual conditions. The following topics on abnormal psychology can help to ease the dissertation topic selection process for your thesis project:

Topic 1: Assessing and Investigating the concepts of abnormality and mental health

Research Aim: This research will discuss the basics of abnormality and mental health. The literature review will cover the various mental health conditions and what leads them to these issues.

Topic 2: A neuropsychological investigation of frontal brain asymmetry in depression with comorbid anxiety

Research Aim: This research will investigate a neuropsychological issue, i.e., frontal brain asymmetry in depression with comorbid anxiety.

Topic 3: What is the relationship between children’s home routines and treatment for ADHD? A study of the literature

Research Aim: This research will talk about a common yet ignored issue, ADHD. The study will explore the relationship between children’s home routines and treatment procedures.

Topic 4: Investigating the relationship between depression and diet – A qualitative study of how the Mediterranean diet can help to lower depression levels

Research Aim: This research will investigate an interesting relationship – between depression and diet. The study will also explore how the Mediterranean diet can help reduce levels of depression.

Topic 5: Promoting mental health and psychological wellbeing in children: A socio-cultural activity theory analysis of professional contributions and learning in a multidisciplinary team

Research Aim: This research will aim to promote mental health and psychological well-being in children. The study will be based on a socio-cultural activity theory analysis of professional contributions and learning in a multidisciplinary team.

Topic 6: A critical inquiry into the views of professionals working with families, parents, and children.

Research Aim: This research will help conduct a critical inquiry into the views of professionals working with parents, families, and children.

Topic 7: Exploring ways of managing stress and coping with poor mental health

Research Aim: This research will help to explore stress and coping issues amongst individuals with poor mental health.

Topic 8: The role of positive irrational beliefs in mental health & wellbeing

Research Aim: This research will talk about the positive role of irrational beliefs associated with mental health and wellbeing.

Topic 9: To understand and establish the relationship between social media websites and self-harm in adolescent females

Research Aim: This research will aim to understand and establish the relationship between social media websites and self-harm in adolescent females.

Topic 10: A biographical narrative study exploring mental ill-health through the life course

Research Aim: This will be a biographical narrative study that will explore the mental illness issues that may cause difficulties in the course of life.

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Developmental and Educational Psychology Dissertation Topics

According to Kendra Cherry (2001), “Educational psychology involves the study of how people learn, including topics such as student outcomes, the instructional process, individual differences in learning, gifted learners and learning disabilities.” This branch of psychology considers not only the learning process but also the social and emotional aspects of development.

Developmental and educational psychologists are responsible for designing professional development programmes, evaluating programmes and interventions, designing training programmes, consulting with groups and individuals, counselling, designing effective treatment programmes, assessing developmental learning and behavioural problems among individuals, diagnosing disabilities and disorders, and identifying and clarifying problems.

Here’s a list of developmental and educational psychology dissertation topics for you to choose from:

Topic 1: Investigating parents’ concerns with a child’s development: A Case Study

Research Aim: This research will investigate the concerns of parents related to child development. A specific case will be examined in this research.

Topic 2: To examine the parent-child relationship issues

Research Aim: This research will explore the issues related to the parent-child bond. Solutions will also be provided as to how these should be tackled.

Topic 3: Managing a child’s difficult temperament or behaviour

Research Aim: This research will help parents understand how they can manage a child who has a difficult temperament.

Topic 4: How educational psychologists can assist a child with disabilities

Research Aim: This research will explore how educational psychologists help in assisting disabled children.

Topic 5: Exploring the causes of sibling rivalries in the family: Studying How These can Be Tackled.

Research Aim: This research will explore the causes behind sibling rivalries in families and will also suggest how these can be controlled.

Topic 6: Problems parents, teachers, and children may face in the transition from early childhood to school years

Research Aim: This study will explore issues and problems parents, teachers, and children face in the transition from early childhood to school years.

Topic 7: Exploring the impact of consultation on educational psychology service users, including pupils, teachers, and parents

Research Aim: This research will explore the impacts of consultation on educational psychology services which include pupils, teachers, and parents.

Topic 8: The development of the theory of mind in deaf, hard of hearing, and hearing preschool children

Research Aim: This research will talk about the developmental theory of mind in deaf people, hard of hearing, and hearing of preschool children.

Topic 9: Cultural differences and perceptions of autism among school psychologists

Research Aim: This research will talk about the cultural differences and perceptions of autism amongst school psychologists.

Topic 10: High school special education teachers’ use of positive behaviour: Effects of a behaviour prompting routine on specific praise rates

Research Aim: This research will discuss the use of positive behaviour by high school special education teachers. Furthermore, the dissertation will also study the impact of behaviour that prompts a routine for specific praise rates.

Related Links:

• Cognitive psychology dissertationtopics
• Educational psychology dissertation topics
• Clinical psychology dissertation topics
• Counselling psychology dissertation topics
• Forensic-psychology-dissertation-topics
• Criminal Psychology Dissertation Ideas
• Sports Psychology Dissertation Ideas
• Neuro Psychology Dissertation Ideas
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Important Notes:

As a psychology student looking to get good grades, it is essential to develop new ideas and experiment with existing psychology theories – i.e., to add value and interest to your research topic.

Psychology is vast and interrelated with so many other academic disciplines. That is why it is imperative to create a psychology dissertation topic that is particular, sound, and actually solves a practical problem that may be rampant in the field.

We can’t stress how important it is to develop a logical research topic based on your entire research. There are several significant downfalls to getting your topic wrong; your supervisor may not be interested in working on it, the topic has no academic creditability, the research may not make logical sense, and there is a possibility that the study is not viable.

This impacts your time and efforts in writing your dissertation as you may end up in the cycle of rejection at the initial stage of the dissertation. That is why we recommend reviewing existing research to develop a topic, taking advice from your supervisor, and even asking for help in this particular stage of your dissertation.

Keeping our advice in mind while developing a research topic will allow you to pick one of the best psychology dissertation topics that fulfil your requirement of writing a research paper and adds to the body of knowledge.

Therefore, it is recommended that when finalising your dissertation topic, you read recently published literature to identify gaps in the research that you may help fill.

Remember- dissertation topics need to be unique, solve an identified problem, be logical, and be practically implemented. Please look at some of our sample psychology dissertation topics to get an idea for your own dissertation.

How to Structure Your Psychology Dissertation

A well-structured dissertation can help students to achieve a high overall academic grade.

• A Title Page
• Acknowledgements
• Declaration
• Abstract: A summary of the research completed
• Table of Contents
• Introduction : This chapter includes the project rationale, research background, key research aims and objectives, and the research problems. An outline of the structure of a dissertation can also be added to this chapter.
• Literature Review : This chapter presents relevant theories and frameworks by analyzing published and unpublished literature on the chosen research topic to address research questions . The purpose is to highlight and discuss the selected research area’s relative weaknesses and strengths while identifying any research gaps. Break down the topic and key terms that can positively impact your dissertation and your tutor.
• Methodology : The data collection and analysis methods and techniques employed by the researcher are presented in the Methodology chapter, which usually includes research design , research philosophy, research limitations, code of conduct, ethical consideration, data collection methods, and data analysis strategy .
• Findings and Analysis : Findings of the research are analysed in detail under the Findings and Analysis chapter. All key findings/results are outlined in this chapter without interpreting the data or drawing any conclusions. It can be useful to include graphs, charts, and tables in this chapter to identify meaningful trends and relationships.
• Discussion and Conclusion : The researcher presents his interpretation of the results in this chapter and states whether the research hypothesis has been verified or not. An essential aspect of this section is establishing the link between the results and evidence from the literature. Recommendations with regard to the implications of the findings and directions for the future may also be provided. Finally, a summary of the overall research, along with final judgments, opinions, and comments, must be included in the form of suggestions for improvement.
• References : Make sure to complete this following your University’s requirements
• Bibliography
• Appendices : Any additional information, diagrams, and graphs used to complete the dissertation but not part of the dissertation should be included in the Appendices chapter. Essentially, the purpose is to expand the information/data.

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Portfolio management examines the projects and programs of an organization. There are three aspects involved here: selection, prioritization, and control. This is done by taking into account the strategic goals of the organization.

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Psychological Factors in Physical Education and Sport, volume III

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The Influence of Social Support and Resilience with Coping Strategies among Students

| Published: May 15, 2024

The college experience can be a challenging time for many students as they navigate academic pressures, social relationships, and personal growth. In this context, the interplay between social support, resilience, coping strategies and mental health plays a crucial role in determining the wellbeing of college students. Social support refers to the assistance and resources provided by others in times of need. Resilience is the ability to bounce back from adversity and adapt positively to stressors. Coping strategies are the behavioural or psychological efforts individuals employ to manage stressful situations. Mental health encompasses emotional, psychological, and social well-being. The present study aimed to study the influence of social support and resilience with coping strategies among students. A sample of 200 students (100 Male, 100 Female) was selected. The Multidimensional Scale of Perceived Social Support (Zimet, Dahlem, & Farley,1988), Brief Resilience Scale (Smith, 2008) and Coping Strategy Indicator (Amirkhan & James H., 1994) were administered. Multiple Regression Analysis was done to find out the contribution of different predictor variables. The results indicated a significant positive relationship between the predictor variables and the criterion variable. Positive correlation was found between Social Support and Resilience with Coping Strategies which were Problem Solving, Seeking Social Support and Avoidance. The contribution of emotional maturity was much more remarkable as compared to the other two predictor variables i.e. self-concept and spirituality in determining academic resilience among undergraduates.

Social Support , Resilience , Coping Strategies

This is an Open Access Research distributed under the terms of the Creative Commons Attribution License (www.creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any Medium, provided the original work is properly cited.

© 2024, Barwal, V. & Cherian, J.

Received: April 05, 2024; Revision Received: May 12, 2024; Accepted: May 15, 2024

Vrishti Barwal @ [email protected]

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Ideas for Psychology Experiments

Inspiration for psychology experiments is all around if you know where to look

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Steven Gans, MD is board-certified in psychiatry and is an active supervisor, teacher, and mentor at Massachusetts General Hospital.

Psychology experiments can run the gamut from simple to complex. Students are often expected to design—and sometimes perform—their own experiments, but finding great experiment ideas can be a little challenging. Fortunately, inspiration is all around if you know where to look—from your textbooks to the questions that you have about your own life.

Always discuss your idea with your instructor before beginning your experiment—particularly if your research involves human participants. (Note: You'll probably need to submit a proposal and get approval from your school's institutional review board.)

At a Glance

If you are looking for an idea for psychology experiments, start your search early and make sure you have the time you need. Doing background research, choosing an experimental design, and actually performing your experiment can be quite the process. Keep reading to find some great psychology experiment ideas that can serve as inspiration. You can then find ways to adapt these ideas for your own assignments.

15 Ideas for Psychology Experiments

Most of these experiments can be performed easily at home or at school. That said, you will need to find out if you have to get approval from your teacher or from an institutional review board before getting started.

The following are some questions you could attempt to answer as part of a psychological experiment:

• Are people really able to "feel like someone is watching" them ? Have some participants sit alone in a room and have them note when they feel as if they are being watched. Then, see how those results line up to your own record of when participants were actually being observed.
• Can certain colors improve learning ? You may have heard teachers or students claim that printing text on green paper helps students read better, or that yellow paper helps students perform better on math exams. Design an experiment to see whether using a specific color of paper helps improve students' scores on math exams.
• Can color cause physiological reactions ? Perform an experiment to determine whether certain colors cause a participant's blood pressure to rise or fall.
• Can different types of music lead to different physiological responses ? Measure the heart rates of participants in response to various types of music to see if there is a difference.
• Can smelling one thing while tasting another impact a person's ability to detect what the food really is ? Have participants engage in a blind taste test where the smell and the food they eat are mismatched. Ask the participants to identify the food they are trying and note how accurate their guesses are.
• Could a person's taste in music offer hints about their personality ? Previous research has suggested that people who prefer certain styles of music tend to exhibit similar  personality traits. Administer a personality assessment and survey participants about their musical preferences and examine your results.
• Do action films cause people to eat more popcorn and candy during a movie ? Have one group of participants watch an action movie, and another group watch a slow-paced drama. Compare how much popcorn is consumed by each group.
• Do colors really impact moods ? Investigate to see if the  color blue makes people feel calm, or if the color red leaves them feeling agitated.
• Do creative people see  optical illusions  differently than more analytical people ? Have participants complete an assessment to measure their level of creative thinking. Then ask participants to look at optical illusions and note what they perceive.
• Do people rate individuals with perfectly symmetrical faces as more beautiful than those with asymmetrical faces ? Create sample cards with both symmetrical and asymmetrical faces and ask participants to rate the attractiveness of each picture.
• Do people who use social media exhibit signs of addiction ? Have participants complete an assessment of their social media habits, then have them complete an addiction questionnaire.
• Does eating breakfast help students do better in school ? According to some, eating breakfast can have a beneficial influence on school performance. For your experiment, you could compare the test scores of students who ate breakfast to those who did not.
• Does sex influence short-term memory ? You could arrange an experiment that tests whether men or women are better at remembering specific types of information.
• How likely are people to conform in groups ? Try this experiment to see what percentage of people are likely to conform . Enlist confederates to give the wrong response to a math problem and then see if the participants defy or conform to the rest of the group.
• How much information can people store in short-term memory ? Have participants study a word list and then test their memory. Try different versions of the experiment to see which memorization strategies, like chunking or mnemonics, are most effective.

Once you have an idea, the next step is to learn more about  how to conduct a psychology experiment .

Psychology Experiments on Your Interests

If none of the ideas in the list above grabbed your attention, there are other ways to find inspiration for your psychology experiments.

How do you come up with good psychology experiments? One of the most effective approaches is to look at the various problems, situations, and questions that you are facing in your own life.

You can also think about the things that interest you. Start by considering the topics you've studied in class thus far that have really piqued your interest. Then, whittle the list down to two or three major areas within psychology that seem to interest you the most.

From there, make a list of questions you have related to the topic. Any of these questions could potentially serve as an experiment idea.

Use Textbooks for Inspiration for Psychology Experiments

Your psychology textbooks are another excellent source you can turn to for experiment ideas. Choose the chapters or sections that you find particularly interesting—perhaps it's a chapter on  social psychology  or a section on child development.

Start by browsing the experiments discussed in your book. Then think of how you could devise an experiment related to some of the questions your text asks. The reference section at the back of your textbook can also serve as a great source for additional reference material.

Discuss Psychology Experiments with Other Students

It can be helpful to brainstorm with your classmates to gather outside ideas and perspectives. Get together with a group of students and make a list of interesting ideas, subjects, or questions you have.

The information from your brainstorming session can serve as a basis for your experiment topic. It's also a great way to get feedback on your own ideas and to determine if they are worth exploring in greater depth.

Study Classic Psychology Experiments

Taking a closer look at a classic psychology experiment can be an excellent way to trigger some unique and thoughtful ideas of your own. To start, you could try conducting your own version of a famous experiment or even updating a classic experiment to assess a slightly different question.

Famous Psychology Experiments

Examples of famous psychology experiments that might be a source of further questions you'd like to explore include:

• Marshmallow test experiments
• Little Albert experiment
• Hawthorne effect experiments
• Bystander effect experiments
• Robbers Cave experiments
• Halo effect experiments
• Piano stairs experiment
• Cognitive dissonance experiments
• False memory experiments

You might not be able to replicate an experiment exactly (lots of classic psychology experiments have ethical issues that would preclude conducting them today), but you can use well-known studies as a basis for inspiration.

Review the Literature on Psychology Experiments

If you have a general idea about what topic you'd like to experiment, you might want to spend a little time doing a brief literature review before you start designing. In other words, do your homework before you invest too much time on an idea.

Visit your university library and find some of the best books and articles that cover the particular topic you are interested in. What research has already been done in this area? Are there any major questions that still need to be answered? What were the findings of previous psychology experiments?

Tackling this step early will make the later process of writing the introduction  to your  lab report  or research paper much easier.

Ask Your Instructor About Ideas for Psychology Experiments

If you have made a good effort to come up with an idea on your own but you're still feeling stumped, it might help to talk to your instructor. Ask for pointers on finding a good experiment topic for the specific assignment. You can also ask them to suggest some other ways you could generate ideas or inspiration.

While it can feel intimidating to ask for help, your instructor should be more than happy to provide some guidance. Plus, they might offer insights that you wouldn't have gathered on your own. Your instructor probably has lots of ideas for psychology experiments that would be worth exploring.

If you need to design or conduct psychology experiments, there are plenty of great ideas (both old and new) for you to explore. Consider an idea from the list above or turn some of your own questions about the human mind and behavior into an experiment.

Before you dive in, make sure that you are observing the guidelines provided by your instructor and always obtain the appropriate permission before conducting any research with human or animal subjects.

Frequently Asked Questions

Finding a topic for a research paper is much like finding an idea for an experiment. Start by considering your own interests, or browse though your textbooks for inspiration. You might also consider looking at online news stories or journal articles as a source of inspiration.

Three of the most classic social psychology experiments are:

• The Asch Conformity Experiment : This experiment involved seeing if people would conform to group pressure when rating the length of a line.
• The Milgram Obedience Experiment : This experiment involved ordering participants to deliver what they thought was a painful shock to another person.
• The Stanford Prison Experiment : This experiment involved students replicating a prison environment to see how it would affect participant behavior. 

Jakovljević T, Janković MM, Savić AM, et al. The effect of colour on reading performance in children, measured by a sensor hub: From the perspective of gender .  PLoS One . 2021;16(6):e0252622. doi:10.1371/journal.pone.0252622

Greenberg DM, et al. Musical preferences are linked to cognitive styles . PLoS One. 2015;10(7). doi:10.1371/journal.pone.0131151

Kurt S, Osueke KK. The effects of color on the moods of college students . Sage. 2014;4(1). doi:10.1177/2158244014525423

Hartline-Grafton H, Levin M. Breakfast and School-Related Outcomes in Children and Adolescents in the US: A Literature Review and its Implications for School Nutrition Policy .  Curr Nutr Rep . 2022;11(4):653-664. doi:10.1007/s13668-022-00434-z

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."