writing a hypothesis for psychology

Research Hypothesis In Psychology: Types, & Examples

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.

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A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

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

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk, "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

Operationalization

Hypothesis Types

Hypotheses examples.

Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

Forming a question
Performing background research
Creating a hypothesis
Designing an experiment
Collecting data
Analyzing the results
Drawing conclusions
Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you expect to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment do not support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

Is your hypothesis based on your research on a topic?
Can your hypothesis be tested?
Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

Collect as many observations about a topic or problem as you can.
Evaluate these observations and look for possible causes of the problem.
Create a list of possible explanations that you might want to explore.
After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method , falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that if something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

Does your hypothesis focus on something that you can actually test?
Does your hypothesis include both an independent and dependent variable?
Can you manipulate the variables?
Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the dependent variable if you change the independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

"Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
"Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."
"Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
"Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

"People with high-sugar diets and sedentary activity levels are more likely to develop depression."
"Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

"There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
"There is no difference in scores on a memory recall task between children and adults."
"There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

"People who take St. John's wort supplements will have less anxiety than those who do not."
"Adults will perform better on a memory task than children."
"Children who play first-person shooter games will show higher levels of aggression than children who do not."

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as case studies , naturalistic observations , and surveys are often used when conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a correlational study can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually cause another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses . R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:]. Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ? PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies . Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

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

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How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection.

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

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Step 1: ask a question.

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2: Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.

Step 3: Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

Step 4: Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

The relevant variables
The specific group being studied
The predicted outcome of the experiment or analysis

Step 5: Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

Step 6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

A hypothesis is not just a guess. It should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations, and statistical analysis of data).

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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2.4 Developing a Hypothesis

Learning objectives.

Distinguish between a theory and a hypothesis.
Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis it is imporant to distinguish betwee a theory and a hypothesis. A theory is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition. He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.

A hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observation before we can develop a broader theory.

Theories and hypotheses always have this if-then relationship. “ If drive theory is correct, then cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this question is an interesting one on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [1] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the number of examples they bring to mind and the other was that people base their judgments on how easily they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method (although this term is much more likely to be used by philosophers of science than by scientists themselves). A researcher begins with a set of phenomena and either constructs a theory to explain or interpret them or chooses an existing theory to work with. He or she then makes a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researcher then conducts an empirical study to test the hypothesis. Finally, he or she reevaluates the theory in light of the new results and revises it if necessary. This process is usually conceptualized as a cycle because the researcher can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As Figure 2.2 shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

Figure 4.4 Hypothetico-Deductive Method Combined With the General Model of Scientific Research in Psychology Together they form a model of theoretically motivated research.

Figure 2.2 Hypothetico-Deductive Method Combined With the General Model of Scientific Research in Psychology Together they form a model of theoretically motivated research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [2] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans (Zajonc & Sales, 1966) [3] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use inductive reasoning which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be positive. That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that really it does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

Key Takeaways

A theory is broad in nature and explains larger bodies of data. A hypothesis is more specific and makes a prediction about the outcome of a particular study.
Working with theories is not “icing on the cake.” It is a basic ingredient of psychological research.
Like other scientists, psychologists use the hypothetico-deductive method. They construct theories to explain or interpret phenomena (or work with existing theories), derive hypotheses from their theories, test the hypotheses, and then reevaluate the theories in light of the new results.
Practice: Find a recent empirical research report in a professional journal. Read the introduction and highlight in different colors descriptions of theories and hypotheses.
Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic. Journal of Personality and Social Psychology, 61 , 195–202. ↵
Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach. Journal of Personality and Social Psychology, 13 , 83–92. ↵
Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

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Developing a Hypothesis

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

Learning Objectives

Distinguish between a theory and a hypothesis.
Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis, it is important to distinguish between a theory and a hypothesis. A theory is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition (1965) [1] . He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [2] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the number of examples they bring to mind and the other was that people base their judgments on how easily they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method (although this term is much more likely to be used by philosophers of science than by scientists themselves). Researchers begin with a set of phenomena and either construct a theory to explain or interpret them or choose an existing theory to work with. They then make a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researchers then conduct an empirical study to test the hypothesis. Finally, they reevaluate the theory in light of the new results and revise it if necessary. This process is usually conceptualized as a cycle because the researchers can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As Figure 2.3 shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [3] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans [Zajonc & Sales, 1966] [4] in many other studies afterward).

Incorporating Theory into Your Research

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use inductive reasoning which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be positive. That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that it really does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

Zajonc, R. B. (1965). Social facilitation. Science, 149 , 269–274 ↵
Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic. Journal of Personality and Social Psychology, 61 , 195–202. ↵
Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach. Journal of Personality and Social Psychology, 13 , 83–92. ↵
Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

A coherent explanation or interpretation of one or more phenomena.

A specific prediction about a new phenomenon that should be observed if a particular theory is accurate.

A cyclical process of theory development, starting with an observed phenomenon, then developing or using a theory to make a specific prediction of what should happen if that theory is correct, testing that prediction, refining the theory in light of the findings, and using that refined theory to develop new hypotheses, and so on.

The ability to test the hypothesis using the methods of science and the possibility to gather evidence that will disconfirm the hypothesis if it is indeed false.

Developing a Hypothesis 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.

Share This Book

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Overview of the Scientific Method

10 Developing a Hypothesis

Learning objectives.

Distinguish between a theory and a hypothesis.
Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [2] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the number of examples they bring to mind and the other was that people base their judgments on how easily they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method (although this term is much more likely to be used by philosophers of science than by scientists themselves). Researchers begin with a set of phenomena and either construct a theory to explain or interpret them or choose an existing theory to work with. They then make a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researchers then conduct an empirical study to test the hypothesis. Finally, they reevaluate the theory in light of the new results and revise it if necessary. This process is usually conceptualized as a cycle because the researchers can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As Figure 2.3 shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [3] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans [Zajonc & Sales, 1966] [4] in many other studies afterward).

Incorporating Theory into Your Research

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use inductive reasoning which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be positive. That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that it really does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

Zajonc, R. B. (1965). Social facilitation. Science, 149 , 269–274 ↵
Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic. Journal of Personality and Social Psychology, 61 , 195–202. ↵
Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach. Journal of Personality and Social Psychology, 13 , 83–92. ↵
Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

A coherent explanation or interpretation of one or more phenomena.

A specific prediction about a new phenomenon that should be observed if a particular theory is accurate.

The ability to test the hypothesis using the methods of science and the possibility to gather evidence that will disconfirm the hypothesis if it is indeed false.

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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2.4: Developing a Hypothesis

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Learning Objectives

Distinguish between a theory and a hypothesis.
Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis it is important to distinguish between a theory and a hypothesis. A theory is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition. He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Theory Testing

Incorporating Theory into Your Research

Characteristics of a Good Hypothesis

Key Takeaways

A theory is broad in nature and explains larger bodies of data. A hypothesis is more specific and makes a prediction about the outcome of a particular study.
Working with theories is not “icing on the cake.” It is a basic ingredient of psychological research.
Like other scientists, psychologists use the hypothetico-deductive method. They construct theories to explain or interpret phenomena (or work with existing theories), derive hypotheses from their theories, test the hypotheses, and then reevaluate the theories in light of the new results.
Practice: Find a recent empirical research report in a professional journal. Read the introduction and highlight in different colors descriptions of theories and hypotheses.
Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic. Journal of Personality and Social Psychology, 61 , 195–202.
Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach. Journal of Personality and Social Psychology, 13 , 83–92.
Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168.

AIMS, HYPOTHESES AND HOW TO WRITE THEM

WHY DO WE HAVE AIMS AND HYPOTHESES?

Aims and hypotheses in research serve as fundamental components that provide direction and structure to the study. They are like signposts that guide researchers along the path of investigation. The aims outline the overarching goals or purposes of the study, while the hypotheses propose specific predictions or explanations to be tested. Together, they help researchers stay focused, establish clear objectives, and frame the inquiry systematically and organised. Defining the scope and purpose of the research, aims, and hypotheses enables researchers to pursue meaningful inquiry and contribute to advancing knowledge in their field.

Imagine you're embarking on a research journey to understand the factors influencing human mate selection. You aim to uncover the underlying mechanisms that drive mate preferences and ultimately contribute to reproductive success.

Your aim, therefore, is to explore the relationship between specific traits and their perceived attractiveness in potential mates. You want to investigate whether evolutionary factors such as facial symmetry, body proportions, and even personality traits play a role in shaping mate preferences.

With this aim in mind, you formulate hypotheses to guide your investigation. For example:

Hypothesis 1: Potential mates will perceive individuals with symmetrical facial features as more attractive than those with asymmetrical features.

Hypothesis 2: Men will prefer female partners who exhibit signs of reproductive health and fertility, such as a waist-to-hip ratio of 0.7.

Hypothesis 3: Women will prioritize traits in potential mates that signal resource acquisition and provisioning abilities, such as socioeconomic status and ambition.

These hypotheses serve as your roadmap, outlining the specific predictions you aim to test in your research. They provide a clear direction for your investigation and guide you toward a deeper understanding of the evolutionary underpinnings of mate selection.

In summary, aims and hypotheses in psychology, much like in any field of research, work together to guide inquiry, shape investigations, and ultimately contribute to advancing knowledge in understanding human behaviour and cognition.

WHAT’S THE DIFFERENCE BETWEEN AIMS AND HYPOTHESES?

The aims and hypotheses of a study serve distinct purposes in research.

Aims are typically articulated towards the conclusion of the introduction section of a research paper, following the review of psychological literature. Once the researcher has provided the background history of the study and justified its necessity, the subsequent step is to outline the study's aim explicitly. This entails explaining the study's intended investigation and serving as a guiding framework for the research. The aim is to offer a comprehensive overview of the research study or proposal, delineating the objectives and questions to be addressed.

AIMS IN SHORT:

The justification provided in the introduction should logically lead to the aims, which in turn should logically transition into a statement of the hypothesis(es).

A general prediction about what the researcher expects to happen at the start of an investigation/research.

The aims typically focus on the intended outcomes or contributions of the research to the existing body of knowledge in the field.

AIM VERSUS HYPOTHESIS EXAMPLE

Aim - Strange Situation : The Strange Situation was developed by Mary Ainsworth and aimed to investigate infants' attachment styles towards their caregivers. Specifically, the study explored how infants react when separated from and reunited with their primary caregivers in a controlled laboratory setting. Based on the infants ' behaviours during seven key events, this study sought to identify attachment patterns, such as secure, insecure-avoidant, and insecure-resistant.

Hypothesis- Strange Situation: " There will be a higher quality of attachment associated with more positive behaviours exhibited by infants and their primary caregivers across seven key areas."

EXAMPLE OF AN “AIMS”

“Studies indicate a significant distortion in how individuals perceive body shape within the general population, with a particular emphasis on females; most research was conducted 20 years ago on American undergraduates. The following study aims to determine if these body distortions exist today in 16-to 18-year-old female English students. It will investigate the relationship between perceived body size and ideal body size in females with no history of eating disorders.”

Hypotheses are tentative propositions or educated guesses formulated to explain observed phenomena or answer specific research questions. In neuroscience, hypotheses are often constructed to propose relationships between variables, such as brain activity and behaviour, or the effects of certain interventions on neural processes.

Example: Hypothesis: Increased activation in the prefrontal cortex is associated with improved working memory performance in adults.

Explanation: This hypothesis suggests that there is a relationship between the level of activation in the prefrontal cortex, a brain region associated with executive functions like working memory, and the performance of working memory tasks in adults

IN SUMMARY:

Hypotheses are specific, testable predictions or statements that propose a relationship or difference between variables.

Hypotheses have testable, operationalised terms.

Hypotheses are derived from the study's aims and are formulated based on theoretical considerations, existing evidence, or logical reasoning.

They articulate the expected outcomes or results of the study and provide a basis for testing the research questions.

Hypotheses are often framed as if-then statements, where the independent variable is expected to affect the dependent variable.

They guide the research process by clearly focusing on data collection, analysis, and interpretation.

Hypotheses are typically stated after the aims, as they are more specific and detailed statements that stem from the broader research goals outlined in the aims.

EXAMPLES OF HYPOTHESES

NULL AND ALTERNATIVE HYPOTHESES

There are two types of hypotheses: The Alternative (sometimes called the Experimental Hypothesis) and the Null Hypothesis.

WHAT IS A NULL HYPOTHESIS?

POPPER'S INFLUENCE ON NULL AND ALTERNATIVE HYPOTHESES

Contrary to popular belief, in scientific research, the protocol is to reject the null hypothesis, not confirm the alternative hypothesis. Before Popper, the null hypothesis, as it is now commonly understood, did not have a defined place in scientific methodology.

The null hypothesis (HO) i s the foundational element in scientific experimentation. It represents the default assumption that there is no effect or difference in what is being studied. It is formulated in a way that can be potentially refuted. When deciding whether your research has worked, the scientific language is to accept or reject the null hypothesis and not to accept or reject the alternative hypothesis. This seemingly inconsequential rule demonstrates that the research in question is genuinely scientific as it is capable of having a null hypothesis, e.g., being refuted, unlike, for example, pseudoscientific theories like Freud's, which are incapable of being falsified. For instance, it cannot be refuted that a person has unconscious biases.

The null hypothesis states no effect or difference in what is being studied. It is formulated in a way that can be potentially refuted. For instance, consider the humorous hypothesis: "Baked beans cause naughtiness." This example illustrates how the null hypothesis can be tested and potentially disputed. However, if a hypothesis is theoretically impossible to disprove, such as the non-existence of ghosts, it may not be possible to formulate a null hypothesis, rendering the research unscientific.

Predicting nothing will happen is the opposite of your alternative/experimental hypothesis.

For example Null Hypothesis (H0):

“There is no difference between the perceived current body size of 16-18-year-old female students and their ideal body size as selected on a body shape scale.

"There is no significant relationship between the consumption of cheese before bedtime and the frequency or intensity of nightmares in individuals."

WHAT IS AN ALTERNATIVE HYPOTHESIS?

The alternative hypothesis suggests an effect or difference in the phenomenon under investigation and serves as the basis for comparison against the null hypothesis.

The term "Alternative Hypothesis" (H1) was coined to underscore its role as an alternative explanation to the null hypothesis. In scientific inquiry, the null hypothesis is pivotal as it can be either accepted or rejected based on empirical evidence, making it a fundamental aspect of hypothesis testing.

It is imperative for all research endeavors to incorporate an alternative hypothesis as it acknowledges the possibility that observed correlations or differences in conditions may not be solely attributable to chance, which the null hypothesis cannot ascertain. Interestingly, some psychologists interchangeably refer to the alternative hypothesis as an experimental hypothesis, though the latter term is specifically reserved for studies involving true experimental designs. Nonetheless, conceptually, both terms represent hypotheses that deviate from the null.

Alternative hypotheses are applicable across a spectrum of research contexts, encompassing both non-experimental studies and experiments, such as correlations and content analysis. They encompass both directional (1-tailed) and non-directional (2-tailed) hypotheses and are structured differently from experimental hypotheses.

Alternative Hypothesis ( H1 or HA). : "The consumption of cheese before bedtime is associated with an increase in the frequency and intensity of nightmares in individuals."

In this alternative hypothesis, it is suggested that there is a specific relationship between eating cheese before bedtime and experiencing more frequent and intense nightmares. It proposes a cause-and-effect connection between the two variables.

On the other hand, the null hypothesis suggests no meaningful connection exists between eating cheese before bedtime and the occurrence or intensity of nightmares. It essentially states that any observed differences in nightmares are due to chance and unrelated to cheese consumption.

EXPERIMENTAL AND ALTERNATIVE HYPOTHESES

The term "Alternative Hypothesis" (H1) was coined to underscore its role as an alternative explanation to the null hypothesis. In scientific inquiry, the null hypothesis is pivotal as it can be accepted or rejected based on empirical evidence, making it a fundamental aspect of hypothesis testing.

All research endeavours must incorporate an alternative hypothesis as it acknowledges the possibility that observed correlations or differences in conditions may not be solely attributable to chance, which the null hypothesis cannot ascertain. Interestingly, some psychologists interchangeably refer to the alternative hypothesis as an experimental hypothesis, though the latter term is reserved explicitly for studies involving true experimental designs. Nonetheless, conceptually, both terms represent hypotheses that deviate from the null.

Alternative hypotheses are applicable across a spectrum of research contexts, encompassing both non-experimental studies and experiments, such as correlations and content analysis. They encompass directional (1-tailed) and non-directional (2-tailed) hypotheses and are structured differently from experimental hypotheses.

WHAT ARE DIRECTIONAL OR ONE-TAILED HYPOTHESES?

Hypotheses may take two forms: directional (1-tailed) and non-directional (2-tailed).

For directional experimental hypotheses , they propose a specific direction of the effect or relationship between variables. This is typically utilised in scenarios where researchers expect the outcome based on prior knowledge or theory. In other words, a hypothesis predicts which condition (IV) will do better or worse. In other words, it predicts one direction (tail) in which the results should occur.

Examples: ' Participants in the jogging condition will rate photographs of the opposite sex higher than participants in the non-jogging condition.’

Participants in the jogging condition will rate photographs of the opposite sex higher than participants in the non-jogging condition.

If Correlational, a directional hypothesis will predict a specific direction, e.g., negative or positive.

Example: ' There will be a positive correlation between the 4D and 2D finger ratio and Bateman’s risk-taking questionnaire scores .’

If a correlation does not specify if the outcome is considered to be positive or negative, then it is a non-directional hypothesis.

Directional quasi-experimental hypotheses propos e a specific direction of the effect of participant differences on the dependent variable (DV) based on prior knowledge or theory.

Example: “ Female participants will have higher IQs than male participants.”

WHAT ARE NON-DIRECTIONAL OR TWO-TAILED HYPOTHESES?

On the other hand, non-directional experimental hypotheses suggest a relationship or effect between variables, but they do not specify the direction of this effect. This is often used when researchers have no specific expectations regarding the outcome.

A hypothesis that does not predict which condition will do better or worse only states there will be differences in conditions (the IV). ¬ ® Example:

¬ ‘There will be a difference in the ratings of photographs of the opposite sex made by Participants in the jogging condition and participants in the non-jogging condition.’

Similarly, hypotheses may be non-directional in quasi-experimental designs where participant differences are independent variables (IV).

Non-directional quasi-experimental hypotheses, like their experimental counterparts, suggest a relationship between participant differences and the DV without specifying the direction of this relationship.

UNDERSTANDING HYPOTHESES:

Experimental hypotheses are for experimental research and should contain the word "difference" if applicable in their hypotheses (e.g., "There will be a difference between participants in the cheese condition and the non-cheese condition in the number of nightmares they experience").

Quasi-designs should also include the word "difference" in the hypotheses (e.g., "There will be a difference between French participants and English participants in the number of nightmares they experience.”

Alternative hypotheses are for all types of research, but they are usually used in non-experimental. research

For non-experimental research other than correlations, the word "association should be included in their hypotheses. (e.g., "There will be an association between variables advertised in Lonely Heart advertisements for females").

For correlations, include the words "correlation," "link," or "relationship" in the hypothesis (e.g., "There is a relationship between smartphone usage and lower attention span").

For all non-experimental research other than correlations, use the word "association" (e.g., "There will be an association between variables advertised in Lonely Heart advertisements for females")

TESTS OF DIFFERENCE HYPOTHESES

Tests of difference hypotheses are commonly used in experiments,e.g., those that compare the effects of different conditions or treatments on an outcome variable. They are also used in quasi-experiments,where the aim is to test differences between participants.

Experimental hypotheses are for experimental research and should contain the word "difference" if applicable (e.g., "There will be a difference between participants in the cheese condition and the non-cheese condition in the number of nightmares they experience").

Experimental hypothesis - directional (1 tailed) for ”true experiments”, e.g., laboratory and field

Experimental hypothesis - non-directional (2-tailed) for ”true experiments”, e.g., laboratory and field

Quasi-experimental hypothesis - directional (1 tailed) for experiments where the participant’s differences are the IV.

Quasi-experimental hypothesis - non-directional (2-tailed) for experiments where the participant’s differences are the IV.

TESTS OF ASSOCIATION HYPOTHESES

Non-experimental directional (1 tailed) for observations, questionnaire/surveys, interviews and case studies

Non-experimental, non-directional (2-tailed) for observations, questionnaire/surveys, interviews and case studies

TESTS OF CORRELATION HYPOTHESES

Correlations directional (1-tailed), e.g., positive correlations and negative correlations.

Correlations are non-directional (2-tailed), e.g., just predicting a correlation but not a direction, e.g., it could be either negative or positive.

Writing Experimental Alternative Hypotheses

These are for all experiments: Laboratory, Field, Quasi and Natural. Experimental Hypotheses include directional (1 tailed) and non-directional (2 tailed hypotheses).

HOW TO WRITE A HYPOTHESES

General stuff about writing hypotheses.

TERMINOLOGY USAGE:

Always refer to individuals as "participants" unless studying non-human animals.

Use "male" and "female" instead of other terms like "man," "woman," "boy," or "girl."

OPERATIONALISATION OF VARIABLES:

Operationalise the independent variable (IV) by specifying how it will be measured or manipulated. For example, if studying the perception of age, indicate the age range (e.g., "Participants aged 18-19" to avoid subjective terms like "child" or "old").

Similarly, operationalise the dependent variable (DV) by stating how it will be measured or assessed. For instance, if studying intelligence, operationalise it as "participants' scores on a standardised IQ test."

Know the difference between experimental and alternative hypotheses. Alternative hypotheses are formulated in distinct ways to accommodate the requirements of diverse research methodologies.

You always write a null hypothesis.

HYPOTHEIS FORMULATION

The hypothesis must be worded precisely (called operationalised). A hypothesis such as:

‘Younger people have better memories than older people’ is too imprecise. What age groups are being tested?

The initial hypothesis, "Younger people have better memories than older people," lacks specificity. It's essential to specify the age groups being tested, the type of memory being assessed (short-term or long-term memory), and the metric used to determine "better" memory.

Candidates should ensure that the hypothesis (es) is unambiguous and understandable to someone who has not yet read the rest of the report.

A revised operationalised hypothesis could be:

Participants aged between 16 -25 will recall more digits from a standardised memory test than participants aged between 26- 35 .

This hypothesis outlines the age groups, specifies the type of memory (short-term memory), and clarifies the measure of memory performance (number of digits recalled).

WRITING THE EXPERIMENTAL HYPOTHESES

Guide for writing ‘directional or one-tailed experimental hypotheses..

There are many ways to write directional hypotheses; you'll adopt your version once you find your feet.

To write a directional or one-tailed experimental hypothesis, follow these steps using the following hypothesis as an example: "Participants in the cheese condition will have more nightmares than participants in the non-cheese condition."

STEP ONE: Identify the First IV/Condition

Begin with "Participants in the………… followed by the first condition. Example: "Participants in the cheese condition..."

"Participants in the cheese condition will have more nightmares than participants in the non-cheese condition."

STEP TWO: State the Expected Outcome:

Express what you predict will happen about the first IV/Condition. Use terms like "higher," "lower," "more," "less," "better," or "worse" to indicate the direction of the effect. Example: "...will have more."

STEP THREE: Operationalise the DV:

Clearly define the dependent variable (DV) and how it will be measured or assessed. Example: "...nightmares."

STEP THREE: Identify the Second IV/Condition: EXAMPLE: non-cheese condition

After stating the operationalized DV Example, mention the second condition: "...will have more nightmares..."Examples:

“Participants in the cheese condition will have more nightmares than participants in the non-cheese condition.”

GUIDE FOR WRITING ‘DIRECTIONAL OR ONE-TAILED QUASI-EXPERIMENTS

Remember you are testing the difference between two groups of participants here. So, the groups are different somehow (age, gender, intelligence, birth order, etc.). Why would you test the difference between two groups if they were not different?

The two groups will be tested on the same thing, so only one condition exists. We are not so much interested in what the two groups are doing; we are more interested in the difference between how the two groups perform against each other—for example, Blind participants and sighted participants and their hearing ability. The two groups of participants are the IV.

There are many ways to write non-directional hypotheses; you'll adopt your version once you find your feet.

To write a directional or one-tailed experimental hypothesis for quasi-designs, follow these steps using the following hypothesis as an example: “Participants from Nigeria 2) will have lower scores 3) on a body shape questionnaire 4) than participants from the UK”.

STEP ONE: Always start with ‘Participants who are (then state what is different from them to the other group, for example, male; female, aged 40-60; from Nigeria, etc.…………).

“ Participants from Nigeria will have lower scores on a body shape questionnaire than participants from the UK”.

STEP TWO: State what you think will happen, e.g., if they score higher/lower, prefer more/less, better/worse, etc. What is your prediction?

“Participants from Nigeria will have lower scores on a body shape questionnaire 4) than participants from the UK”.

STEP THREE: State the OPERATIONALISED variable.

STEP FOUR: Always finish with the other set of participants (then state what is different from them to the other group, for example, male, female, aged 40-60; from Nigeria, etc.).

“Participants from Nigeria will have lower scores on a body shape questionnaire than participants from the UK”.

Participants from Nigeria will have lower scores on a body shape questionnaire 4) than participants from the UK.

Participants who are male will rate photographs of the opposite sex higher than participants who are female.

Green participants will recall more digits from a standardised memory test than participants who are yellow.

Participants who came to live in the UK after the age of 18 will have lower scores on the Television addiction questionnaire than participants who were born in the UK (1 tail).

GUIDE FOR WRITING ‘NON-DIRECTIONAL OR TWO-TAILED EXPERIMENTAL HYPOTHESES.

To write a non-directional or two-tailed experimental hypothesis, follow these steps using the following hypothesis as an example: "There will be a difference between participants in the cheese condition and participants in the non-cheese condition and the number of nightmares they have.”

STEP ONE: Identify the First IV/Condition and state the Expected Outcome:

Always start with ‘There will be a difference as you are not predicting a direction or tail, only non-similar results.

"There will be a difference between participants in the cheese condition and participants in the non-cheese condition and the number of nightmares they have.”

STEP TWO: Name the first condition

STEP TWO: Identify the Second IV/Condition: EXAMPLE: non-cheese condition

GUIDE FOR WRITING ‘NON-DIRECTIONAL OR TWO-TAILED QUASI-EXPERIMENTS

The two groups will be tested on the same thing, so only one condition exists. We are not so much interested in what the two groups are doing; we are more interested in the difference between how the two groups perform against each other—for example, males and females and driving ability. The two groups of participants are the IV.

To write a non-directional or two-tailed experimental hypothesis for quasi-designs, follow these steps using the following hypothesis as an example: “There will be a difference between male participants’ scores on a standardised anxiety test and female participants’ scores on a test (2-tailed).”

STEP ONE: The prediction part.

Begin with:” There will be a difference between….”

“There will be a difference between male participants’ scores on a standardised anxiety test and female participants’ scores on a test (2-tailed).”

STEP TWO: State what the difference will manifest as—e.g. scores, attitudes, preferences, etc.

“There will be a difference between male participants’ scores on a standardised anxiety test a nd female participants’ scores on a test (2-tailed).”

STEP THREE: State the operationalised DV.

State the first IV/condition. Always put ‘between participants in ……….condition and

“ There will be a difference between male participants’ scores on a standardised anxiety test and female participants’ scores on a test (2-tailed).”

STEP four: State the second IV.

Always finish with ‘Participants in the other ……….condition.’

There will be a difference in the number of nightmares between participants in the cheese condition and in the non-cheese condition.

There will be a difference in the ratings of photographs of the opposite sex) between participants in the jogging condition and between participants in the non-jogging condition.

WRITING A NULL HYPOTHESES

Null Hypotheses are formulated in a manner akin to two-tailed hypotheses, with the inclusion of the term "no." Once you grasp the structure of writing alternative and experimental hypotheses, crafting null hypotheses becomes straightforward

’ There will be no difference in the number of nightmares between participants in the cheese and non-cheese conditions.

There will be no difference in the ratings of photographs of the opposite sex between participants in the jogging condition and participants in the non-jogging condition.

There will be no difference between green and yellow participants’ scores on a standardized memory test.

There will be no correlation between height and drinking alcohol.

There will be no correlation between siblings’ SRSS scores.

There will be no association between female participants aged 25 – 35, who are more attracted to males with professional jobs, and female participants aged 18- 24, who are more attracted to looks.

WRITING FOR NON- EXPERIMENTAL HYPOTHESES

Hypotheses for tests of correlation.

Use the word correlation or link or relationship in your hypothesis.

If one-tailed use either positive or negative

If two-tailed use just correlation

Examples below:

ONE-TAILED/DIRECTIONAL:

There will be a negative correlation between siblings’ scores on the Social Readjustment Rating Scale (SRSS) (1 tail).

There will be a positive correlation between high scores on a standardised happiness scale and high scores on the relationship satisfaction questionnaire (2 tail)

There will be a negative correlation between siblings' birth order and IQ.(1 tail)

There will be a positive correlation between siblings' birth order and IQ.(1 tail)

There will be a positive correlation between above 6ft and drinking alcohol excessively (1 tail)

TWO-TAILED/NON-DIRECTIONAL:

There will be a correlation between siblings’ scores on the Social Readjustment Rating Scale (SRSS) (2 tail).

There will be a correlation between high scores on a standardised happiness scale and high scores on relationship satisfaction questionnaire (2 Tail)

There will be a correlation between siblings' birth order and IQ.(2 Tail)

There will be a correlation between siblings’ scores on the Social Readjustment Rating Scale (SRSS) (1 tail).

There will be a correlation between above 6ft and drinking alcohol excessively (2 tail)

HYPOTHESES FOR TESTS OF ASSOCIATION

Tests of association cover interviews, questionnaire surveys, content analysis and observations.

Observation of girls and boys at play on the road and pavement.

Content analysis of males and females and what they advertise in Lonely Heart advertisements.

Questionnaire on older and younger females and types of males they are attracted to.

Remember to use the word association in your hypothesis, e.g., there will be an association between this variable and this variable.

Hypothesis examples:

DIRECTIONAL/ONE-TAILED

There will be a higher quality of attachment will be associated with more positive behaviours exhibited by infants and their primary caregivers across seven key areas."

Observation of girls and boys at play on the road and pavement. Hypothesis: Boys are likelier to play on the road than girls.

Content analysis of males and females and what they advertise in Lonely Heart advertisements. Hypothesis: Males will be more likely to advertise status than females in Lonely Heart advertisements.

Questionnaire on older and younger females and types of males they are attracted to. Hypothesis: Older females will be more likely to prefer males with professional jobs than younger females.

NON- NON-DIRECTIONAL/TWO-TAILED

"There will be a significant association between the quality of attachment and the behaviours exhibited by infants and their primary caregivers across seven key areas.

Observation of girls and boys at play on the road and pavement. Hypothesis: There will be an association between the gender of participants (girls vs. boys) and the location of play (road vs. pavement).

Content analysis of males and females and what they advertise in Lonely Heart advertisements. Hypothesis: There will be an association between the gender of participants (males vs. females) and the attributes advertised in Lonely Heart advertisements (looks vs. status).

Questionnaire on older and younger females and types of males they are attracted to. Hypothesis: There will be an association between the age group of female participants (older vs. younger) and the preferences for attributes in males (professional jobs vs. looks)

OPERATIONALISING VARIABLES

Operationalise refers to precisely defining a variable so that it becomes unambiguous and objective. For instance, if two researchers are tasked with observing "naughtiness" on the playground, they might provide different interpretations because "naughtiness" is subjective

Try and operationalise the following:

Risk-taking

Depression:

Sexual attraction

Aggression: ·

Short-term memory

Intelligence

There are no set answers in this question, check with me if you think you may have a good idea but it has not been listed.

Risk-taking :

Develop a risk-taking questionnaire or measure finger length ratio.

Question: How likely are you to engage in risky behaviour in the following scenarios? (Scale: 1-5)

Measure: Calculate the ratio between the length of the index and ring fingers.

Depression :

Administer a depression scale or conduct a clinical interview.

Question: Over the past two weeks, how often have you experienced symptoms such as sadness, loss of interest, or changes in appetite? (Scale: 0-3)

Measure: Conduct a structured clinical interview based on DSM criteria.

Sexual attraction :

Rate attractiveness from photographs or observe real-life interactions.

Question: On a scale from 1 to 10, how physically attractive do you find the person in the photograph?

Measure: Observe participants' eye movements when presented with images of different genders.

Aggression :

Conduct observations in playgrounds or use aggression scales.

Question: How often do you engage in physical or verbal aggression towards others? (Scale: 1-5)

Measure: Record the frequency of aggressive behaviours observed during playground observations.

Administer memory tests or specifically use a digit span test for short-term memory.

Question: How many words can you recall from the list you just heard? (Immediate recall)

Measure: Use the number of correctly recalled digits in a sequence to measure short-term memory.

Intelligence :

Calculate an average of GCSE scores, ALIS scores, or IQ tests.

Question: What was your score on the standardized intelligence test?

Measure: Calculate the average score across multiple standardized tests.

Attachment :

Use the Strange Situation procedure or the Hazan and Shaver Love Quiz.

Question: How do you typically feel when separated from your primary caregiver? (Secure, insecure-avoidant, insecure-resistant)

Measure: Assess attachment style based on behaviours observed during the Strange Situation procedure.

Measure pupil dilation, use an empathy scale, or conduct experiments where participants stop to help an abandoned child.

Question: How much do you feel for others when they are experiencing strong emotions? (Scale: 1-7)

Measure: Record changes in pupil size while participants view emotionally charged images.

Utilise the Social Readjustment Rating Scale (SRRS), daily Hassles Scale, or measure physiological responses like blood pressure and pupil dilation.

Question: How many stressful life events have you experienced over the past year? (Checklist)

Measure: Record changes in blood pressure and pupil dilation in response to stress-inducing stimuli.

Measure heart rate, employ anxiety questionnaires, conduct Galvanic Skin Response tests, or test blood pressure.

Question: How anxious do you feel in social situations? (Scale: 1-10)

Measure: Record changes in heart rate during a stress-inducing task or social interaction

ACTIVITY: OPERATIONALISING VARIABLES

For each scenario below, operationalise the variables.

Adults with a mental illness will have impaired memory abilities.

Consumption of sugar-filled drinks will increase aggression in boys.

Girls who use social networking sites will have learning difficulties.

Stressed males will take more days off work.

Participants diagnosed with bipolar depression will have lower digit spans than participants without a mental illness.

Male participants aged (5-10) who consume one can of Cola will commit more physically aggressive acts during their ten-minute morning break than male participants aged (5-10) who do not consume Cola.

Female participants (aged 12-17) who use social networking sites for more than 10 hours per week will have lower scores on the Stanford Binet intelligence test than participants who do not use social networking sites.

There will be a positive correlation between high scores on the SRRS and the number of sick days in the preceding year for male participants aged 18-30.

Sometimes psychologists find it hard to operationalise variables themselves (or too unethical to operationalise – how could you operationalise risk-taking behaviour, for instance, without compromising a participant’s physical or psychological well-being?) and so rely on questionnaires, attitudes, tests, etc. These questionnaires, attitudes, and tests need to be standardised. You cannot just make one up! Standardized scales and tests are usually referred to by their specific name, e.g., The GAF scale, which measures a person’s everyday functioning. Standardised questionnaires/tests/scales (like IQ, ALIS, GCSE, A ‘level, Cattel’s personality test, etc.) are questionnaires or scales that psychologists have tested for validity and reliability.

QUESTIONS ON HYPOTHESES

What role do aims and hypotheses play in the research process?

Can you provide an example of an aim in research, specifically in evolutionary psychology?

Explain the purpose of formulating hypotheses in research.

What are the differences between aims and hypotheses?

How are aims typically presented in a research paper?

Provide an example of a hypothesis formulated in evolutionary psychology research.

What is the null hypothesis, and why is it important in scientific experimentation?

Explain the difference between a null hypothesis and an alternative hypothesis.

What is the significance of Popper's influence on null and alternative hypotheses?

Can you give an example of a null hypothesis in a research context?

Describe the characteristics of an alternative hypothesis.

How do directional (1-tailed) hypotheses differ from non-directional (2-tailed) hypotheses?

Provide an example of a directional hypothesis in an experimental context.

What is the purpose of tests of different hypotheses in experimental and quasi-experimental research?

Explain how to write hypotheses with the operationalisation of variables.

Why is it important for hypotheses to be worded precisely and unambiguously?

Write out the hypothesis for the following. Include 1, 2 tail and null

Participants either listen to music with aggressive or non-aggressive lyrics and then compare their scores on an aggression questionnaire.

2. Preference for masculine and feminine faces of men when females are ovulating or not ovulating.

3. The effect of TV on creativity (operationalise DV).

4. 2d and 4d finger length ratios and testosterone (operationalise as risk-taking, then operationalise variables further).

5. Who are the most conforming, males or females (operationalise variables)?

6. Gender and playing on the road or not (operationalise variables).

7. Older siblings and younger siblings and empathy (operationalise variables).

8. Physiological arousal or not (operationalise IV) and attraction to the opposite sex (operationalise DV).

1a). Participants in the aggressive lyric condition will have higher scores on a standardised aggression test than participants in the non-aggressive lyric condition.

1b). There will be a difference in the scores on a standardised aggression test between participants in the aggressive and non-aggressive conditions.

1c). There will be no difference in the scores on the standardised aggression test between participants in the aggressive and non-aggressive lyric conditions.

2a). Female participants in the ovulation condition will have more preferences for masculine faces (BBC Masculine/Feminine face scale) than female participants in the non-ovulating condition.

2b). There will be a difference in a number of preferences for masculine and feminine faces (BBC Masculine/Feminine face scale) between female participants in the ovulating condition and female participants in the non-ovulating condition.

2c). There will be no difference in the preference for masculine and feminine faces (BBC Masculine/Feminine face scale) between female participants in the ovulating and non-ovulating conditions.

3a). Participants in the non-TV-watching condition will score higher on a standardised creativity test than participants in the TV watching condition

3b). There will be a difference in the scores on a standardised creativity test between participants in the watching TV condition and participants in the non-watching television condition.

3c). There will be no difference in the scores on a standardised creativity test between participants in the watching TV condition and participants in the non-watching television condition.

4a). There will be a correlation between participants’ 2d and 4d finger length ratios and scores on a risk-taking test.

4b). There will be a positive correlation between participants’ 2d and 4d finger length ratio and scores on a risk-taking test; the higher the ratio, the higher the risk test score. Or

4b). There will be a positive correlation between high’ 2d and 4d finger length ratios and high scores on a risk taking test.

4c). There will be no correlation between participants’ 2d and 4d finger length ratios and scores on a risk taking test.

5a). Male participants will score higher on a conformity questionnaire than female participants

5b). Male and female participants will differ in the scores on a conformity test.

5c). There will be no difference in the scores on a conformity test between male participants and female participants

6a). Male participants aged 5-7 will have one foot on the road more frequently than female participants aged 5-7

6b). There will be a difference in the frequency of having one foot on the road between male and female participants aged 5-7.

6c). There will be no difference in the frequency of having one foot on the road between male and female participants aged 5-7.

7a). There will be a negative correlation between older and younger siblings’ empathy scores.

7b). There will be a correlation between older and younger siblings’ empathy scores.

7c). There will be no correlation between older and younger siblings’ empathy scores.

8a). Participants in the jogging on the spot condition will rate photographs of the opposite sex higher in attractiveness than participants in the non-jogging on the spot condition.

8b). There will be a difference in the ratings of photographs of the opposite sex for attractiveness between participants in the jogging on-the-spot condition and participants in the non-jogging on-the-spot condition.

8c). There will be no difference in the ratings of photographs of the opposite sex for attractiveness between participants in the jogging on-the-spot condition and participants in the non-jogging on-the-spot condition.

INTRODUCTION TO SCIENTIFIC PROCESSES

How to Develop a Good Research Hypothesis

The story of a research study begins by asking a question. Researchers all around the globe are asking curious questions and formulating research hypothesis. However, whether the research study provides an effective conclusion depends on how well one develops a good research hypothesis. Research hypothesis examples could help researchers get an idea as to how to write a good research hypothesis.

This blog will help you understand what is a research hypothesis, its characteristics and, how to formulate a research hypothesis

Table of Contents

What is Hypothesis?

Hypothesis is an assumption or an idea proposed for the sake of argument so that it can be tested. It is a precise, testable statement of what the researchers predict will be outcome of the study. Hypothesis usually involves proposing a relationship between two variables: the independent variable (what the researchers change) and the dependent variable (what the research measures).

What is a Research Hypothesis?

Research hypothesis is a statement that introduces a research question and proposes an expected result. It is an integral part of the scientific method that forms the basis of scientific experiments. Therefore, you need to be careful and thorough when building your research hypothesis. A minor flaw in the construction of your hypothesis could have an adverse effect on your experiment. In research, there is a convention that the hypothesis is written in two forms, the null hypothesis, and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment).

Characteristics of a Good Research Hypothesis

As the hypothesis is specific, there is a testable prediction about what you expect to happen in a study. You may consider drawing hypothesis from previously published research based on the theory.

A good research hypothesis involves more effort than just a guess. In particular, your hypothesis may begin with a question that could be further explored through background research.

To help you formulate a promising research hypothesis, you should ask yourself the following questions:

Is the language clear and focused?
What is the relationship between your hypothesis and your research topic?
Is your hypothesis testable? If yes, then how?
What are the possible explanations that you might want to explore?
Does your hypothesis include both an independent and dependent variable?
Can you manipulate your variables without hampering the ethical standards?
Does your research predict the relationship and outcome?
Is your research simple and concise (avoids wordiness)?
Is it clear with no ambiguity or assumptions about the readers’ knowledge
Is your research observable and testable results?
Is it relevant and specific to the research question or problem?

The questions listed above can be used as a checklist to make sure your hypothesis is based on a solid foundation. Furthermore, it can help you identify weaknesses in your hypothesis and revise it if necessary.

Source: Educational Hub

How to formulate a research hypothesis.

A testable hypothesis is not a simple statement. It is rather an intricate statement that needs to offer a clear introduction to a scientific experiment, its intentions, and the possible outcomes. However, there are some important things to consider when building a compelling hypothesis.

1. State the problem that you are trying to solve.

Make sure that the hypothesis clearly defines the topic and the focus of the experiment.

2. Try to write the hypothesis as an if-then statement.

Follow this template: If a specific action is taken, then a certain outcome is expected.

3. Define the variables

Independent variables are the ones that are manipulated, controlled, or changed. Independent variables are isolated from other factors of the study.

Dependent variables , as the name suggests are dependent on other factors of the study. They are influenced by the change in independent variable.

4. Scrutinize the hypothesis

Evaluate assumptions, predictions, and evidence rigorously to refine your understanding.

Types of Research Hypothesis

The types of research hypothesis are stated below:

1. Simple Hypothesis

It predicts the relationship between a single dependent variable and a single independent variable.

2. Complex Hypothesis

It predicts the relationship between two or more independent and dependent variables.

3. Directional Hypothesis

It specifies the expected direction to be followed to determine the relationship between variables and is derived from theory. Furthermore, it implies the researcher’s intellectual commitment to a particular outcome.

4. Non-directional Hypothesis

It does not predict the exact direction or nature of the relationship between the two variables. The non-directional hypothesis is used when there is no theory involved or when findings contradict previous research.

5. Associative and Causal Hypothesis

The associative hypothesis defines interdependency between variables. A change in one variable results in the change of the other variable. On the other hand, the causal hypothesis proposes an effect on the dependent due to manipulation of the independent variable.

6. Null Hypothesis

Null hypothesis states a negative statement to support the researcher’s findings that there is no relationship between two variables. There will be no changes in the dependent variable due the manipulation of the independent variable. Furthermore, it states results are due to chance and are not significant in terms of supporting the idea being investigated.

7. Alternative Hypothesis

It states that there is a relationship between the two variables of the study and that the results are significant to the research topic. An experimental hypothesis predicts what changes will take place in the dependent variable when the independent variable is manipulated. Also, it states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

Research Hypothesis Examples of Independent and Dependent Variables

Research Hypothesis Example 1 The greater number of coal plants in a region (independent variable) increases water pollution (dependent variable). If you change the independent variable (building more coal factories), it will change the dependent variable (amount of water pollution).

Research Hypothesis Example 2 What is the effect of diet or regular soda (independent variable) on blood sugar levels (dependent variable)? If you change the independent variable (the type of soda you consume), it will change the dependent variable (blood sugar levels)

You should not ignore the importance of the above steps. The validity of your experiment and its results rely on a robust testable hypothesis. Developing a strong testable hypothesis has few advantages, it compels us to think intensely and specifically about the outcomes of a study. Consequently, it enables us to understand the implication of the question and the different variables involved in the study. Furthermore, it helps us to make precise predictions based on prior research. Hence, forming a hypothesis would be of great value to the research. Here are some good examples of testable hypotheses.

More importantly, you need to build a robust testable research hypothesis for your scientific experiments. A testable hypothesis is a hypothesis that can be proved or disproved as a result of experimentation.

Importance of a Testable Hypothesis

To devise and perform an experiment using scientific method, you need to make sure that your hypothesis is testable. To be considered testable, some essential criteria must be met:

There must be a possibility to prove that the hypothesis is true.
There must be a possibility to prove that the hypothesis is false.
The results of the hypothesis must be reproducible.

Without these criteria, the hypothesis and the results will be vague. As a result, the experiment will not prove or disprove anything significant.

What are your experiences with building hypotheses for scientific experiments? What challenges did you face? How did you overcome these challenges? Please share your thoughts with us in the comments section.

Frequently Asked Questions

The steps to write a research hypothesis are: 1. Stating the problem: Ensure that the hypothesis defines the research problem 2. Writing a hypothesis as an 'if-then' statement: Include the action and the expected outcome of your study by following a ‘if-then’ structure. 3. Defining the variables: Define the variables as Dependent or Independent based on their dependency to other factors. 4. Scrutinizing the hypothesis: Identify the type of your hypothesis

Hypothesis testing is a statistical tool which is used to make inferences about a population data to draw conclusions for a particular hypothesis.

Hypothesis in statistics is a formal statement about the nature of a population within a structured framework of a statistical model. It is used to test an existing hypothesis by studying a population.

Research hypothesis is a statement that introduces a research question and proposes an expected result. It forms the basis of scientific experiments.

The different types of hypothesis in research are: • Null hypothesis: Null hypothesis is a negative statement to support the researcher’s findings that there is no relationship between two variables. • Alternate hypothesis: Alternate hypothesis predicts the relationship between the two variables of the study. • Directional hypothesis: Directional hypothesis specifies the expected direction to be followed to determine the relationship between variables. • Non-directional hypothesis: Non-directional hypothesis does not predict the exact direction or nature of the relationship between the two variables. • Simple hypothesis: Simple hypothesis predicts the relationship between a single dependent variable and a single independent variable. • Complex hypothesis: Complex hypothesis predicts the relationship between two or more independent and dependent variables. • Associative and casual hypothesis: Associative and casual hypothesis predicts the relationship between two or more independent and dependent variables. • Empirical hypothesis: Empirical hypothesis can be tested via experiments and observation. • Statistical hypothesis: A statistical hypothesis utilizes statistical models to draw conclusions about broader populations.

Wow! You really simplified your explanation that even dummies would find it easy to comprehend. Thank you so much.

Thanks a lot for your valuable guidance.

I enjoy reading the post. Hypotheses are actually an intrinsic part in a study. It bridges the research question and the methodology of the study.

Useful piece!

This is awesome.Wow.

It very interesting to read the topic, can you guide me any specific example of hypothesis process establish throw the Demand and supply of the specific product in market

Nicely explained

It is really a useful for me Kindly give some examples of hypothesis

It was a well explained content ,can you please give me an example with the null and alternative hypothesis illustrated

clear and concise. thanks.

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Good to learn

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Explained well and in simple terms. Quick read! Thank you

It awesome. It has really positioned me in my research project

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Home » What is a Hypothesis – Types, Examples and Writing Guide

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
Education : “Implementing a new teaching method will result in higher student achievement scores.”
Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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How to Get Started on Your First Psychology Experiment

Acquiring even a little expertise in advance makes science research easier..

Updated May 16, 2024 | Reviewed by Ray Parker

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Students often struggle at the beginning of research projects—knowing how to begin.
Research projects can sometimes be inspired by everyday life or personal concerns.
Becoming something of an "expert" on a topic in advance makes designing a study go more smoothly.

One of the most rewarding and frustrating parts of my long career as a psychology professor at a small liberal arts college has been guiding students through the senior capstone research experience required near the end of their college years. Each psychology major must conduct an independent experiment in which they collect data to test a hypothesis, analyze the data, write a research paper, and present their results at a college poster session or at a professional conference.

The rewarding part of the process is clear: The students' pride at seeing their poster on display and maybe even getting their name on an article in a professional journal allows us professors to get a glimpse of students being happy and excited—for a change. I also derive great satisfaction from watching a student discover that he or she has an aptitude for research and perhaps start shifting their career plans accordingly.

The frustrating part comes at the beginning of the research process when students are attempting to find a topic to work on. There is a lot of floundering around as students get stuck by doing something that seems to make sense: They begin by trying to “think up a study.”

The problem is that even if the student's research interest is driven by some very personal topic that is deeply relevant to their own life, they simply do not yet know enough to know where to begin. They do not know what has already been done by others, nor do they know how researchers typically attack that topic.

Students also tend to think in terms of mission statements (I want to cure eating disorders) rather than in terms of research questions (Why are people of some ages or genders more susceptible to eating disorders than others?).

Needless to say, attempting to solve a serious, long-standing societal problem in a few weeks while conducting one’s first psychology experiment can be a showstopper.

Even a Little Bit of Expertise Can Go a Long Way

My usual approach to helping students get past this floundering stage is to tell them to try to avoid thinking up a study altogether. Instead, I tell them to conceive of their mission as becoming an “expert” on some topic that they find interesting. They begin by reading journal articles, writing summaries of these articles, and talking to me about them. As the student learns more about the topic, our conversations become more sophisticated and interesting. Researchable questions begin to emerge, and soon, the student is ready to start writing a literature review that will sharpen the focus of their research question.

In short, even a little bit of expertise on a subject makes it infinitely easier to craft an experiment on that topic because the research done by others provides a framework into which the student can fit his or her own work.

This was a lesson I learned early in my career when I was working on my own undergraduate capstone experience. Faced with the necessity of coming up with a research topic and lacking any urgent personal issues that I was trying to resolve, I fell back on what little psychological expertise I had already accumulated.

In a previous psychology course, I had written a literature review on why some information fails to move from short-term memory into long-term memory. The journal articles that I had read for this paper relied primarily on laboratory studies with mice, and the debate that was going on between researchers who had produced different results in their labs revolved around subtle differences in the way that mice were released into the experimental apparatus in the studies.

Because I already had done some homework on this, I had a ready-made research question available: What if the experimental task was set up so that the researcher had no influence on how the mouse entered the apparatus at all? I was able to design a simple animal memory experiment that fit very nicely into the psychological literature that was already out there, and this prevented a lot of angst.

Please note that my undergraduate research project was guided by the “expertise” that I had already acquired rather than by a burning desire to solve some sort of personal or social problem. I guarantee that I had not been walking around as an undergraduate student worrying about why mice forget things, but I was nonetheless able to complete a fun and interesting study.

My first experiment may not have changed the world, but it successfully launched my research career, and I fondly remember it as I work with my students 50 years later.

Frank McAndrew, Ph.D., is the Cornelia H. Dudley Professor of Psychology at Knox College.

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Chapter 11: Presenting Your Research

Writing a Research Report in American Psychological Association (APA) Style

Learning Objectives

Identify the major sections of an APA-style research report and the basic contents of each section.
Plan and write an effective APA-style research report.

In this section, we look at how to write an APA-style empirical research report , an article that presents the results of one or more new studies. Recall that the standard sections of an empirical research report provide a kind of outline. Here we consider each of these sections in detail, including what information it contains, how that information is formatted and organized, and tips for writing each section. At the end of this section is a sample APA-style research report that illustrates many of these principles.

Sections of a Research Report

Title page and abstract.

An APA-style research report begins with a title page . The title is centred in the upper half of the page, with each important word capitalized. The title should clearly and concisely (in about 12 words or fewer) communicate the primary variables and research questions. This sometimes requires a main title followed by a subtitle that elaborates on the main title, in which case the main title and subtitle are separated by a colon. Here are some titles from recent issues of professional journals published by the American Psychological Association.

Sex Differences in Coping Styles and Implications for Depressed Mood
Effects of Aging and Divided Attention on Memory for Items and Their Contexts
Computer-Assisted Cognitive Behavioural Therapy for Child Anxiety: Results of a Randomized Clinical Trial
Virtual Driving and Risk Taking: Do Racing Games Increase Risk-Taking Cognitions, Affect, and Behaviour?

Below the title are the authors’ names and, on the next line, their institutional affiliation—the university or other institution where the authors worked when they conducted the research. As we have already seen, the authors are listed in an order that reflects their contribution to the research. When multiple authors have made equal contributions to the research, they often list their names alphabetically or in a randomly determined order.

In some areas of psychology, the titles of many empirical research reports are informal in a way that is perhaps best described as “cute.” They usually take the form of a play on words or a well-known expression that relates to the topic under study. Here are some examples from recent issues of the Journal Psychological Science .

“Smells Like Clean Spirit: Nonconscious Effects of Scent on Cognition and Behavior”
“Time Crawls: The Temporal Resolution of Infants’ Visual Attention”
“Scent of a Woman: Men’s Testosterone Responses to Olfactory Ovulation Cues”
“Apocalypse Soon?: Dire Messages Reduce Belief in Global Warming by Contradicting Just-World Beliefs”
“Serial vs. Parallel Processing: Sometimes They Look Like Tweedledum and Tweedledee but They Can (and Should) Be Distinguished”
“How Do I Love Thee? Let Me Count the Words: The Social Effects of Expressive Writing”

Individual researchers differ quite a bit in their preference for such titles. Some use them regularly, while others never use them. What might be some of the pros and cons of using cute article titles?

For articles that are being submitted for publication, the title page also includes an author note that lists the authors’ full institutional affiliations, any acknowledgments the authors wish to make to agencies that funded the research or to colleagues who commented on it, and contact information for the authors. For student papers that are not being submitted for publication—including theses—author notes are generally not necessary.

The abstract is a summary of the study. It is the second page of the manuscript and is headed with the word Abstract . The first line is not indented. The abstract presents the research question, a summary of the method, the basic results, and the most important conclusions. Because the abstract is usually limited to about 200 words, it can be a challenge to write a good one.

Introduction

The introduction begins on the third page of the manuscript. The heading at the top of this page is the full title of the manuscript, with each important word capitalized as on the title page. The introduction includes three distinct subsections, although these are typically not identified by separate headings. The opening introduces the research question and explains why it is interesting, the literature review discusses relevant previous research, and the closing restates the research question and comments on the method used to answer it.

The Opening

The opening , which is usually a paragraph or two in length, introduces the research question and explains why it is interesting. To capture the reader’s attention, researcher Daryl Bem recommends starting with general observations about the topic under study, expressed in ordinary language (not technical jargon)—observations that are about people and their behaviour (not about researchers or their research; Bem, 2003 [1] ). Concrete examples are often very useful here. According to Bem, this would be a poor way to begin a research report:

Festinger’s theory of cognitive dissonance received a great deal of attention during the latter part of the 20th century (p. 191)

The following would be much better:

The individual who holds two beliefs that are inconsistent with one another may feel uncomfortable. For example, the person who knows that he or she enjoys smoking but believes it to be unhealthy may experience discomfort arising from the inconsistency or disharmony between these two thoughts or cognitions. This feeling of discomfort was called cognitive dissonance by social psychologist Leon Festinger (1957), who suggested that individuals will be motivated to remove this dissonance in whatever way they can (p. 191).

After capturing the reader’s attention, the opening should go on to introduce the research question and explain why it is interesting. Will the answer fill a gap in the literature? Will it provide a test of an important theory? Does it have practical implications? Giving readers a clear sense of what the research is about and why they should care about it will motivate them to continue reading the literature review—and will help them make sense of it.

Breaking the Rules

Researcher Larry Jacoby reported several studies showing that a word that people see or hear repeatedly can seem more familiar even when they do not recall the repetitions—and that this tendency is especially pronounced among older adults. He opened his article with the following humourous anecdote:

A friend whose mother is suffering symptoms of Alzheimer’s disease (AD) tells the story of taking her mother to visit a nursing home, preliminary to her mother’s moving there. During an orientation meeting at the nursing home, the rules and regulations were explained, one of which regarded the dining room. The dining room was described as similar to a fine restaurant except that tipping was not required. The absence of tipping was a central theme in the orientation lecture, mentioned frequently to emphasize the quality of care along with the advantages of having paid in advance. At the end of the meeting, the friend’s mother was asked whether she had any questions. She replied that she only had one question: “Should I tip?” (Jacoby, 1999, p. 3)

Although both humour and personal anecdotes are generally discouraged in APA-style writing, this example is a highly effective way to start because it both engages the reader and provides an excellent real-world example of the topic under study.

The Literature Review

Immediately after the opening comes the literature review , which describes relevant previous research on the topic and can be anywhere from several paragraphs to several pages in length. However, the literature review is not simply a list of past studies. Instead, it constitutes a kind of argument for why the research question is worth addressing. By the end of the literature review, readers should be convinced that the research question makes sense and that the present study is a logical next step in the ongoing research process.

Like any effective argument, the literature review must have some kind of structure. For example, it might begin by describing a phenomenon in a general way along with several studies that demonstrate it, then describing two or more competing theories of the phenomenon, and finally presenting a hypothesis to test one or more of the theories. Or it might describe one phenomenon, then describe another phenomenon that seems inconsistent with the first one, then propose a theory that resolves the inconsistency, and finally present a hypothesis to test that theory. In applied research, it might describe a phenomenon or theory, then describe how that phenomenon or theory applies to some important real-world situation, and finally suggest a way to test whether it does, in fact, apply to that situation.

Looking at the literature review in this way emphasizes a few things. First, it is extremely important to start with an outline of the main points that you want to make, organized in the order that you want to make them. The basic structure of your argument, then, should be apparent from the outline itself. Second, it is important to emphasize the structure of your argument in your writing. One way to do this is to begin the literature review by summarizing your argument even before you begin to make it. “In this article, I will describe two apparently contradictory phenomena, present a new theory that has the potential to resolve the apparent contradiction, and finally present a novel hypothesis to test the theory.” Another way is to open each paragraph with a sentence that summarizes the main point of the paragraph and links it to the preceding points. These opening sentences provide the “transitions” that many beginning researchers have difficulty with. Instead of beginning a paragraph by launching into a description of a previous study, such as “Williams (2004) found that…,” it is better to start by indicating something about why you are describing this particular study. Here are some simple examples:

Another example of this phenomenon comes from the work of Williams (2004).

Williams (2004) offers one explanation of this phenomenon.

An alternative perspective has been provided by Williams (2004).

We used a method based on the one used by Williams (2004).

Finally, remember that your goal is to construct an argument for why your research question is interesting and worth addressing—not necessarily why your favourite answer to it is correct. In other words, your literature review must be balanced. If you want to emphasize the generality of a phenomenon, then of course you should discuss various studies that have demonstrated it. However, if there are other studies that have failed to demonstrate it, you should discuss them too. Or if you are proposing a new theory, then of course you should discuss findings that are consistent with that theory. However, if there are other findings that are inconsistent with it, again, you should discuss them too. It is acceptable to argue that the balance of the research supports the existence of a phenomenon or is consistent with a theory (and that is usually the best that researchers in psychology can hope for), but it is not acceptable to ignore contradictory evidence. Besides, a large part of what makes a research question interesting is uncertainty about its answer.

The Closing

The closing of the introduction—typically the final paragraph or two—usually includes two important elements. The first is a clear statement of the main research question or hypothesis. This statement tends to be more formal and precise than in the opening and is often expressed in terms of operational definitions of the key variables. The second is a brief overview of the method and some comment on its appropriateness. Here, for example, is how Darley and Latané (1968) [2] concluded the introduction to their classic article on the bystander effect:

These considerations lead to the hypothesis that the more bystanders to an emergency, the less likely, or the more slowly, any one bystander will intervene to provide aid. To test this proposition it would be necessary to create a situation in which a realistic “emergency” could plausibly occur. Each subject should also be blocked from communicating with others to prevent his getting information about their behaviour during the emergency. Finally, the experimental situation should allow for the assessment of the speed and frequency of the subjects’ reaction to the emergency. The experiment reported below attempted to fulfill these conditions. (p. 378)

Thus the introduction leads smoothly into the next major section of the article—the method section.

The method section is where you describe how you conducted your study. An important principle for writing a method section is that it should be clear and detailed enough that other researchers could replicate the study by following your “recipe.” This means that it must describe all the important elements of the study—basic demographic characteristics of the participants, how they were recruited, whether they were randomly assigned, how the variables were manipulated or measured, how counterbalancing was accomplished, and so on. At the same time, it should avoid irrelevant details such as the fact that the study was conducted in Classroom 37B of the Industrial Technology Building or that the questionnaire was double-sided and completed using pencils.

The method section begins immediately after the introduction ends with the heading “Method” (not “Methods”) centred on the page. Immediately after this is the subheading “Participants,” left justified and in italics. The participants subsection indicates how many participants there were, the number of women and men, some indication of their age, other demographics that may be relevant to the study, and how they were recruited, including any incentives given for participation.

Three ways of organizing an APA-style method. Long description available.

After the participants section, the structure can vary a bit. Figure 11.1 shows three common approaches. In the first, the participants section is followed by a design and procedure subsection, which describes the rest of the method. This works well for methods that are relatively simple and can be described adequately in a few paragraphs. In the second approach, the participants section is followed by separate design and procedure subsections. This works well when both the design and the procedure are relatively complicated and each requires multiple paragraphs.

What is the difference between design and procedure? The design of a study is its overall structure. What were the independent and dependent variables? Was the independent variable manipulated, and if so, was it manipulated between or within subjects? How were the variables operationally defined? The procedure is how the study was carried out. It often works well to describe the procedure in terms of what the participants did rather than what the researchers did. For example, the participants gave their informed consent, read a set of instructions, completed a block of four practice trials, completed a block of 20 test trials, completed two questionnaires, and were debriefed and excused.

In the third basic way to organize a method section, the participants subsection is followed by a materials subsection before the design and procedure subsections. This works well when there are complicated materials to describe. This might mean multiple questionnaires, written vignettes that participants read and respond to, perceptual stimuli, and so on. The heading of this subsection can be modified to reflect its content. Instead of “Materials,” it can be “Questionnaires,” “Stimuli,” and so on.

The results section is where you present the main results of the study, including the results of the statistical analyses. Although it does not include the raw data—individual participants’ responses or scores—researchers should save their raw data and make them available to other researchers who request them. Several journals now encourage the open sharing of raw data online.

Although there are no standard subsections, it is still important for the results section to be logically organized. Typically it begins with certain preliminary issues. One is whether any participants or responses were excluded from the analyses and why. The rationale for excluding data should be described clearly so that other researchers can decide whether it is appropriate. A second preliminary issue is how multiple responses were combined to produce the primary variables in the analyses. For example, if participants rated the attractiveness of 20 stimulus people, you might have to explain that you began by computing the mean attractiveness rating for each participant. Or if they recalled as many items as they could from study list of 20 words, did you count the number correctly recalled, compute the percentage correctly recalled, or perhaps compute the number correct minus the number incorrect? A third preliminary issue is the reliability of the measures. This is where you would present test-retest correlations, Cronbach’s α, or other statistics to show that the measures are consistent across time and across items. A final preliminary issue is whether the manipulation was successful. This is where you would report the results of any manipulation checks.

The results section should then tackle the primary research questions, one at a time. Again, there should be a clear organization. One approach would be to answer the most general questions and then proceed to answer more specific ones. Another would be to answer the main question first and then to answer secondary ones. Regardless, Bem (2003) [3] suggests the following basic structure for discussing each new result:

Remind the reader of the research question.
Give the answer to the research question in words.
Present the relevant statistics.
Qualify the answer if necessary.
Summarize the result.

Notice that only Step 3 necessarily involves numbers. The rest of the steps involve presenting the research question and the answer to it in words. In fact, the basic results should be clear even to a reader who skips over the numbers.

The discussion is the last major section of the research report. Discussions usually consist of some combination of the following elements:

Summary of the research
Theoretical implications
Practical implications
Limitations
Suggestions for future research

The discussion typically begins with a summary of the study that provides a clear answer to the research question. In a short report with a single study, this might require no more than a sentence. In a longer report with multiple studies, it might require a paragraph or even two. The summary is often followed by a discussion of the theoretical implications of the research. Do the results provide support for any existing theories? If not, how can they be explained? Although you do not have to provide a definitive explanation or detailed theory for your results, you at least need to outline one or more possible explanations. In applied research—and often in basic research—there is also some discussion of the practical implications of the research. How can the results be used, and by whom, to accomplish some real-world goal?

The theoretical and practical implications are often followed by a discussion of the study’s limitations. Perhaps there are problems with its internal or external validity. Perhaps the manipulation was not very effective or the measures not very reliable. Perhaps there is some evidence that participants did not fully understand their task or that they were suspicious of the intent of the researchers. Now is the time to discuss these issues and how they might have affected the results. But do not overdo it. All studies have limitations, and most readers will understand that a different sample or different measures might have produced different results. Unless there is good reason to think they would have, however, there is no reason to mention these routine issues. Instead, pick two or three limitations that seem like they could have influenced the results, explain how they could have influenced the results, and suggest ways to deal with them.

Most discussions end with some suggestions for future research. If the study did not satisfactorily answer the original research question, what will it take to do so? What new research questions has the study raised? This part of the discussion, however, is not just a list of new questions. It is a discussion of two or three of the most important unresolved issues. This means identifying and clarifying each question, suggesting some alternative answers, and even suggesting ways they could be studied.

Finally, some researchers are quite good at ending their articles with a sweeping or thought-provoking conclusion. Darley and Latané (1968) [4] , for example, ended their article on the bystander effect by discussing the idea that whether people help others may depend more on the situation than on their personalities. Their final sentence is, “If people understand the situational forces that can make them hesitate to intervene, they may better overcome them” (p. 383). However, this kind of ending can be difficult to pull off. It can sound overreaching or just banal and end up detracting from the overall impact of the article. It is often better simply to end when you have made your final point (although you should avoid ending on a limitation).

The references section begins on a new page with the heading “References” centred at the top of the page. All references cited in the text are then listed in the format presented earlier. They are listed alphabetically by the last name of the first author. If two sources have the same first author, they are listed alphabetically by the last name of the second author. If all the authors are the same, then they are listed chronologically by the year of publication. Everything in the reference list is double-spaced both within and between references.

Appendices, Tables, and Figures

Appendices, tables, and figures come after the references. An appendix is appropriate for supplemental material that would interrupt the flow of the research report if it were presented within any of the major sections. An appendix could be used to present lists of stimulus words, questionnaire items, detailed descriptions of special equipment or unusual statistical analyses, or references to the studies that are included in a meta-analysis. Each appendix begins on a new page. If there is only one, the heading is “Appendix,” centred at the top of the page. If there is more than one, the headings are “Appendix A,” “Appendix B,” and so on, and they appear in the order they were first mentioned in the text of the report.

After any appendices come tables and then figures. Tables and figures are both used to present results. Figures can also be used to illustrate theories (e.g., in the form of a flowchart), display stimuli, outline procedures, and present many other kinds of information. Each table and figure appears on its own page. Tables are numbered in the order that they are first mentioned in the text (“Table 1,” “Table 2,” and so on). Figures are numbered the same way (“Figure 1,” “Figure 2,” and so on). A brief explanatory title, with the important words capitalized, appears above each table. Each figure is given a brief explanatory caption, where (aside from proper nouns or names) only the first word of each sentence is capitalized. More details on preparing APA-style tables and figures are presented later in the book.

Sample APA-Style Research Report

Figures 11.2, 11.3, 11.4, and 11.5 show some sample pages from an APA-style empirical research report originally written by undergraduate student Tomoe Suyama at California State University, Fresno. The main purpose of these figures is to illustrate the basic organization and formatting of an APA-style empirical research report, although many high-level and low-level style conventions can be seen here too.

Key Takeaways

An APA-style empirical research report consists of several standard sections. The main ones are the abstract, introduction, method, results, discussion, and references.
The introduction consists of an opening that presents the research question, a literature review that describes previous research on the topic, and a closing that restates the research question and comments on the method. The literature review constitutes an argument for why the current study is worth doing.
The method section describes the method in enough detail that another researcher could replicate the study. At a minimum, it consists of a participants subsection and a design and procedure subsection.
The results section describes the results in an organized fashion. Each primary result is presented in terms of statistical results but also explained in words.
The discussion typically summarizes the study, discusses theoretical and practical implications and limitations of the study, and offers suggestions for further research.
Practice: Look through an issue of a general interest professional journal (e.g., Psychological Science ). Read the opening of the first five articles and rate the effectiveness of each one from 1 ( very ineffective ) to 5 ( very effective ). Write a sentence or two explaining each rating.
Practice: Find a recent article in a professional journal and identify where the opening, literature review, and closing of the introduction begin and end.
Practice: Find a recent article in a professional journal and highlight in a different colour each of the following elements in the discussion: summary, theoretical implications, practical implications, limitations, and suggestions for future research.

Long Descriptions

Figure 11.1 long description: Table showing three ways of organizing an APA-style method section.

In the simple method, there are two subheadings: “Participants” (which might begin “The participants were…”) and “Design and procedure” (which might begin “There were three conditions…”).

In the typical method, there are three subheadings: “Participants” (“The participants were…”), “Design” (“There were three conditions…”), and “Procedure” (“Participants viewed each stimulus on the computer screen…”).

In the complex method, there are four subheadings: “Participants” (“The participants were…”), “Materials” (“The stimuli were…”), “Design” (“There were three conditions…”), and “Procedure” (“Participants viewed each stimulus on the computer screen…”). [Return to Figure 11.1]

Bem, D. J. (2003). Writing the empirical journal article. In J. M. Darley, M. P. Zanna, & H. R. Roediger III (Eds.), The compleat academic: A practical guide for the beginning social scientist (2nd ed.). Washington, DC: American Psychological Association. ↵
Darley, J. M., & Latané, B. (1968). Bystander intervention in emergencies: Diffusion of responsibility. Journal of Personality and Social Psychology, 4 , 377–383. ↵

A type of research article which describes one or more new empirical studies conducted by the authors.

The page at the beginning of an APA-style research report containing the title of the article, the authors’ names, and their institutional affiliation.

A summary of a research study.

The third page of a manuscript containing the research question, the literature review, and comments about how to answer the research question.

An introduction to the research question and explanation for why this question is interesting.

A description of relevant previous research on the topic being discusses and an argument for why the research is worth addressing.

The end of the introduction, where the research question is reiterated and the method is commented upon.

The section of a research report where the method used to conduct the study is described.

The main results of the study, including the results from statistical analyses, are presented in a research article.

Section of a research report that summarizes the study's results and interprets them by referring back to the study's theoretical background.

Part of a research report which contains supplemental material.

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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A hypothesis is a testable prediction about the variables in a study. The hypothesis should always contain the independent variable (IV) and the dependent variable (DV). A hypothesis can be directional (one-tailed) or non-directional (two-tailed).

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How do you write a good hypothesis?

The way to write a good hypothesis is to follow a 3 step proess.

1) Identify your variables and operationalise them.

2) Identify whether you are looking for a difference or a relationship.

3) Identify whether you are going to write a directional or non-directional hypothesis.

As long as your hypothesis includes these three things then it will be a strong statement.

Let's look at a specific example to see how we can do this:

The hypothesis we want to write is for a piece of research which is looking to see if the length of sleep impacts memory.

So let's go to step 1.

1) Our independent variable (which is the variable that we are able to change and manipulate) in this case is the length of sleep , and the dependent variable (which we cannot control but is what we measure) for this piece of research is memory. But now we need to operationalise them. Operationalising variables means explain how we measure the variable. So for example we could operationalise length of sleep to be 'people who slept more than 6 hours in comparison to people who slept less than 6 hours.' You often find that there are many ways to operationalise the dependent variable as something like memory can be measured in many ways. One way which you could operationalise the variable would be 'number of words correctly recalled from a list.'

So now we have both our operationalised variables, we can move on to step two.

2) We need to decide if we are looking for a difference or a relationship. A difference would be if we are directly comparing two things, whereas a relationship would be showing how one thing impacts another. If you are testing for a difference then your hypothesis will sound something like 'group A is more/less/different to group B' whereas if you are testing for a relationship you will say 'A increases/decreases/changes as B increases.' For this piece of research we are comparing people with more than 6 hours of sleep with those who had less than 6 hours of sleep so we are looking for a difference . This means our hypothesis will sound like 'people who sleep more than 6 hours will .... more/less/differently to people who slept less than 6 hours.'

Now we can move onto the final step of writing the hypothesis.

3) A hypothesis can be written as either directional (when you predict what the results will show, and so say 'A will be more than B or A will be less than B') or it can be non-directional (which is when you know that there will be a difference but do not know which one will be more or less so write 'A will be significantly different to B'). You can pick which type of hypothesis you want to write (unless the exam question specifies!) but for this example let's write a directional hypothesis. If we predict that more sleep will improve your memory we would write people who sleep more will have better memories than people who sleep less.

But now let's put everything together and write our final excellent hypothesis.

'People who sleep for more than 6 hours will recall more words correctly from a list than those who slept for less than 6 hours.

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ORIGINAL RESEARCH article

Linking servant leadership to followers' thriving at work: self-determination theory perspective.

$\r\nXiaoqun Jiang$

1 School of Management, Guangxi Minzu University, Nanning, China
2 School of Labor and Human Resources, Renmin University of China, Beijing, China

Previous studies have confirmed that servant leadership has a positive impact on thriving at work, however, the psychological mechanism in this process has not been fully understood. Based on Self-Determination Theory, this study examines the mediating effect of basic psychological needs and the moderating effect of power distance on the relationship between servant leadership and followers' thriving at work. The results from the between-subject experimental design (Study 1) indicate that servant leadership can satisfy followers' three basic psychological needs. And the results from a questionnaire survey of 455 civil servants at two-time points (Study 2) indicate: (1) Servant leadership has a significantly positive impact on followers' thriving at work; (2) All three basic psychological needs satisfaction serve as a mediator in the relationship between servant leadership and followers' thriving at work; (3) Power distance negatively moderates the relationship between servant leadership and the satisfaction of three basic psychological needs, meaning that the lower on the power distance, the stronger the positive relationship between servant leadership and the satisfaction of three basic psychological needs; (4) Power distance negatively moderates the mediating effects of competence needs satisfaction and relatedness needs satisfaction in the relationship between servant leadership and followers' thriving at work, indicating that the lower on the power distance, the stronger the mediating effects. Our findings highlight the important role of servant leadership in fostering followers' thriving at work and explore the critical role of basic psychological needs satisfaction. This provides empirical evidence to further refine theories regarding thriving at work, and suggests that in order to promote employee thriving, it is important to guide leaders to reevaluating and repositioning their roles.

1 Introduction

In today's turbulent economic and business environment, organizations need to rely on thriving employees to maintain competitive advantage and achieve organizational goals ( Cao et al., 2022 ; Goh et al., 2022 ; Rahaman et al., 2022 ). Thriving at work is a two-dimensional concept, characterized as the positive psychological state in which individuals jointly experience vitality and learning ( Anand et al., 2018 ). Vitality belongs to the affective dimension, which refers to the feeling that an individual is energetic, enthusiastic and interested in work; Learning belongs to the cognitive dimension, which refers to the sense that an individual is acquiring and applying valuable knowledge and skills ( Spreitzer et al., 2005 ). Thriving at work shares similarities with work engagement, but there are notable distinctions. Both concepts involve energy components, with vitality associated with thriving and vigor with work engagement. However, thriving at work incorporates a distinct element of learning, emphasizing individual growth and development within the workplace. In contrast, work engagement centers on the cognitive and emotional connection between individuals and their work, focusing on absorption in tasks and dedication to job responsibilities ( Schaufeli and Bakker, 2010 ; Niessen et al., 2012 ). Much has been written about the importance of thriving at work in the academic literature ( Walumbwa et al., 2018 ; Rego et al., 2021 ). Thriving employees can actively adapt to physical, mental and social adversity with vibrant growth, and they can generate new resources instead of consuming the existing resources ( Spreitzer et al., 2012 ). A meta-analysis found that thriving at work leads to positive employee outcomes ( Kleine et al., 2019 ), including individual health and growth (e.g., physical and mental health, job burnout, career development), work attitude (e.g., organizational commitment, turnover intention) and performance-related outcomes (e.g., work performance, innovation, organizational citizenship behavior). Given its critical role in sustaining individuals and organizations, understanding the antecedents of thriving at work becomes important in fostering sustainable success.

Thriving at work exhibits characteristics of social embeddedness, with vitality and learning deeply embedded in the individual's social systems. Self-development occurs through dynamic interactions with others, particularly interactions with leaders ( Spreitzer et al., 2005 ). Leadership is one of the important antecedents of thriving at work, yet remains understudied. With societal advancements, employees in the workplace not only seek economic rewards but also exhibit pursuits for higher-level mental needs. They prefer leadership styles that are more humane and hope to receive more care, assistance, and support from their leaders ( Li and Mao, 2018 ). Servant leadership is an altruistic leadership style, which has attracted intense attention from scholars and managers in recent years ( Eva et al., 2019 ; Schowalter and Volmer, 2023 ). Servant leaders do not appear superior to others, they are friendly and prioritize the interests of employees, ultimately achieving organizational goals by fostering followers' growth and development ( Liden et al., 2008 ; van Dierendonck, 2011 ; Eva et al., 2019 ; Liao et al., 2021 ). The meta-analysis by Eva et al. (2019) systematically examined research findings of servant leadership. Specifically, previous studies have explained the influence mechanism of servant leadership mainly from the theoretical perspectives of social exchange theory, social identity theory, social learning theory, resource conservation theory and attribution theory. It is confirmed that servant leadership has significant effects on followers' key work attitudes (such as job engagement, job satisfaction, job meaning and turnover intention, etc.), important organizational behaviors (such as organizational citizenship behavior, innovation behavior, knowledge sharing, helping behavior, initiative behavior, voice behavior, etc.), work performance and wellbeing ( Eva et al., 2019 ).

Although prior studies have evidenced the effectiveness of servant leadership in promoting followers' thriving at work ( Walumbwa et al., 2018 ; Sheikh et al., 2019 ; Xu and Wang, 2020 ). However, limited research attention has been paid to the underlying psychological mechanisms through which servant leaders affect followers' thriving at work. According to self-determination theory (SDT), humans have three basic psychological needs: autonomy, competence and relatedness, and individuals tend to develop in a positive direction when these needs are met ( Deci and Ryan, 2000 ). Spreitzer and Porath (2013) further integrated SDT with the concept of thriving at work, proposing an Integrative Model of Human Growth at Work. This model emphasizes the importance of satisfying the three basic psychological needs for individual development and growth, considering them essential nutrients for human flourishing ( Spreitzer and Porath, 2013 ). Servant leadership theory places a greater emphasis on attending to the needs of followers than any other leadership theory ( van Dierendonck et al., 2014 ). Therefore, based on SDT, this study employs the satisfaction of basic psychological needs as the mediating variable to elucidate how servant leadership influences followers' thriving at work by satisfying their three basic psychological needs, respectively.

Beyond the proposed mediating effects, this study also aims to investigate the boundary condition of the relationship, offering further insight into the underlying mechanism. In fact, how followers perceive and interpret leadership behavior is a crucial influencing factor in leadership effectiveness. Previous research on leadership has underpinned the moderating role of power distance in leadership effectiveness ( Lian et al., 2012 ; Anand et al., 2018 ). Given that power is inherent in hierarchical organizations, and is fundamental to all relationships, employees' perception of power can impact various organizational management processes and outcomes ( Daniels and Greguras, 2014 ). Therefore, we consider power distance as the moderating variable, examining its influence on the effectiveness of servant leadership in fostering thriving at work.

This research contributes to the literature in the following aspects: firstly, based on the social embedding characteristics of thriving at work, we explore the leadership factors that affect followers' thriving at work. This enriches the understanding of the antecedents of thriving at work. Secondly, while prior studies have explored how servant leaders meet the basic psychological needs of their followers, they have not extended to thriving at work ( van Dierendonck et al., 2014 ; Chiniara and Bentein, 2016 ). Our study bridges this gap by examining the mediating effect of basic psychological needs between servant leadership and thriving at work from the perspective of self-determination theory. Different from other research perspectives (e.g., social exchange theory, social learning theory, attribution theory), SDT captures servant leadership's core tenet of “prioritizing followers' needs” ( van Dierendonck et al., 2014 ). Thus, this study expands the understanding of the psychological mechanism through which servant leadership influences followers' thriving at work. Thirdly, there has been a dearth of experimental research design in the realm of servant leadership studies. In response to recent calls by Eva et al. (2019) and Schowalter and Volmer (2023) , we adopted the situational experiment method in study 1 by utilizing a scenario-based experiment design to examine the causal relationship between servant leadership and psychological needs satisfaction. Finally, we identify unique boundary conditions around the relationships between servant leadership and thriving at work. These findings provide valuable managerial insights for promoting workplace thriving.

2 Theory and hypotheses

2.1 servant leadership and follower thriving at work.

Servant leadership is a holistic leadership style that is “people-centered” but does not ignore performance expectations ( Eva et al., 2019 ; Schowalter and Volmer, 2023 ). Leaders advocate the belief of “service” and demonstrate personality traits such as modesty, authenticity, and conscientiousness, they care about the needs of their followers, actively share resources, provide guidance, and empower their followers ( van Dierendonck, 2011 ). A recent meta-analysis reveal that servant leadership can lead to several important employee outcomes, including follower attitudes, behaviors, and performance, as well as team and organizational outcomes ( Eva et al., 2019 ). Compared with transformational, authentic, and ethical leadership, servant leadership show greater predicative capability across many outcomes ( Eva et al., 2019 ; Lee et al., 2019 ; Schowalter and Volmer, 2023 ). Leaders perceive themselves as service providers, satisfying followers' needs and helping them in their development and success. Thus, servant leaders are increasingly favored by employees ( Macedo et al., 2022 ).

A core assumption of thriving at work is that high levels of both vitality and learning need to be achieved for employees to thrive ( Kleine et al., 2019 ). Scholars posit that while both dimensions of vitality and learning can represent self-growth and personal development in the workplace to some extent, the experience of thriving occurs when these two dimensions mutually reinforce each other ( Porath et al., 2012 ). Thriving at work exhibits social embedding features, and unlike an enduring personality trait, it is a temporary psychological state that can be shaped by the environment ( Porath et al., 2012 ; Van der Walt, 2018 ). Previous research has pointed out that leadership, leader-follower relationships, and organizational practices are associated with employees' thriving at work ( Ren et al., 2022 ). Thriving at work is conceptualized as a continuum, where individuals experience more or less thriving at any point in time ( Spreitzer et al., 2005 ; Spreitzer and Porath, 2013 ). As proposed by Deci and Ryan (2000) , all individuals have an inherent tendency to pursue growth and development, but the success of this pursuit depends on environmental factors. A meta-analysis has revealed that positive leadership factors, such as supportive leader behaviors, empowerment, and high-quality leader-member exchanges, serve as relational resources for employees to thrive ( Kleine et al., 2019 ). Servant leaders develop followers by prioritizing followers' work needs and interests to achieve organizational sustainability ( Jaiswal and Dhar, 2017 ; Macedo et al., 2022 ). We posit that servant leaders are humane and wise leaders with a long-term outlook. They focus their efforts on promoting employee thriving to achieve long-term organizational success.

Firstly, servant leaders exhibit traits of altruism and moral reasoning, prioritizing the needs and interests of followers over self-interest. As a result, followers experience an increased sense of growth, empowerment and wellbeing ( Lee et al., 2019 ). When followers feel that their leaders prioritize their growth and wellbeing, they develop higher levels of psychological safety and organizational esteem. This, in turn, fosters their vitality and enhances their work engagement and efficiency ( Bao et al., 2018 ; Walumbwa et al., 2018 ; Eva et al., 2019 ). Moreover, servant leaders embrace a “service-first” mindset ( Eva et al., 2019 ), demonstrating care for both the work and lives of followers and providing support. These supportive behaviors help to enhancing the sense of work meaningfulness ( Jang et al., 2022 ), balancing work and family relationships ( Tang et al., 2016 ; Russo et al., 2018 ; Ren et al., 2022 ), and alleviating burnout ( Tang et al., 2016 ). Therefore, servant leadership nurtures followers' vitality by meeting their psychological needs of followers.

Secondly, servant leaders create better career development prospects for followers by offering guidance, feedback and work resources to create opportunities for followers to acquire new knowledge, and develop new skills and abilities ( Walumbwa et al., 2010 ). They cultivate a collaborative and participative work environment where leaders and followers jointly tackle difficulties through continuous learning ( Sheikh et al., 2019 ). Furthermore, servant leaders promote empowerment, innovation and future-oriented thinking, meanwhile, encouraging followers to find ways to enhance work performance ( Chen et al., 2015 ; Walumbwa et al., 2018 ). These management practices contribute to maintaining a continuous learning environment among followers.

Several empirical studies in recent years have confirmed the positive relationship between servant leadership and thriving at work. For example, Jaiswal and Dhar (2017) , Walumbwa et al. (2018) , and Sheikh et al. (2019) all found that servant leadership is significantly positively correlated with followers' thriving at work. Xu and Wang (2020) found that the developmental and social-emotional support provided by servant leaders helps to establish high-quality team-member exchange relationships, consequently fostering thriving at work among team members. Jang et al. (2022) found that servant leadership promotes thriving at work by enhancing followers' work meaningfulness and workplace spirit. Therefore, we hypothesize that:

Hypothesis 1: Servant leadership relates positively to followers' thriving at work .

2.2 The mediating role of basic psychological need satisfaction

According to SDT, people have three basic psychological needs: autonomy, competence, and relatedness. These basic psychological needs are the innate, inherent and necessary “lack” of human individuals ( Deci and Ryan, 2000 ). Fundamental to the theory is the principle that various environmental factors can impact the development, growth and health of an individual through the satisfaction of basic psychological needs. Although dispositional differences exist in the strength of these needs, however, SDT asserts that it is the degree to which psychological needs are met, rather than their strength, that determines and shapes personal growth, integrity, and wellbeing ( Chiniara and Bentein, 2016 ). The three basic psychological needs are structurally independent, and the satisfaction of each need can contribute positively to an individual's personal growth and development. Deci et al. (2017) proposed that the influence of various workplace background factors on employee motivation and experience is also mediated by the satisfaction of three basic psychological needs. These basic needs include employees' needs for competence or effectance, relatedness or belongingness and autonomy or self-determination in their work. Servant leadership theory is built on the core assumption that servant leaders focus on meeting followers' needs and establishing a long-term and stable relationship with followers, thereby, influencing organizational outcomes by promoting employees' growth and wellbeing ( Liden et al., 2008 ). Therefore, we need to further explore the relationship between servant leadership and the satisfaction of each psychological need.

Autonomy is considered a prominent need that is fundamental to intrinsic motivation ( Chiniara and Bentein, 2016 ). In the workplace, the satisfaction of employees' autonomy needs mainly comes from two aspects: first, the free will they can experience at work, that is, the feeling of psychological freedom. Second, the freedom to choose how tasks are completed and the ability to work in their own way ( Van den Broeck et al., 2016 ; van Hooff and De Pater, 2019 ). Servant leadership effectively meets followers' autonomy needs in the following ways: Firstly, servant leaders are forward-thinking managers; they value the intrinsic worth and potential development of followers, respecting their emotions, interests, perspectives, and opinions. They aim to nurture followers' independence and develop a sense of autonomy. This managerial mindset allows the followers to experience more autonomous growth. Secondly, servant leaders advocate power sharing and granting autonomy with the intent of providing followers with opportunities to act independently and make their own decisions ( Liden et al., 2008 ; Newman et al., 2017 ). When followers perceive their actions as autonomous and self-determined, they experience a heightened sense of autonomy. Thirdly, humility is a fundamental pillar of servant leadership ( Sousa and van Dierendonck, 2017 ; Van Dierendonck et al., 2017 ). By taking humble positions as servants to followers and respecting them as equal partners rather than exerting command and control, servant leaders can foster mutual trust. This, in turn, creates a reciprocal relationship between leader and followers. Followers are more likely to understand and respect their leaders and experience a greater sense of psychological freedom.

Competence refers to an individual's need to interact effectively with others and have opportunities to use their talents ( Deci and Ryan, 2000 ; Chiniara and Bentein, 2016 ; van Hooff and De Pater, 2019 ). At work, employees hope to tackle challenging tasks and achieve desired outcomes. When their competence needs are satisfied, employees experience a sense of accomplishment ( Deci et al., 2001 ). “Helping followers to grow and succeed” is considered one of the important dimensions of servant leadership ( Liden et al., 2008 ). Servant leaders help followers to grow and succeed by actively developing their abilities and providing opportunities for skill improvement or acquiring new ones. They demonstrate genuine concern for followers' career growth and development, they understand followers' interests, abilities, and career goals ( Chiniara and Bentein, 2016 ). Understanding and prioritizing followers' needs enables servant leaders to best match their interests, abilities and goals with their work, enabling them to leverage their abilities and realize their value, and guide them toward optimal career development paths. Moreover, servant leaders offer sufficient autonomy, expressing confidence in followers' ability to excel in their roles. This trust fosters a greater sense of control and competence among followers. A recent study has found a significant positive impact of servant leadership on followers' innovation self-efficacy ( Iqbal et al., 2022 ).

Relatedness refers to the need for connection and maintaining intimacy with others ( van Hooff and De Pater, 2019 ). In organizations, employees seek connection with others, longing to experience care and support, as well as a sense of belonging within their work teams or organization. Servant leaders are relationship-oriented and altruistic-oriented. They demonstrate altruistic sensitivity to the difficulties, concerns, needs and interests of their followers, and interact with them in an open, fair and trustworthy manner. Leaders place particular emphasis on positive emotional communication with followers, offering them emotional support, thereby establishing a long-term, trustworthy, dyadic interaction ( Schaubroeck et al., 2011 ; Özkan et al., 2023 ). The altruistic mindset of servant leaders, prioritizing followers' interests over self-interests, enhances followers' psychological safety in organizational interpersonal relationships ( Jiang and Li, 2020 ). These supportive behaviors foster a stronger sense of connection for followers with their leader and work teams, and strengthen their sense of belonging within the organization. In addition, servant leaders emphasize a spirit of service that not only to followers, but also to the organization, and other organizational stakeholders ( Lemoine et al., 2019 ). As a result, servant leaders may cultivate positive relationships with followers, who, in turn, reciprocate by engaging in more proactive behaviors.

According to SDT, basic psychological needs satisfaction is the underlying mechanism that motivates and guides people's behavior, and is “necessary for an individual to function effectively and healthily” ( Deci and Ryan, 2000 ). Followers are more likely to experience thriving when their work environment meets their three basic psychological needs ( Deci and Ryan, 2008 ). The relationship between basic psychological needs satisfaction and thriving at work has been empirically supported. Spreitzer and Porath found that the satisfaction of three basic psychological needs, which together explained 54% of the variance of thriving at work, with each need's satisfaction independently predicting thriving at work ( Spreitzer and Porath, 2013 ). Therefore, we hypothesize the following:

Hypothesis 2a: autonomy need satisfaction mediates the relationship between servant leadership and thriving at work;

Hypothesis 2b: competence need satisfaction mediates the relationship between servant leadership and thriving at work;

Hypothesis 2c: relatedness need satisfaction mediates the relationship between servant leadership and thriving at work .

2.3 The moderating role of power distance

In social interactions, individuals often assess their own status and power in comparison to others, thereby shaping their perceptions of fairness in power distribution. Under different cultural backgrounds, individuals differ in the extent to which they accept inequality in power. This acceptance of power distribution is termed “power distance” and is considered a cultural value. At the individual level, power distance denotes one's acceptance of inequalities in power within institutions and organizations ( Clugston et al., 2000 ). Recognized as a cornerstone in relationships, power distance can significantly affect many organizational processes and outcomes ( Daniels and Greguras, 2014 ; Anand et al., 2018 ). Individuals higher on power distance perceive distinctions based on power or hierarchical positions, and believe that organizational authority should be respected, showing loyalty and obedience to leaders. Whereas individuals lower on power distance view leaders and followers as having equal status, and that everyone in the organization should have the right to express opinions and participate in decision-making ( Daniels and Greguras, 2014 ).

From the perspective of leader-follower fit, leadership effectiveness may be enhanced when the leadership style aligns with the followers' power distance orientation. Servant leadership is a “bottom-up” managerial approach wherein leaders respect followers' advice and opinions, and help followers to grow and succeed. Most importantly, servant leaders do not show superiority and place the satisfaction of followers' needs over their own. These leader behaviors may deviate from the expectations and perceptions that followers higher on power distance have for their leaders. They may feel that is not a true trait of a leader, so they may act more cautiously in their interactions with servant leaders and try to maintain a more prudent distance from them. In this sense, for followers higher on power distance, the effect of servant leadership on the satisfaction of followers' psychological needs may be diminished. In contrast, followers lower on power distance are more willing to establish a close relationship with their leader, expecting support in career development and work-family balance from leaders. The traits and behaviors of servant leaders are thus more likely to satisfy their basic psychological needs. Therefore, we hypothesize that:

Hypothesis 3a: Power distance negatively moderates the relationship between servant leadership and autonomy need satisfaction;

Hypothesis 3b: Power distance negatively moderates the relationship between servant leadership and competence need satisfaction;

Hypothesis 3c: Power distance negatively moderates the relationship between servant leadership and relatedness need satisfaction .

Combining Hypothesis 2 and Hypothesis 3, this study further anticipates that power distance will negatively moderate the mediating role of basic psychological needs. That is, the mediating effect of basic psychological needs satisfaction in the relationship between servant leadership and thriving at work is expected to be moderated by power distance. A lower power distance is expected to strengthen the positive relationship between servant leadership and thriving at work through the mediating role of basic psychological needs satisfaction. Conversely, a higher power distance is expected to weaken the relationship through the mediating role. We therefore propose the following hypotheses:

Hypothesis 4a: Power distance negatively moderates the mediating effect of autonomy need satisfaction;

Hypothesis 4b: Power distance negatively moderates the mediating effect of competence need satisfaction;

Hypothesis 4c: Power distance negatively moderates the mediating effect of relatedness need satisfaction .

All hypotheses were combined into one comprehensive research model ( Figure 1 ).

Figure 1 . Hypothesized model.

3.1 Participants and design

Sixty-six students enrolled in the MBA course at a business school in China's university voluntarily participated in the study. The sample comprised 30 males and 36 females, with a mean age of 32.29 years (SD = 4.26). The participants were randomly assigned to one of two experimental scenarios using a between-subject design: (1) high servant leadership; (2) low servant leadership (n= 33 for each group). Then, participants provided demographic information, read the material, and were instructed to imagine their supervisor as described in the material. Following this, participants answered questions about how well their basic psychological needs would be satisfied while working with their immediate supervisor.

3.2 Experimental materials

We developed the materials based on the behavioral description of servant leadership by Eva et al. (2019) and the relevant script developed by van Dierendonck et al. (2014) .

High servant leadership is described as follows:

He/she has often assisted you with your work and life .

He/she is a person of humility, integrity, honesty and sincerity, and shares his/her thoughts and feelings with you .

He/she constantly listened to your opinions, and did not take one employees' side over another .

He/she tolerates mistakes, and provides freedom so you can develop your own abilities .

He/she values ethical standards and emphasizes that it is more important to do the right thing than looking good in front of your workmates .

He/she shows great humanity, and understanding of your personal needs and standpoint .

Low servant leadership is described as follows:

He/she rarely assists you with your work and life .

He/she is proud, hypocritical and rarely shares his/her thoughts and feelings .

He/she rarely listens to you and sometimes favor one employees' side over another..... .

He/she is not allowed to make mistakes and must do the job according to his/her requirements and manner .

He/she emphasizes how to achieve a set goal, regardless of whether it violates ethical standards .

He/she is not very human, does not care about your personal needs, and does not understand your position .

3.3 Measures

After reading the material, participants completed a survey. Basic psychological needs satisfaction was measured using the La Guardia et al. (2000) scale. The scale contains three dimensions: autonomy satisfaction, competence satisfaction and relatedness satisfaction, with each dimension comprising three items. Sample items include “When I am with my supervisor, I feel free to be who I am”, “When I am with my supervisor, I feel like a competent person” and “When I am with my supervisor, I feel closeness and intimacy”. The Cronbach's α of the total scale is 0.924, and the Cronbach's α of the three dimensions is 0.861, 0.742, and 0.813 respectively. All items were measured on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree).

3.4 Results

We conducted a one-way analysis of variance (ANOVA), and the results showed that the main effect of servant leadership on the satisfaction of the three basic psychological needs was significant. The satisfaction of followers' autonomy needs [F (1, 74) = 203.310, p < 0.001, η 2 = 0.733] in high servant leadership condition (M = 16.553, SD = 2.638) are significantly higher than that under low servant leadership condition (M = 7.42, SD = 2.937);competence needs [F (1, 74) = 47.911, p < 0.001, η 2 = 0.393] in high servant leadership condition (M = 16.316 SD = 2.682) are significantly higher than that under low servant leadership condition (M = 10.711, SD = 4.210); relatedness needs [F (1, 74) = 91.315, p < 0.001, η 2 = 0.552] in high servant leadership condition (M = 12.078, SD = 2.148) are significantly higher than that under low servant leadership condition (M = 7.395, SD=2.125). Hence, Hypothesis 2a, Hypothesis 2b and Hypothesis 3c are supported.

In sum, the results of Study 1 show that servant leadership is positively related to the satisfaction of followers' basic psychological needs. In order to further examine the impact of basic psychological need satisfaction on thriving at work and the mediating role of basic psychological need satisfaction between servant leadership and thriving at work, we conducted study 2. To improve the external validity, Study 2 will reexamine the conclusions of Study 1 through a questionnaire survey, and further examine other hypotheses.

4.1.1 Sample and procedure

The researchers contacted 50 civil servants working in government institutions across 25 provinces and municipalities in China (e.g., Beijing, Shandong, Hebei, Guangxi, and Yunnan), and asked them to help collect data in their organization.

To reduce common method bias, we collected data from paper-based and web-based surveys simultaneously at two-time points. At Time 1, a total of 634 civil servants took part in the survey and rated on the Servant Leadership and Basic Psychological Needs Satisfaction Scale. One month later, the same participants rated on Power Distance and Thriving at Work Scales. We matched the data from the two surveys and screened out the ineligible data, resulting in 455 valid records at two-time points, and the effective response rate was 71.77%.

To ensure the data from the two surveys can be accurately matched, the contacts were required to make a record of questionnaire distribution at the first round. In specific, the paper-based survey was matched according to the questionnaire number, and the contacts distributed surveys centrally and collected them on the spot. The web-based survey was completed through a mobile social networking platform (i.e., Wechat). The contacts forwarded the questionnaire link to the participants via WeChat and matched their questionnaire according to their WeChat ID number.

Of the total sample respondents, 54.7% were male and 45.3% were female. With regard to age, 33.6% were under 30, 45.8% were between 31 and 40, 18.0% were between 41 and 50, and 2.6% were over 50. In terms of administrative rank, clerks account for 44.4%, deputy section chiefs 19.1%, section chiefs 24.0%, deputy department heads 7.0%, and department heads 5.5%. In terms of academic qualifications, 5.1% were junior college-educated or lower educated, 61.5% hold bachelor's degrees, 30.7% hold master's degrees and 2.7% hold doctoral degrees.

4.1.2 Measures

4.1.2.1 servant leadership.

Servant leadership was measured using the scale developed by Liden et al. (2015) . This scale is the shortened version of the Servant Leadership Scale developed by Liden et al. (2008) . The scale consists of seven items, a sample item includes “My leader puts my best interests ahead of his/her own”. Each item was rated on a 7-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree). In this study, Cronbach's α of this scale was 0.870.

4.1.2.2 Basic psychological needs satisfaction

Basic psychological needs satisfaction was measured using the Work-related Basic Need Satisfaction Scale (W-BNS) developed by Van den Broeck et al. (2010) . The scale consists of three different sub-scales: autonomy satisfaction, competence satisfaction and relatedness satisfaction, each of which contains 6 items. We remove one item whose CITC value is < 0.5 on the autonomy satisfaction scale, leaving five items. A sample item for autonomy satisfaction includes “I feel free to do my job the way I think it could best be done”. We have reserved 6 items of the competence satisfaction scale, and its sample item includes “I feel competent at my job”. We removed one item with a CITC value below 0.5 on the relatedness satisfaction scale, leaving five items. A sample item for relatedness satisfaction includes “Some people I work with are close friends of mine”. Each item was rated on a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). In this study, Cronbach's α were 0.818, 0.853, and 0.785 respectively.

4.1.2.3 Power distance

The scale used to capture the followers' perceptions of power was obtained from Dorfman and Howell (1988) . The scale consists of five items, a sample item includes “Leaders should make most decisions without consulting followers”. Each item was rated on a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). In this study, Cronbach's α of this scale was 0.755.

4.1.2.4 Thriving at work

Participants reported their thriving at work using the scale developed by Porath et al. (2012) . This scale consists of ten items, five of which assess the individuals' state of learning (e.g., “I find myself learning often”), while the other five items assess the individuals' vitality (e.g., “I feel alive and vital”). Each item was rated on a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). In this study, Cronbach's α of this scale was 0.907.

4.1.2.5 Control variables

Previous studies have found that age, position and educational background are related to thriving at work to a certain extent ( Kleine et al., 2019 ). In addition, women are more likely to feel tired and less energetic than men ( Niessen et al., 2012 ; Jiang et al., 2019 ). Therefore, demographic variables such as gender, age, administrative level and education were used as control variables in this study.

4.2 Results

This study conducted statistical analyses on the data using SPSS 24.0 and Mplus 8.0. First, Mplus 8.0 was adopted for confirmatory factor analysis. Second, descriptive statistics and correlation analysis were conducted using SPSS 24.0. Third, hypothesis testing was performed using the PROCESS in SPSS.

4.2.1 Confirmatory factor analyses

We used Mplus 8.0 to conduct confirmatory factor analysis to examine the discriminant validity of the study variables (servant leadership, autonomy need satisfaction, competence need satisfaction, relatedness need satisfaction, power distance, and thriving at work). The results are shown in Table 1 . The fitting index of the six-factor model is the best compared to other models (χ 2 /df = 2.360, CFI = 0.907, TLI = 0.897, RMSEA = 0.055, SRMR = 0.059). The fitting index for the four-factor, three-factor, two-factor, and single-factor models are not ideal, and each of them decreases as the number of factors decreases. Therefore, the results confirm the good discriminant validity of our study variables.

Table 1 . The result of confirmatory factor analyses.

4.2.2 Common method bias

To minimize the impact of common method bias, we collected data at two-time points and exercised strict procedural controls during the investigation. We used Harman's single-factor test to assess common method bias. The results showed that the highest single factor contributed was 31%, less than the 40% cut-off value, suggesting no CMV in the data. Moreover, we also employed the common latent factor technique by Podsakoff et al. (2003) . The results show that when a method factor is added to the six-factor model (χ 2 /df = 2.505, CFI = 0.896, TLI = 0.886, RMSEA = 0.058, SRMR = 0.067), the model fitting index decreases (ΔCFI = −0.011, ΔTLI = −0.011, ΔRMSEA = 0.003, ΔSRMR = 0.008). This further indicates that the common method bias is no threat to this study.

4.3 Descriptive statistics

The mean, standard deviation and correlation of the study variables are presented in Table 2 . Servant leadership was significantly positively correlated with autonomy need satisfaction, competence need satisfaction, relatedness need satisfaction, and thriving at work ( r = 0.593, p < 0.001; r = 0.248, p < 0.001; r = 0.527, p < 0.001; r = 0.593, p < 0.001; r = 0.494, p < 0.001), autonomy need satisfaction, competence need satisfaction, and relatedness need satisfaction were significantly positively correlated with thriving at work ( r = 0.549, p < 0.001; r = 0.592, p < 0.001; r = 0.577, p < 0.001), power distance was significantly negatively correlated with servant leadership, autonomy need satisfaction, competence need satisfaction, relatedness need satisfaction and thriving at work ( r = −0.323, p < 0.001; r = 0.298, p < 0.001; r = −0.271, p < 0.001; r = −0.393, p < 0.001; r = −0.327, p < 0.001).

Table 2 . Means, standard deviations, and correlations among the variables.

4.4 Results of proposed hypotheses

We used the PROCESS in SPSS 24.0 and selected Model 4 to test the mediating effect with servant leadership as the independent variable, thriving at work as the dependent variables, autonomy, competence and relatedness need satisfaction as the mediating variables, while incorporating gender, age, administrative level and education as control variables. As shown in Model 4 in Table 3 , servant leadership has a significant positive predictive influence on thriving at work (β = 0.461, p < 0.001). Hypothesis 1 is supported. When servant leadership and the satisfaction of the three basic psychological needs simultaneously predict thriving at work (Model 5 in Table 3 ), the satisfaction of the three basic psychological needs has a significant positive influence on thriving at work (β = 0.179, p < 0.001; β = 0.353, p < 0.001; β = 0.193, p < 0.001), while the direct effect of servant leadership is still significant (β = 0.176, p < 0.001). The findings suggest that the satisfaction of three basic psychological needs partially mediates the relationship between servant leadership and thriving at work. Hypothesis 2 is supported.

Table 3 . Hierarchical regression: the mediating effect of servant leadership on thriving at work.

To further clarify the mediating effect, we performed a Bootstrap test. Data analysis results show that the total effect of servant leadership on thriving at work is 0.461, with a 95% confidence interval of [0.380, 0.541]. Table 4 shows the results of direct effect, indirect effect, and difference comparison. The mediation effect index of autonomy need satisfaction is 0.108, the 95% confidence interval is [0.050, 0.166], excluding 0; the mediation effect index of competence need satisfaction is 0.078, and the 95% confidence interval is [0.039, 0.125], excluding 0; the mediating effect index of relatedness need satisfaction is 0.099, and the 95% confidence interval is [0.054, 0.148], excluding 0. The findings suggest that the mediating effect of satisfaction of the three basic psychological needs is significant. The direct effect accounted for 38.18% of the total effect. The mediating effects of autonomy, competence and relatedness needs satisfaction accounted for 23.43%, 16.91% and 21.48% of the total effect respectively. Moreover, we compared the differences in the mediating effects of the satisfaction of the three psychological needs. The findings show that the confidence intervals of the difference coefficients all contain 0, indicating that there is no significant difference between the mediating effect indexes of the three psychological needs satisfaction.

Table 4 . Decomposition of mediation effects.

4.5 Moderated mediation effects

We used Model 7 of the PROCESS program to test moderating effects and moderated mediating effects. Controlling for gender, age, administrative level and education, we used servant leadership as the independent variable, power distance as the moderating variable, autonomy, competence and relatedness need satisfaction as the mediating variables, and thriving at work as the dependent variables to test the moderated mediating effects of the first half of the model path.

As Model 1–3 shown in Table 5 , the interaction term of servant leadership and power distance has a significant negative impact on the satisfaction of autonomy needs, competence needs and relatedness needs (β = −0.074, p < 0.001; β = −0.124, p < 0.001; β = −0.095, p < 0.001). This result indicates that H3a, H3b, and H3c are supported. We conducted simple slope analyses and plotted three simple slope graphs ( Figures 2 – 4 ). For followers lower on power distance, servant leadership significantly predicted their autonomy, competence, and relatedness needs satisfaction (β = 0.649, p < 0.001; β = 0.301, p < 0.001; β = 0.544, p < 0.001). For followers higher on power distance, servant leadership can also significantly predict their autonomy and relatedness needs satisfaction (β = 0.502, p < 0.001; β = 0.355, p < 0.001), except for competence needs satisfaction (β = 0.053, p > 0.05).

Table 5 . Hierarchical regression: the moderation effects of power distance.

Figure 2 . The moderating effect of power distance on servant leadership and autonomous need satisfaction.

Figure 3 . The moderating effect of power distance on servant leadership and competence need satisfaction.

Figure 4 . The moderating effect of power distance on servant leadership and relatedness need satisfaction.

Then, in order to examine the moderating effect of power distance on the mediating effect of the satisfaction of three basic psychological needs respectively, we conducted a conditional mediating effects analysis. Results are shown in Table 6 . In the path of Servant Leadership → Satisfaction of Autonomy Needs → Thriving at work, the moderated mediation effect value is −0.013, with a 95% confidence interval of [-0.031, 0.000], including 0. This indicates that the moderated mediation effect in this path is not significant. Therefore, Hypothesis 4a is not supported.

Table 6 . Results for conditional indirect effect analysis.

In the path of Servant Leadership → Satisfaction of Competence Needs → Thriving at work, the moderated mediation effect value is −0.044, with a 95% confidence interval of [−0.083, −0.009], excluding 0. This indicates that the mediating effect of competence needs satisfaction is negatively moderated by power distance. For followers lower on power distance, competence needs satisfaction mediates the relationship between servant leadership on thriving at work (mediation effect value: 0.106, 95% confidence interval: [0.056, 0.162]). In contrast, for followers higher on power distance, the mediated effect of servant leadership through competence needs satisfaction on thriving at work is not significant (mediation effect value: 0.019, 95% confidence interval: [−0.037, 0.075]). Additionally, the group difference comparison results show a significant difference between the two, with a difference of −0.088 and a 95% confidence interval of [−0.167, −0.017], not including 0. Therefore, Hypothesis 4b is supported.

In the path of Servant Leadership → Satisfaction of Relatedness Needs → Thriving at work, the moderated mediation effect value is −0.018, with a 95% confidence interval of [−0.038, −0.002], excluding 0. This indicates that the mediating effect of relatedness needs satisfaction between servant leadership and thriving at work is negatively moderated by power distance. For followers lower on power distance, relatedness needs satisfaction mediates the relationship between servant leadership on thriving at work (mediation effect values of 0.105, 95% confidence interval: [0.056, 0.160]). Similarly, for followers higher on power distance, relatedness needs satisfaction mediates the relationship between servant leadership on thriving at work (mediation effect values of 0.069, 95% confidence interval: [0.034, 0.111]). Moreover, the group difference comparison results show a significant difference between the two, with a difference of −0.037 and a 95% confidence interval of [−0.076, −0.005], excluding 0. Therefore, Hypothesis 4c is supported.

5 Discussion

While prior research has discussed the various impact of leadership styles on thriving at work ( Niessen et al., 2017 ; Xu and Wang, 2020 ), limited research attention has been given to servant leadership. Our findings highlight the important role of servant leadership in fostering followers' thriving at work, consistent with previous studies by Chen et al. (2015) and Sheikh et al. (2019) . Similarly, our findings also support the viewpoint of scholars like Spreitzer et al. (2005) , suggesting that thriving at work requires more than just eliminating or reducing stressors; instead, it necessitates the introduction of favorable contextual factors in the workplace. Servant leadership is a type of leadership that is committed to achieving organizational thriving through employee thriving. Leaders constantly meet the various needs of their followers, helping them grow and develop new knowledge toward organizational goals ( Macedo et al., 2022 ), and organizations can sustain long-term thriving.

We provide empirical evidence that servant leadership influences followers' thriving at work through a multiple mediation pathway, including the satisfaction of autonomy, competence, and relatedness needs. The social embeddedness model and the self-growth integration model, grounded in self-determination theory, posit that one important psychological mechanism between leadership behavior and thriving at work is the satisfaction of basic psychological needs ( Spreitzer et al., 2005 ; Spreitzer and Porath, 2013 ). Employees are more likely to thrive when the work environment facilitates the satisfaction of employees' autonomy, competence, and relatedness needs ( Deci and Ryan, 2008 ). In managerial practices, leaders often prioritize followers' contributions and value to the organization, overlooking the significance of psychological needs satisfaction and the ways to meet these needs. This study empirically supported the mediating role of the three basic psychological needs satisfaction in the relationship between servant leadership and followers' thriving at work, and highlighting servant leadership's contribution to meeting these needs. The comparative analysis further reveals no significant differences in the mediating effects of the three psychological needs satisfaction, highlighting their equally crucial roles in the process through which servant leadership influences followers' thriving at work.

Values shape the reactions of followers to leader behavior, because their values affect the ways in which followers perceive their leaders. Power distance, as an individual-level value, is one crucial determinant of leadership effectiveness ( Kirkman et al., 2009 ; Anand et al., 2018 ). Particularly in countries higher on power distance, such as China ( Dorfman and Howell, 1988 ), show high respect for hierarchy and formal authority. Followers' perceptions of power and status are likely to influence the effectiveness of servant leadership in promoting thriving at work. This study found that power distance played a negative moderating role in the relationships between servant leadership and the satisfaction of three basic psychological needs, indicating a mismatch between followers' power distance and the values manifested by servant leadership. Those higher on power distance, tend to accept power inequality, respecting the authority and relying on their directives ( Daniels and Greguras, 2014 ; Anand et al., 2018 ). However, servant leaders, exhibiting characteristics and traits contrary to what followers expect from a true leader, may be perceived as less sincere. As a result, servant leadership effectiveness in meeting their basic psychological needs is greatly diminished. In contrast, followers low on power distance find the management philosophy of servant leadership is more aligned with their own standpoints ( Daniels and Greguras, 2014 ; Anand et al., 2018 ), making servant leadership more effective in meeting their basic psychological needs. This outcome provides additional insights into the boundary conditions surrounding the mediating role of basic psychological need satisfaction.

In addition, the mediating effects (i.e., competence needs satisfaction and relatedness needs satisfaction) are also moderated by power distance. That the indirect effects of servant leadership on followers' thriving at work were stronger when followers lower on power distance. This finding provides further clarification on the boundary conditions under which servant leadership can better promote the thriving of the followers through the mediating role of basic psychological needs satisfaction.

5.1 Theoretical implications

This paper has three main contributions:

Firstly, this study identifies servant leadership as a predictor of followers' thriving at work, thereby enhancing our understanding of the antecedents of thriving at work. Prior studies have initially explored the influence of several types of leadership factors such as authentic leadership ( Mortier et al., 2016 ), transformational leadership ( Niessen et al., 2017 ; Hildenbrand et al., 2018 ), servant leadership ( Sheikh et al., 2019 ; Xu and Wang, 2020 ; Jang et al., 2022 ), family supportive superiors ( Russo et al., 2018 ), leadership helping behaviors ( Chen et al., 2020 ), and gritty Leaders ( Rego et al., 2021 ) on thriving at work. However, the wide array of leadership variables and the limited number of studies to date have hindered the understanding of the genuine relationship between specific leadership factors and thriving at work. Our research finds that servant leadership can promote followers' thriving at work, which enriches our understanding of the social embeddedness of thriving at work and its antecedents.

Secondly, while two previous studies explored the impact of servant leadership on thriving at work from the perspectives of social exchange theory, social learning theory, conservation of resource theory and socially embedded model of thriving ( Sheikh et al., 2019 ; Xu and Wang, 2020 ; Jang et al., 2022 ), the underlying psychological mechanism through which servant leadership promotes thriving at work has not been deeply explored. We respond to the call from scholars like Eva et al. (2019) to deepen our understanding of how servant leadership influences followers' thriving. Drawing on SDT, this study empirically examined the mediating role of three basic psychological needs satisfaction in the relationship between servant leadership and thriving at work. According to SDT, the degree of self-determination is reflected in the extent of basic needs satisfaction, directly influencing the internalization of both intrinsic and extrinsic motivation, thereby promoting individual thriving. In addition, the three basic psychological needs are structurally independent and can individually predict outcome variables ( Van den Broeck et al., 2016 ). In this sense, the study explored the separate mediating effects of the satisfaction of three basic needs, suggesting no difference in their impacts. This provides empirical evidence to further refine theories regarding thriving at work.

Thirdly, our research provides additional insights into the boundary conditions that influence how leadership impacts followers' thriving at work. While most scholars have focused on perceived interpersonal justice, perceived leader support, political climate, and other environmental perception variables as boundary conditions for predicting thriving at work ( Xu and Wang, 2020 ; Rego et al., 2021 ; Jang et al., 2022 ), we take power distance, one of the cultural values, as the moderating variable, and find that power distance negatively moderates the mediating effect of the three basic needs satisfaction between servant leadership and thriving at work. Therefore, our study contributes to the literature on thriving at work by highlighting power distance as a significant boundary condition. Additionally, we expand the research field of the interaction between power distance and leadership characteristics ( Lian et al., 2012 ).

5.2 Practical implications

The study also has several practical implications:

(1) Employee thriving is not only about individual health, growth, and career development but also a crucial factor influencing organizational performance. In promoting employee thriving, it is important to guide leaders to reevaluating and repositioning their roles. Leaders who focus on serving the needs of followers may foster their vitality and learning. Organizations can adopt the servant leadership approach and integrate the concept of “service” into leader selection, training, evaluation, and organizational culture.

(2) In managerial practice, leaders often emphasize followers' contributions to the organization, while neglecting the importance of meeting their psychological needs. This study found positive association between servant leadership and followers' thriving through the mediating role of basic psychological needs satisfaction. In specific, leaders should meet followers' autonomy needs by empowering them, fostering their own decision-making capabilities. Secondly, leaders should meet followers' competence needs by providing opportunities for learning and growth, helping them enhance their job skills, and making them experience sense of learning and growing. Thirdly, leaders should address followers' relatedness needs by establishing long-term, mutually trusting relationships, caring about their needs and interests, and strengthening followers' sense of belonging to the organization.

(3) Due to the impact of followers' power distance on leadership effectiveness, leaders can adopt differentiated managerial practices. For followers higher on power distance, leaders can minimize consulting their opinions, meanwhile, simply giving assignments and instructions. For followers lower on power distance, leaders can engage in more communication with them, and provide them with more opportunities in decision-makings.

5.3 Limitations and future directions

The study has several limitations that indicate future research avenues. Firstly, the scenario experiment of Study 1 describes two leadership styles: high servant leadership and low servant leadership. These hypothetical scenarios leads to non-consequential outcomes and limited causal inferences ( Schowalter and Volmer, 2023 ). Future research could design field experiments or laboratory experiments to examine the effects of servant leadership. Secondly, the data of all variables in this study are self-reported. Although common method bias analyses show no threat to this study, future research should consider multi-source data. Thirdly, this study uses followers' perception of servant leadership to estimate the impact of servant leadership on thriving at work. However, Schowalter and Volmer (2023) recently pointed out that this measurement may pose a threat to casual inferences. Future studies may investigate alternative methods such as situational judgment tests to draw a more scientific implication. Finally, this study only discussed the relationship between servant leadership and thriving at work from the individual level. We might consider using a cross-level research design to investigate the impact of servant leadership on thriving at work at the team and organizational levels.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the [patients/participants or patients/participants legal guardian/next of kin] was not required to participate in this study in accordance with the national legislation and the institutional requirements.

Author contributions

XJ: Writing—original draft, Writing—review & editing, Conceptualization, Formal analysis, Methodology. YW: Investigation, Writing—review & editing, Writing—original draft.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by Guangxi Minzu University Introduced Talent Research Project (Grant No. 2021SKQD07).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: servant leadership, thriving at work, basic psychological need satisfaction, power distance, self-determination theory

Citation: Jiang X and Wei Y (2024) Linking servant leadership to followers' thriving at work: self-determination theory perspective. Front. Psychol. 15:1384110. doi: 10.3389/fpsyg.2024.1384110

Received: 08 February 2024; Accepted: 29 April 2024; Published: 16 May 2024.

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Copyright © 2024 Jiang and Wei. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yiyi Wei, yiyisland@163.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Research Hypothesis In Psychology: Types, & Examples

Some key points about hypotheses:

Types of Research Hypotheses

Null Hypothesis

Nondirectional Hypothesis

Directional Hypothesis

Falsifiability

Can a Hypothesis be Proven?

How to Write a Hypothesis

More Examples

How to Write a Great Hypothesis

Hypothesis Format

Hypothesis Types

At a Glance

The Hypothesis in the Scientific Method

Elements of a Good Hypothesis

How to Formulate a Good Hypothesis

The Importance of Operational Definitions

Replicability

Hypothesis Checklist

A few examples of simple hypotheses:

Examples of a complex hypothesis include:

Examples of a null hypothesis include:

Examples of an alternative hypothesis:

Collecting Data on Your Hypothesis

Descriptive Research Methods

Experimental Research Methods

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How to Write a Strong Hypothesis | Guide & Examples

Table of contents

Variables in hypotheses

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Step 2: Do some preliminary research

Step 3: Formulate your hypothesis

Step 4: Refine your hypothesis

Step 5: Phrase your hypothesis in three ways

Step 6. Write a null hypothesis

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Shona McCombes

2.4 Developing a Hypothesis

Theories and Hypotheses

Theory Testing

Incorporating Theory into Your Research

Characteristics of a Good Hypothesis

Key Takeaways

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Developing a Hypothesis

Learning Objectives

Theories and Hypotheses

Theory Testing

Incorporating Theory into Your Research

Characteristics of a Good Hypothesis

Share This Book

10 Developing a Hypothesis

Theories and Hypotheses

Theory Testing

Incorporating Theory into Your Research

Characteristics of a Good Hypothesis

Share This Book

Margin Size

2.4: Developing a Hypothesis

Theories and Hypotheses

Theory Testing

Incorporating Theory into Your Research

Characteristics of a Good Hypothesis

Key Takeaways

AIMS, HYPOTHESES AND HOW TO WRITE THEM

WHY DO WE HAVE AIMS AND HYPOTHESES?

WHAT’S THE DIFFERENCE BETWEEN AIMS AND HYPOTHESES?

EXAMPLES OF HYPOTHESES

WHAT IS A NULL HYPOTHESIS?

WHAT IS AN ALTERNATIVE HYPOTHESIS?

EXPERIMENTAL AND ALTERNATIVE HYPOTHESES

WHAT ARE DIRECTIONAL OR ONE-TAILED HYPOTHESES?

WHAT ARE NON-DIRECTIONAL OR TWO-TAILED HYPOTHESES?

UNDERSTANDING HYPOTHESES:

TESTS OF DIFFERENCE HYPOTHESES

TESTS OF ASSOCIATION HYPOTHESES

TESTS OF CORRELATION HYPOTHESES