what is a research questions in statistics

Statistical Research Questions: Five Examples for Quantitative Analysis

Table of contents, introduction.

How are statistical research questions for quantitative analysis written? This article provides five examples of statistical research questions that will allow statistical analysis to take place.

In quantitative research projects, writing statistical research questions requires a good understanding and the ability to discern the type of data that you will analyze. This knowledge is elemental in framing research questions that shall guide you in identifying the appropriate statistical test to use in your research.

Thus, before writing your statistical research questions and reading the examples in this article, read first the article that enumerates the  four types of measurement scales . Knowing the four types of measurement scales will enable you to appreciate the formulation or structuring of research questions.

Once you feel confident that you can correctly identify the nature of your data, the following examples of statistical research questions will strengthen your understanding. Asking these questions can help you unravel unexpected outcomes or discoveries particularly while doing exploratory data analysis .

Five Examples of Statistical Research Questions

In writing the statistical research questions, I provide a topic that shows the variables of the study, the study description, and a link to the original scientific article to give you a glimpse of the real-world examples.

Topic 1: Physical Fitness and Academic Achievement

A study was conducted to determine the relationship between physical fitness and academic achievement. The subjects of the study include school children in urban schools.

Statistical Research Question No. 1

Is there a significant relationship between physical fitness and academic achievement?

Notice that this study correlated two variables, namely 1) physical fitness, and 2) academic achievement.

To allow statistical analysis to take place, there is a need to define what is physical fitness, as well as academic achievement. The researchers measured physical fitness in terms of  the number of physical fitness tests  that the students passed during their physical education class. It’s simply counting the ‘number of PE tests passed.’

On the other hand, the researchers measured academic achievement in terms of a passing score in Mathematics and English. The variable is the  number of passing scores  in both Mathematics and English.

Both variables are ratio variables. 

Given the statistical research question, the appropriate statistical test can be applied to determine the relationship. A Pearson correlation coefficient test will test the significance and degree of the relationship. But the more sophisticated higher level statistical test can be applied if there is a need to correlate with other variables.

In the particular study mentioned, the researchers used  multivariate logistic regression analyses  to assess the probability of passing the tests, controlling for students’ weight status, ethnicity, gender, grade, and socioeconomic status. For the novice researcher, this requires further study of multivariate (or many variables) statistical tests. You may study it on your own.

Most of what I discuss in the statistics articles I wrote came from self-study. It’s easier to understand concepts now as there are a lot of resource materials available online. Videos and ebooks from places like Youtube, Veoh, The Internet Archives, among others, provide free educational materials. Online education will be the norm of the future. I describe this situation in my post about  Education 4.0 .

The following video sheds light on the frequently used statistical tests and their selection. It is an excellent resource for beginners. Just maintain an open mind to get rid of your dislike for numbers; that is, if you are one of those who have a hard time understanding mathematical concepts. My ebook on  statistical tests and their selection  provides many examples.

Source: Chomitz et al. (2009)

Topic 2: Climate Conditions and Consumption of Bottled Water

This study attempted to correlate climate conditions with the decision of people in Ecuador to consume bottled water, including the volume consumed. Specifically, the researchers investigated if the increase in average ambient temperature affects the consumption of bottled water.

Statistical Research Question No. 2

Is there a significant relationship between average temperature and amount of bottled water consumed?

In this instance, the variables measured include the  average temperature in the areas studied  and the  volume of water consumed . Temperature is an  interval variable,  while volume is a  ratio variable .

In this example, the variables include the  average temperature  and  volume of bottled water . The first variable (average temperature) is an interval variable, and the latter (volume of water) is a ratio variable.

Now, it’s easy to identify the statistical test to analyze the relationship between the two variables. You may refer to my previous post titled  Parametric Statistics: Four Widely Used Parametric Tests and When to Use Them . Using the figure supplied in that article, the appropriate test to use is, again, Pearson’s Correlation Coefficient.

Source: Zapata (2021)

Topic 3: Nursing Home Staff Size and Number of COVID-19 Cases

An investigation sought to determine if the size of nursing home staff and the number of COVID-19 cases are correlated. Specifically, they looked into the number of unique employees working daily, and the outcomes include weekly counts of confirmed COVID-19 cases among residents and staff and weekly COVID-19 deaths among residents.

Statistical Research Question No. 3

Is there a significant relationship between the number of unique employees working in skilled nursing homes and the following:

• number of weekly confirmed COVID-19 cases among residents and staff, and
• number of weekly COVID-19 deaths among residents.

Note that this study on COVID-19 looked into three variables, namely 1) number of unique employees working in skilled nursing homes, 2) number of weekly confirmed cases among residents and staff, and 3) number of weekly COVID-19 deaths among residents.

We call the variable  number of unique employees  the  independent variable , and the other two variables ( number of weekly confirmed cases among residents and staff  and  number of weekly COVID-19 deaths among residents ) as the  dependent variables .

This correlation study determined if the number of staff members in nursing homes influences the number of COVID-19 cases and deaths. It aims to understand if staffing has got to do with the transmission of the deadly coronavirus. Thus, the study’s outcome could inform policy on staffing in nursing homes during the pandemic.

A simple Pearson test may be used to correlate one variable with another variable. But the study used multiple variables. Hence, they produced  regression models  that show how multiple variables affect the outcome. Some of the variables in the study may be redundant, meaning, those variables may represent the same attribute of a population.  Stepwise multiple regression models  take care of those redundancies. Using this statistical test requires further study and experience.

Source: McGarry et al. (2021)

Topic 4: Surrounding Greenness, Stress, and Memory

Scientific evidence has shown that surrounding greenness has multiple health-related benefits. Health benefits include better cognitive functioning or better intellectual activity such as thinking, reasoning, or remembering things. These findings, however, are not well understood. A study, therefore, analyzed the relationship between surrounding greenness and memory performance, with stress as a mediating variable.

Statistical Research Question No. 4

Is there a significant relationship between exposure to and use of natural environments, stress, and memory performance?

As this article is behind a paywall and we cannot see the full article, we can content ourselves with the knowledge that three major variables were explored in this study. These are 1) exposure to and use of natural environments, 2) stress, and 3) memory performance.

Referring to the abstract of this study,  exposure to and use of natural environments  as a variable of the study may be measured in terms of the days spent by the respondent in green surroundings. That will be a ratio variable as we can count it and has an absolute zero point. Stress levels can be measured using standardized instruments like the  Perceived Stress Scale . The third variable, i.e., memory performance in terms of short-term, working memory, and overall memory may be measured using a variety of  memory assessment tools as described by Murray (2016) .

As you become more familiar and well-versed in identifying the variables you would like to investigate in your study, reading studies like this requires reading the method or methodology section. This section will tell you how the researchers measured the variables of their study. Knowing how those variables are quantified can help you design your research and formulate the appropriate statistical research questions.

Source: Lega et al. (2021)

Topic 5: Income and Happiness

This recent finding is an interesting read and is available online. Just click on the link I provide as the source below. The study sought to determine if income plays a role in people’s happiness across three age groups: young (18-30 years), middle (31-64 years), and old (65 or older). The literature review suggests that income has a positive effect on an individual’s sense of happiness. That’s because more money increases opportunities to fulfill dreams and buy more goods and services.

Reading the abstract, we can readily identify one of the variables used in the study, i.e., money. It’s easy to count that. But for happiness, that is a largely subjective matter. Happiness varies between individuals. So how did the researcher measured happiness? As previously mentioned, we need to see the methodology portion to find out why.

If you click on the link to the full text of the paper on pages 10 and 11, you will read that the researcher measured happiness using a 10-point scale. The scale was categorized into three namely, 1) unhappy, 2) happy, and 3) very happy.

An investigation was conducted to determine if the size of nursing home staff and the number of COVID-19 cases are correlated. Specifically, they looked into the number of unique employees working daily, and the outcomes include weekly counts of confirmed COVID-19 cases among residents and staff and weekly COVID-19 deaths among residents.

Statistical Research Question No. 5

Is there a significant relationship between income and happiness?

Source: Måseide (2021)

Now the statistical test used by the researcher is, honestly, beyond me. I may be able to understand it how to use it but doing so requires further study. Although I have initially did some readings on logit models, ordered logit model and generalized ordered logit model are way beyond my self-study in statistics.

Anyhow, those variables found with asterisk (***, **, and **) on page 24 tell us that there are significant relationships between income and happiness. You just have to look at the probability values and refer to the bottom of the table for the level of significance of those relationships.

I do hope that upon reaching this part of the article, you are now well familiar on how to write statistical research questions. Practice makes perfect.

References:

Chomitz, V. R., Slining, M. M., McGowan, R. J., Mitchell, S. E., Dawson, G. F., & Hacker, K. A. (2009). Is there a relationship between physical fitness and academic achievement? Positive results from public school children in the northeastern United States.  Journal of School Health ,  79 (1), 30-37.

Lega, C., Gidlow, C., Jones, M., Ellis, N., & Hurst, G. (2021). The relationship between surrounding greenness, stress and memory.  Urban Forestry & Urban Greening ,  59 , 126974.

Måseide, H. (2021). Income and Happiness: Does the relationship vary with age?

McGarry, B. E., Gandhi, A. D., Grabowski, D. C., & Barnett, M. L. (2021). Larger Nursing Home Staff Size Linked To Higher Number Of COVID-19 Cases In 2020: Study examines the relationship between staff size and COVID-19 cases in nursing homes and skilled nursing facilities. Health Affairs, 40(8), 1261-1269.

Zapata, O. (2021). The relationship between climate conditions and consumption of bottled water: A potential link between climate change and plastic pollution. Ecological Economics, 187, 107090.

© P. A. Regoniel 12 October 2021 | Updated 08 January 2024

Related Posts

Gnumeric 1.12.50: Free Spreadsheet Software Like Excel

Mango Pulp Weevil: A Pest Control Problem in Palawan Island

Writing a research article: how to paraphrase, about the author, patrick regoniel.

Dr. Regoniel, a faculty member of the graduate school, served as consultant to various environmental research and development projects covering issues and concerns on climate change, coral reef resources and management, economic valuation of environmental and natural resources, mining, and waste management and pollution. He has extensive experience on applied statistics, systems modelling and analysis, an avid practitioner of LaTeX, and a multidisciplinary web developer. He leverages pioneering AI-powered content creation tools to produce unique and comprehensive articles in this website.

SimplyEducate.Me Privacy Policy

Statistics Resources

• Excel - Tutorials
• Basic Probability Rules
• Single Event Probability
• Complement Rule
• Intersections & Unions
• Compound Events
• Levels of Measurement
• Independent and Dependent Variables
• Entering Data
• Central Tendency
• Data and Tests
• Displaying Data
• Discussing Statistics In-text
• SEM and Confidence Intervals
• Two-Way Frequency Tables
• Empirical Rule
• Finding Probability
• Accessing SPSS
• Chart and Graphs
• Frequency Table and Distribution
• Descriptive Statistics
• Converting Raw Scores to Z-Scores
• Converting Z-scores to t-scores
• Split File/Split Output
• Partial Eta Squared
• Downloading and Installing G*Power: Windows/PC
• Correlation
• Testing Parametric Assumptions
• One-Way ANOVA
• Two-Way ANOVA
• Repeated Measures ANOVA
• Goodness-of-Fit
• Test of Association
• Pearson's r
• Point Biserial
• Mediation and Moderation
• Simple Linear Regression
• Multiple Linear Regression
• Binomial Logistic Regression
• Multinomial Logistic Regression
• Independent Samples T-test
• Dependent Samples T-test
• Testing Assumptions
• T-tests using SPSS
• T-Test Practice
• Predictive Analytics This link opens in a new window

Quantitative Research Questions

• Null & Alternative Hypotheses
• One-Tail vs. Two-Tail
• Alpha & Beta
• Associated Probability
• Decision Rule
• Statement of Conclusion
• Statistics Group Sessions

ASC Chat Hours

ASC Chat is usually available at the following times ( Pacific Time):

If there is not a coach on duty, submit your question via one of the below methods:

  928-440-1325

  Ask a Coach

  [email protected]

Search our FAQs on the Academic Success Center's  Ask a Coach   page.

Research Questions Tutorial

What is a Quantitative Research Question?

A research question is the driving question(s) behind your research. It should be about an issue that you are genuinely curious and/or passionate about. A good research question is:

Clear :  The purpose of the study should be clear to the reader, without additional explanation.

Focused :  The question is specific. Narrow enough in scope that it can be thoroughly explored within the page limits of the research paper. It brings the common thread that weaves throughout the paper.

Concise :  Clarity should be obtained in the fewest possible words. This is not the place to add unnecessary descriptors and fluff (i.e. “very”).

Complex :  A true research question is not a yes/no question. It brings together a collection of ideas obtained from extensive research, without losing focus or clarity.

Arguable :  It doesn’t provide a definitive answer. Rather, it presents a potential position that future studies could debate.

The format of a research question will depend on a number of factors, including the area of discipline, the proposed research design, and the anticipated analysis.

Unclear:   Does loneliness cause the jitters? Clear:   What is the relationship between feelings of loneliness, as measured by the Lonely Inventory, and uncontrollable shaking?

Unfocused:   What’s the best way to learn? Focused:   In what ways do different teaching styles affect recall and retention in middle schoolers?

Verbose :  Can reading different books of varying genres influence a person’s performance on a test that measures familiarity and knowledge of different words?

Concise:   How does exposure to words through reading novels influence a person’s language development?

Definitive:   What is my favorite color? Arguable:   What is the most popular color amongst teens in America?

Developing a Quantitative Research Question

Developing a research question, was this resource helpful.

• << Previous: Predictive Analytics
• Next: Hypothesis Testing >>
• Last Updated: Apr 19, 2024 3:09 PM
• URL: https://resources.nu.edu/statsresources

Quantitative Research: Examples of Research Questions and Solutions

Are you ready to embark on a journey into the world of quantitative research? Whether you’re a seasoned researcher or just beginning your academic journey, understanding how to formulate effective research questions is essential for conducting meaningful studies. In this blog post, we’ll explore examples of quantitative research questions across various disciplines and discuss how StatsCamp.org courses can provide the tools and support you need to overcome any challenges you may encounter along the way.

Understanding Quantitative Research Questions

Quantitative research involves collecting and analyzing numerical data to answer research questions and test hypotheses. These questions typically seek to understand the relationships between variables, predict outcomes, or compare groups. Let’s explore some examples of quantitative research questions across different fields:

• What is the relationship between class size and student academic performance?
• Does the use of technology in the classroom improve learning outcomes?
• How does parental involvement affect student achievement?
• What is the effect of a new drug treatment on reducing blood pressure?
• Is there a correlation between physical activity levels and the risk of cardiovascular disease?
• How does socioeconomic status influence access to healthcare services?
• What factors influence consumer purchasing behavior?
• Is there a relationship between advertising expenditure and sales revenue?
• How do demographic variables affect brand loyalty?

Stats Camp: Your Solution to Mastering Quantitative Research Methodologies

At StatsCamp.org, we understand that navigating the complexities of quantitative research can be daunting. That’s why we offer a range of courses designed to equip you with the knowledge and skills you need to excel in your research endeavors. Whether you’re interested in learning about regression analysis, experimental design, or structural equation modeling, our experienced instructors are here to guide you every step of the way.

Bringing Your Own Data

One of the unique features of StatsCamp.org is the opportunity to bring your own data to the learning process. Our instructors provide personalized guidance and support to help you analyze your data effectively and overcome any roadblocks you may encounter. Whether you’re struggling with data cleaning, model specification, or interpretation of results, our team is here to help you succeed.

Courses Offered at StatsCamp.org

• Latent Profile Analysis Course : Learn how to identify subgroups, or profiles, within a heterogeneous population based on patterns of responses to multiple observed variables.
• Bayesian Statistics Course : A comprehensive introduction to Bayesian data analysis, a powerful statistical approach for inference and decision-making. Through a series of engaging lectures and hands-on exercises, participants will learn how to apply Bayesian methods to a wide range of research questions and data types.
• Structural Equation Modeling (SEM) Course : Dive into advanced statistical techniques for modeling complex relationships among variables.
• Multilevel Modeling Course : A in-depth exploration of this advanced statistical technique, designed to analyze data with nested structures or hierarchies. Whether you’re studying individuals within groups, schools within districts, or any other nested data structure, multilevel modeling provides the tools to account for the dependencies inherent in such data.

As you embark on your journey into quantitative research, remember that StatsCamp.org is here to support you every step of the way. Whether you’re formulating research questions, analyzing data, or interpreting results, our courses provide the knowledge and expertise you need to succeed. Join us today and unlock the power of quantitative research!

Follow Us On Social! Facebook | Instagram | X

933 San Mateo Blvd NE #500, Albuquerque, NM 87108

4414 82 nd Street #212-121 Lubbock, TX 79424

Monday – Friday: 9:00 AM – 5:00 PM

© Copyright 2003 - 2024 | All Rights Reserved Stats Camp Foundation 501(c)(3) Non-Profit Organization.

Ohio State nav bar

The Ohio State University

• BuckeyeLink
• Find People
• Search Ohio State

Research Questions & Hypotheses

Generally, in quantitative studies, reviewers expect hypotheses rather than research questions. However, both research questions and hypotheses serve different purposes and can be beneficial when used together.

Research Questions

Clarify the research’s aim (farrugia et al., 2010).

• Research often begins with an interest in a topic, but a deep understanding of the subject is crucial to formulate an appropriate research question.
• Descriptive: “What factors most influence the academic achievement of senior high school students?”
• Comparative: “What is the performance difference between teaching methods A and B?”
• Relationship-based: “What is the relationship between self-efficacy and academic achievement?”
• Increasing knowledge about a subject can be achieved through systematic literature reviews, in-depth interviews with patients (and proxies), focus groups, and consultations with field experts.
• Some funding bodies, like the Canadian Institute for Health Research, recommend conducting a systematic review or a pilot study before seeking grants for full trials.
• The presence of multiple research questions in a study can complicate the design, statistical analysis, and feasibility.
• It’s advisable to focus on a single primary research question for the study.
• The primary question, clearly stated at the end of a grant proposal’s introduction, usually specifies the study population, intervention, and other relevant factors.
• The FINER criteria underscore aspects that can enhance the chances of a successful research project, including specifying the population of interest, aligning with scientific and public interest, clinical relevance, and contribution to the field, while complying with ethical and national research standards.
• The P ICOT approach is crucial in developing the study’s framework and protocol, influencing inclusion and exclusion criteria and identifying patient groups for inclusion.
• Defining the specific population, intervention, comparator, and outcome helps in selecting the right outcome measurement tool.
• The more precise the population definition and stricter the inclusion and exclusion criteria, the more significant the impact on the interpretation, applicability, and generalizability of the research findings.
• A restricted study population enhances internal validity but may limit the study’s external validity and generalizability to clinical practice.
• A broadly defined study population may better reflect clinical practice but could increase bias and reduce internal validity.
• An inadequately formulated research question can negatively impact study design, potentially leading to ineffective outcomes and affecting publication prospects.

Checklist: Good research questions for social science projects (Panke, 2018)

Research Hypotheses

Present the researcher’s predictions based on specific statements.

• These statements define the research problem or issue and indicate the direction of the researcher’s predictions.
• Formulating the research question and hypothesis from existing data (e.g., a database) can lead to multiple statistical comparisons and potentially spurious findings due to chance.
• The research or clinical hypothesis, derived from the research question, shapes the study’s key elements: sampling strategy, intervention, comparison, and outcome variables.
• Hypotheses can express a single outcome or multiple outcomes.
• After statistical testing, the null hypothesis is either rejected or not rejected based on whether the study’s findings are statistically significant.
• Hypothesis testing helps determine if observed findings are due to true differences and not chance.
• Hypotheses can be 1-sided (specific direction of difference) or 2-sided (presence of a difference without specifying direction).
• 2-sided hypotheses are generally preferred unless there’s a strong justification for a 1-sided hypothesis.
• A solid research hypothesis, informed by a good research question, influences the research design and paves the way for defining clear research objectives.

Types of Research Hypothesis

• In a Y-centered research design, the focus is on the dependent variable (DV) which is specified in the research question. Theories are then used to identify independent variables (IV) and explain their causal relationship with the DV.
• Example: “An increase in teacher-led instructional time (IV) is likely to improve student reading comprehension scores (DV), because extensive guided practice under expert supervision enhances learning retention and skill mastery.”
• Hypothesis Explanation: The dependent variable (student reading comprehension scores) is the focus, and the hypothesis explores how changes in the independent variable (teacher-led instructional time) affect it.
• In X-centered research designs, the independent variable is specified in the research question. Theories are used to determine potential dependent variables and the causal mechanisms at play.
• Example: “Implementing technology-based learning tools (IV) is likely to enhance student engagement in the classroom (DV), because interactive and multimedia content increases student interest and participation.”
• Hypothesis Explanation: The independent variable (technology-based learning tools) is the focus, with the hypothesis exploring its impact on a potential dependent variable (student engagement).
• Probabilistic hypotheses suggest that changes in the independent variable are likely to lead to changes in the dependent variable in a predictable manner, but not with absolute certainty.
• Example: “The more teachers engage in professional development programs (IV), the more their teaching effectiveness (DV) is likely to improve, because continuous training updates pedagogical skills and knowledge.”
• Hypothesis Explanation: This hypothesis implies a probable relationship between the extent of professional development (IV) and teaching effectiveness (DV).
• Deterministic hypotheses state that a specific change in the independent variable will lead to a specific change in the dependent variable, implying a more direct and certain relationship.
• Example: “If the school curriculum changes from traditional lecture-based methods to project-based learning (IV), then student collaboration skills (DV) are expected to improve because project-based learning inherently requires teamwork and peer interaction.”
• Hypothesis Explanation: This hypothesis presumes a direct and definite outcome (improvement in collaboration skills) resulting from a specific change in the teaching method.
• Example : “Students who identify as visual learners will score higher on tests that are presented in a visually rich format compared to tests presented in a text-only format.”
• Explanation : This hypothesis aims to describe the potential difference in test scores between visual learners taking visually rich tests and text-only tests, without implying a direct cause-and-effect relationship.
• Example : “Teaching method A will improve student performance more than method B.”
• Explanation : This hypothesis compares the effectiveness of two different teaching methods, suggesting that one will lead to better student performance than the other. It implies a direct comparison but does not necessarily establish a causal mechanism.
• Example : “Students with higher self-efficacy will show higher levels of academic achievement.”
• Explanation : This hypothesis predicts a relationship between the variable of self-efficacy and academic achievement. Unlike a causal hypothesis, it does not necessarily suggest that one variable causes changes in the other, but rather that they are related in some way.

Tips for developing research questions and hypotheses for research studies

• Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.
• Learn about current trends and technological advances on the topic.
• Seek careful input from experts, mentors, colleagues, and collaborators to refine your research question as this will aid in developing the research question and guide the research study.
• Use the FINER criteria in the development of the research question.
• Ensure that the research question follows PICOT format.
• Develop a research hypothesis from the research question.
• Ensure that the research question and objectives are answerable, feasible, and clinically relevant.

If your research hypotheses are derived from your research questions, particularly when multiple hypotheses address a single question, it’s recommended to use both research questions and hypotheses. However, if this isn’t the case, using hypotheses over research questions is advised. It’s important to note these are general guidelines, not strict rules. If you opt not to use hypotheses, consult with your supervisor for the best approach.

Farrugia, P., Petrisor, B. A., Farrokhyar, F., & Bhandari, M. (2010). Practical tips for surgical research: Research questions, hypotheses and objectives.  Canadian journal of surgery. Journal canadien de chirurgie ,  53 (4), 278–281.

Hulley, S. B., Cummings, S. R., Browner, W. S., Grady, D., & Newman, T. B. (2007). Designing clinical research. Philadelphia.

Panke, D. (2018). Research design & method selection: Making good choices in the social sciences.  Research Design & Method Selection , 1-368.

• For Corporate
• For Individuals
• For Education
• For Homeschool
• For School Boards
• Data Skills Assessments

Unraveling the Significance of Statistical Questions in Research

Table of Contents

Determining a proper research question is like setting sail on a grand adventure. It’s the compass that points us in the right direction, guiding our pursuit of knowledge and unveiling the secrets that lie hidden. Asking the right question is crucial because it shapes our entire journey, influencing the path we take and the answers we seek.

Like a compass guiding the way 🧭, a well-crafted question directs the researcher’s efforts, ensuring that the study’s objectives are clear and relevant. It serves as the foundation upon which data is collected, analyzed, and interpreted, influencing the choice of methodologies and guiding the researcher toward meaningful insights. A thoughtfully posed question allows for focused exploration, unveiling hidden patterns, exposing relationships, and advancing our understanding of the world. Conversely, a poorly formulated question can lead to ambiguity, wasted resources, and inconclusive results, leaving both researchers and society yearning for answers that remain frustratingly elusive. Thus, in the realm of statistical studies, the right question holds the key to unlocking the doors of knowledge and paving the way for impactful discoveries.

Think about it. Have you ever wondered why some plants thrive in certain environments while others struggle? 🌱🌞 Have you ever pondered what makes your favorite song so captivating? 🎵🤔 These are all questions that spark curiosity and push us to explore, just like in a research study. By asking the right research question, we open doors to new insights, deepening our understanding of the world around us.

Crafting a Research Question

But here’s the thing: crafting a research question isn’t as simple as picking an item from a menu. It requires careful thought and consideration. It’s like solving a puzzle, where each piece contributes to the bigger picture. A well-crafted research question captures the essence of our curiosity, guiding us toward the heart of the matter.

• A statistical question is different from a non-statistical question When we ask a statistical question, we’re not just looking for a simple answer. We want to study and understand how things vary within a group. Statistical questions involve analyzing data to find patterns, relationships, and differences. Unlike non-statistical questions, which often have straightforward answers, statistical questions require data to provide meaningful insights.
• Your statistical question is like a map for your study A well-designed statistical question acts as a roadmap for your study. It helps you plan what data you need to collect and guides you in analyzing and interpreting that data. By having a clear question, you can stay focused, organized, and achieve valuable results in your study.
• A good statistical question makes sure your study matters Choosing a statistical question that is important not only to you but also to others makes your study significant. By addressing meaningful questions, you contribute to knowledge and provide insights that are helpful and interesting to a broader audience. This way, your study has a real impact.
• A clear statistical question makes your results easy to understand When your statistical question is well-defined, it leads to clear and understandable results. By stating exactly what you want to find out, others can easily grasp and comprehend your findings. A clear question helps in effective communication and enhances the understanding of the implications of your research.

Guidelines for Defining Your Question

• Pick a topic you’re interested in (i.e., topic of interest) Select a topic that sparks your curiosity and aligns with your areas of interest. A genuine interest in the subject matter will fuel your motivation and engagement throughout the research process.
• Think up questions about your topic Generate a list of questions related to your chosen topic. Begin with broad questions and then gradually narrow them down to those that can be answered using data. This iterative process allows you to refine and focus your research question.
• Refine your questions Evaluate your questions to ensure they are specific, important, and relevant to your study. Consider seeking feedback from peers, mentors, or subject experts to gain different perspectives and improve the clarity and quality of your questions.

Key Attributes that Make a Statistical Question Effective and Suitable for Research

• Addresses variation: A good statistical question focuses on understanding the variation within a population or between different groups. It seeks to explore patterns, differences, or relationships in data.
• Involves data: A statistical question requires the collection and analysis of data to find meaningful answers. It goes beyond subjective opinions or personal anecdotes and relies on empirical evidence.
• Specific and well-defined: A good statistical question is clear, concise, and well-defined. It leaves no room for ambiguity or confusion and provides a precise direction for the research study.
• Relevant and meaningful: The question should be relevant and have significance in the bigger picture. It should address a problem, fill a gap in knowledge, or help us understand something better.
• Answerable with data: A good statistical question can be answered by collecting and analyzing data. It’s possible to get the necessary information and draw valid conclusions. If you can’t collect the needed data, you may need to change your question.
• Focused on a population: The question specifies the group of people or things being studied. It narrows down the focus to make sure the findings are relevant to that specific group.
• Identifies the variable of interest: A good statistical question identifies the specific thing or characteristic being studied. It tells us what aspect of the group we want to measure, compare, or explore.
• Considers units of measurement: If the thing being studied can be measured with numbers, the question should say how that will be done. It tells us what units or scales will be used to measure it.
• Considers time frame and scope: The question thinks about the time period and place of the study. This helps set boundaries and gives context to the research.
• Unbiased and fair: A good statistical question doesn’t favor one answer over another. It allows for fair and impartial analysis. It considers all possibilities and perspectives.

By incorporating these elements into the development of a statistical question, researchers can ensure that their study is focused, meaningful, and capable of generating valuable insights from data analysis.

How to Avoid Mistakes and Make the Best Research Questions for Your Statistical Investigation

• No fuzzy questions: It’s important to avoid fuzzy or unclear questions that make it difficult to gather the necessary data or decide how to analyze it. Make sure your research question is detailed and provides a clear direction for your study. ○ Fuzzy question: “What do students think about the school?” ○ Non-fuzzy question: “What is the overall satisfaction level of 10th-grade students regarding the school’s extracurricular activities?” In this revised version, the question specifies the target group (10th-grade students), the aspect of the school (extracurricular activities), and the information sought (overall satisfaction level). This makes the question clearer and provides a specific focus for data collection and analysis.
• Question without variation: “What is the temperature?”  
• Question with variation: “How does the temperature vary throughout the year in different regions of the world?”  
• Think of many questions: Do not settle for the first question that comes to mind. Think of multiple questions related to your topic and explore various angles. By considering multiple perspectives, you can select the most promising research question for your investigation.  
• Ensure fairness (avoid bias): Try to avoid asking questions that could make you prefer a certain answer or viewpoint. It’s important to stay fair and objective in your research question so you can consider all the different possibilities. This helps you analyze the data in a complete and unbiased way without any favoritism. ○ Biased question: “Do you agree that smartphones are ruining the younger generation?” ○ Fair question: “What are the effects of smartphone usage on the younger generation?” The original question seems to have a biased view against smartphones and assumes they have a bad influence on young people. It may influence people to answer in a specific way. The revised question, on the other hand, removes the bias by asking about the effects of smartphone usage without assuming they are negative. This lets us explore both the good and bad effects and consider different opinions. We can then look at various outcomes, like how smartphones affect social interactions, school performance, mental health, and overall well-being. This approach allows us to have a fair analysis without any preconceived ideas.  

Designing a Statistical Question to Understand the Impact of Video Games on Academic Achievement

Emily knew that to conduct a successful investigation, she needed to develop a well-focused and meaningful research question. She followed a set of guidelines that would help her generate valuable insights from her data analysis.

First, Emily picked the topic of video games and academic performance. She was genuinely interested in understanding how these two aspects might be connected.

Next, she began to think up questions related to her chosen topic. Emily wondered if playing video games affected students’ grades. She pondered how the amount of time spent playing video games related to academic performance. She even considered whether there were differences in grades between students who played video games and those who did not. Additionally, Emily wondered if there was a correlation between the number of hours spent playing video games and a student’s Grade Point Average (GPA).

Emily understood the importance of refining her questions to make them effective statistical questions. She sought feedback from her teacher, who provided valuable insights. Together, they evaluated her questions and concluded that question # 2—about the relationship between the amount of time spent playing video games and academic performance—was specific, important, and relevant to her study.

To ensure fairness and avoid bias in her investigation, Emily took another important step. She rephrased her research question to remove any preconceived ideas. Her new question became: “What is the relationship between the amount of time spent playing video games and academic performance among 10th-grade students?”

By carefully following these guidelines, Emily successfully developed a statistical question that highlighted the importance of considering the question before starting a statistical study. Her question focused on a specific population (10th-grade students), involved data analysis, was specific and well-defined, and considered the units of measurement (time spent playing video games and academic performance). Most importantly, Emily’s research question avoided bias by not assuming whether video games had a positive or negative impact on academic performance.

Now equipped with her well-crafted research question, Emily was ready to collect data, analyze it, and draw conclusions based on her statistical investigation. Exciting adventures and valuable discoveries awaited her on this incredible journey of exploration and knowledge.

Related Tags:

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• HCA Healthc J Med
• v.1(2); 2020
• PMC10324782

Introduction to Research Statistical Analysis: An Overview of the Basics

Christian vandever.

1 HCA Healthcare Graduate Medical Education

Description

This article covers many statistical ideas essential to research statistical analysis. Sample size is explained through the concepts of statistical significance level and power. Variable types and definitions are included to clarify necessities for how the analysis will be interpreted. Categorical and quantitative variable types are defined, as well as response and predictor variables. Statistical tests described include t-tests, ANOVA and chi-square tests. Multiple regression is also explored for both logistic and linear regression. Finally, the most common statistics produced by these methods are explored.

Introduction

Statistical analysis is necessary for any research project seeking to make quantitative conclusions. The following is a primer for research-based statistical analysis. It is intended to be a high-level overview of appropriate statistical testing, while not diving too deep into any specific methodology. Some of the information is more applicable to retrospective projects, where analysis is performed on data that has already been collected, but most of it will be suitable to any type of research. This primer will help the reader understand research results in coordination with a statistician, not to perform the actual analysis. Analysis is commonly performed using statistical programming software such as R, SAS or SPSS. These allow for analysis to be replicated while minimizing the risk for an error. Resources are listed later for those working on analysis without a statistician.

After coming up with a hypothesis for a study, including any variables to be used, one of the first steps is to think about the patient population to apply the question. Results are only relevant to the population that the underlying data represents. Since it is impractical to include everyone with a certain condition, a subset of the population of interest should be taken. This subset should be large enough to have power, which means there is enough data to deliver significant results and accurately reflect the study’s population.

The first statistics of interest are related to significance level and power, alpha and beta. Alpha (α) is the significance level and probability of a type I error, the rejection of the null hypothesis when it is true. The null hypothesis is generally that there is no difference between the groups compared. A type I error is also known as a false positive. An example would be an analysis that finds one medication statistically better than another, when in reality there is no difference in efficacy between the two. Beta (β) is the probability of a type II error, the failure to reject the null hypothesis when it is actually false. A type II error is also known as a false negative. This occurs when the analysis finds there is no difference in two medications when in reality one works better than the other. Power is defined as 1-β and should be calculated prior to running any sort of statistical testing. Ideally, alpha should be as small as possible while power should be as large as possible. Power generally increases with a larger sample size, but so does cost and the effect of any bias in the study design. Additionally, as the sample size gets bigger, the chance for a statistically significant result goes up even though these results can be small differences that do not matter practically. Power calculators include the magnitude of the effect in order to combat the potential for exaggeration and only give significant results that have an actual impact. The calculators take inputs like the mean, effect size and desired power, and output the required minimum sample size for analysis. Effect size is calculated using statistical information on the variables of interest. If that information is not available, most tests have commonly used values for small, medium or large effect sizes.

When the desired patient population is decided, the next step is to define the variables previously chosen to be included. Variables come in different types that determine which statistical methods are appropriate and useful. One way variables can be split is into categorical and quantitative variables. ( Table 1 ) Categorical variables place patients into groups, such as gender, race and smoking status. Quantitative variables measure or count some quantity of interest. Common quantitative variables in research include age and weight. An important note is that there can often be a choice for whether to treat a variable as quantitative or categorical. For example, in a study looking at body mass index (BMI), BMI could be defined as a quantitative variable or as a categorical variable, with each patient’s BMI listed as a category (underweight, normal, overweight, and obese) rather than the discrete value. The decision whether a variable is quantitative or categorical will affect what conclusions can be made when interpreting results from statistical tests. Keep in mind that since quantitative variables are treated on a continuous scale it would be inappropriate to transform a variable like which medication was given into a quantitative variable with values 1, 2 and 3.

Categorical vs. Quantitative Variables

Both of these types of variables can also be split into response and predictor variables. ( Table 2 ) Predictor variables are explanatory, or independent, variables that help explain changes in a response variable. Conversely, response variables are outcome, or dependent, variables whose changes can be partially explained by the predictor variables.

Response vs. Predictor Variables

Choosing the correct statistical test depends on the types of variables defined and the question being answered. The appropriate test is determined by the variables being compared. Some common statistical tests include t-tests, ANOVA and chi-square tests.

T-tests compare whether there are differences in a quantitative variable between two values of a categorical variable. For example, a t-test could be useful to compare the length of stay for knee replacement surgery patients between those that took apixaban and those that took rivaroxaban. A t-test could examine whether there is a statistically significant difference in the length of stay between the two groups. The t-test will output a p-value, a number between zero and one, which represents the probability that the two groups could be as different as they are in the data, if they were actually the same. A value closer to zero suggests that the difference, in this case for length of stay, is more statistically significant than a number closer to one. Prior to collecting the data, set a significance level, the previously defined alpha. Alpha is typically set at 0.05, but is commonly reduced in order to limit the chance of a type I error, or false positive. Going back to the example above, if alpha is set at 0.05 and the analysis gives a p-value of 0.039, then a statistically significant difference in length of stay is observed between apixaban and rivaroxaban patients. If the analysis gives a p-value of 0.91, then there was no statistical evidence of a difference in length of stay between the two medications. Other statistical summaries or methods examine how big of a difference that might be. These other summaries are known as post-hoc analysis since they are performed after the original test to provide additional context to the results.

Analysis of variance, or ANOVA, tests can observe mean differences in a quantitative variable between values of a categorical variable, typically with three or more values to distinguish from a t-test. ANOVA could add patients given dabigatran to the previous population and evaluate whether the length of stay was significantly different across the three medications. If the p-value is lower than the designated significance level then the hypothesis that length of stay was the same across the three medications is rejected. Summaries and post-hoc tests also could be performed to look at the differences between length of stay and which individual medications may have observed statistically significant differences in length of stay from the other medications. A chi-square test examines the association between two categorical variables. An example would be to consider whether the rate of having a post-operative bleed is the same across patients provided with apixaban, rivaroxaban and dabigatran. A chi-square test can compute a p-value determining whether the bleeding rates were significantly different or not. Post-hoc tests could then give the bleeding rate for each medication, as well as a breakdown as to which specific medications may have a significantly different bleeding rate from each other.

A slightly more advanced way of examining a question can come through multiple regression. Regression allows more predictor variables to be analyzed and can act as a control when looking at associations between variables. Common control variables are age, sex and any comorbidities likely to affect the outcome variable that are not closely related to the other explanatory variables. Control variables can be especially important in reducing the effect of bias in a retrospective population. Since retrospective data was not built with the research question in mind, it is important to eliminate threats to the validity of the analysis. Testing that controls for confounding variables, such as regression, is often more valuable with retrospective data because it can ease these concerns. The two main types of regression are linear and logistic. Linear regression is used to predict differences in a quantitative, continuous response variable, such as length of stay. Logistic regression predicts differences in a dichotomous, categorical response variable, such as 90-day readmission. So whether the outcome variable is categorical or quantitative, regression can be appropriate. An example for each of these types could be found in two similar cases. For both examples define the predictor variables as age, gender and anticoagulant usage. In the first, use the predictor variables in a linear regression to evaluate their individual effects on length of stay, a quantitative variable. For the second, use the same predictor variables in a logistic regression to evaluate their individual effects on whether the patient had a 90-day readmission, a dichotomous categorical variable. Analysis can compute a p-value for each included predictor variable to determine whether they are significantly associated. The statistical tests in this article generate an associated test statistic which determines the probability the results could be acquired given that there is no association between the compared variables. These results often come with coefficients which can give the degree of the association and the degree to which one variable changes with another. Most tests, including all listed in this article, also have confidence intervals, which give a range for the correlation with a specified level of confidence. Even if these tests do not give statistically significant results, the results are still important. Not reporting statistically insignificant findings creates a bias in research. Ideas can be repeated enough times that eventually statistically significant results are reached, even though there is no true significance. In some cases with very large sample sizes, p-values will almost always be significant. In this case the effect size is critical as even the smallest, meaningless differences can be found to be statistically significant.

These variables and tests are just some things to keep in mind before, during and after the analysis process in order to make sure that the statistical reports are supporting the questions being answered. The patient population, types of variables and statistical tests are all important things to consider in the process of statistical analysis. Any results are only as useful as the process used to obtain them. This primer can be used as a reference to help ensure appropriate statistical analysis.

Funding Statement

This research was supported (in whole or in part) by HCA Healthcare and/or an HCA Healthcare affiliated entity.

Conflicts of Interest

The author declares he has no conflicts of interest.

Christian Vandever is an employee of HCA Healthcare Graduate Medical Education, an organization affiliated with the journal’s publisher.

This research was supported (in whole or in part) by HCA Healthcare and/or an HCA Healthcare affiliated entity. The views expressed in this publication represent those of the author(s) and do not necessarily represent the official views of HCA Healthcare or any of its affiliated entities.

• Research Questions: Definitions, Types + [Examples]

Research questions lie at the core of systematic investigation and this is because recording accurate research outcomes is tied to asking the right questions. Asking the right questions when conducting research can help you collect relevant and insightful information that ultimately influences your work, positively. 

The right research questions are typically easy to understand, straight to the point, and engaging. In this article, we will share tips on how to create the right research questions and also show you how to create and administer an online questionnaire with Formplus . 

What is a Research Question? 

A research question is a specific inquiry which the research seeks to provide a response to. It resides at the core of systematic investigation and it helps you to clearly define a path for the research process. 

A research question is usually the first step in any research project. Basically, it is the primary interrogation point of your research and it sets the pace for your work.  

Typically, a research question focuses on the research, determines the methodology and hypothesis, and guides all stages of inquiry, analysis, and reporting. With the right research questions, you will be able to gather useful information for your investigation. 

Types of Research Questions 

Research questions are broadly categorized into 2; that is, qualitative research questions and quantitative research questions. Qualitative and quantitative research questions can be used independently and co-dependently in line with the overall focus and objectives of your research. 

If your research aims at collecting quantifiable data , you will need to make use of quantitative research questions. On the other hand, qualitative questions help you to gather qualitative data bothering on the perceptions and observations of your research subjects. 

Qualitative Research Questions  

A qualitative research question is a type of systematic inquiry that aims at collecting qualitative data from research subjects. The aim of qualitative research questions is to gather non-statistical information pertaining to the experiences, observations, and perceptions of the research subjects in line with the objectives of the investigation. 

Types of Qualitative Research Questions  

• Ethnographic Research Questions

As the name clearly suggests, ethnographic research questions are inquiries presented in ethnographic research. Ethnographic research is a qualitative research approach that involves observing variables in their natural environments or habitats in order to arrive at objective research outcomes. 

These research questions help the researcher to gather insights into the habits, dispositions, perceptions, and behaviors of research subjects as they interact in specific environments. 

Ethnographic research questions can be used in education, business, medicine, and other fields of study, and they are very useful in contexts aimed at collecting in-depth and specific information that are peculiar to research variables. For instance, asking educational ethnographic research questions can help you understand how pedagogy affects classroom relations and behaviors. 

This type of research question can be administered physically through one-on-one interviews, naturalism (live and work), and participant observation methods. Alternatively, the researcher can ask ethnographic research questions via online surveys and questionnaires created with Formplus.  

Examples of Ethnographic Research Questions

• Why do you use this product?
• Have you noticed any side effects since you started using this drug?
• Does this product meet your needs?

• Case Studies

A case study is a qualitative research approach that involves carrying out a detailed investigation into a research subject(s) or variable(s). In the course of a case study, the researcher gathers a range of data from multiple sources of information via different data collection methods, and over a period of time. 

The aim of a case study is to analyze specific issues within definite contexts and arrive at detailed research subject analyses by asking the right questions. This research method can be explanatory, descriptive , or exploratory depending on the focus of your systematic investigation or research. 

An explanatory case study is one that seeks to gather information on the causes of real-life occurrences. This type of case study uses “how” and “why” questions in order to gather valid information about the causative factors of an event. 

Descriptive case studies are typically used in business researches, and they aim at analyzing the impact of changing market dynamics on businesses. On the other hand, exploratory case studies aim at providing answers to “who” and “what” questions using data collection tools like interviews and questionnaires. 

Some questions you can include in your case studies are: 

• Why did you choose our services?
• How has this policy affected your business output?
• What benefits have you recorded since you started using our product?

An interview is a qualitative research method that involves asking respondents a series of questions in order to gather information about a research subject. Interview questions can be close-ended or open-ended , and they prompt participants to provide valid information that is useful to the research. 

An interview may also be structured, semi-structured , or unstructured , and this further influences the types of questions they include. Structured interviews are made up of more close-ended questions because they aim at gathering quantitative data while unstructured interviews consist, primarily, of open-ended questions that allow the researcher to collect qualitative information from respondents. 

You can conduct interview research by scheduling a physical meeting with respondents, through a telephone conversation, and via digital media and video conferencing platforms like Skype and Zoom. Alternatively, you can use Formplus surveys and questionnaires for your interview. 

Examples of interview questions include: 

• What challenges did you face while using our product?
• What specific needs did our product meet?
• What would you like us to improve our service delivery?

Quantitative Research Questions

Quantitative research questions are questions that are used to gather quantifiable data from research subjects. These types of research questions are usually more specific and direct because they aim at collecting information that can be measured; that is, statistical information. 

Types of Quantitative Research Questions

• Descriptive Research Questions

Descriptive research questions are inquiries that researchers use to gather quantifiable data about the attributes and characteristics of research subjects. These types of questions primarily seek responses that reveal existing patterns in the nature of the research subjects. 

It is important to note that descriptive research questions are not concerned with the causative factors of the discovered attributes and characteristics. Rather, they focus on the “what”; that is, describing the subject of the research without paying attention to the reasons for its occurrence. 

Descriptive research questions are typically closed-ended because they aim at gathering definite and specific responses from research participants. Also, they can be used in customer experience surveys and market research to collect information about target markets and consumer behaviors. 

Descriptive Research Question Examples

• How often do you make use of our fitness application?
• How much would you be willing to pay for this product?

• Comparative Research Questions

A comparative research question is a type of quantitative research question that is used to gather information about the differences between two or more research subjects across different variables. These types of questions help the researcher to identify distinct features that mark one research subject from the other while highlighting existing similarities. 

Asking comparative research questions in market research surveys can provide insights on how your product or service matches its competitors. In addition, it can help you to identify the strengths and weaknesses of your product for a better competitive advantage.  

The 5 steps involved in the framing of comparative research questions are: 

• Choose your starting phrase
• Identify and name the dependent variable
• Identify the groups you are interested in
• Identify the appropriate adjoining text
• Write out the comparative research question

Comparative Research Question Samples 

• What are the differences between a landline telephone and a smartphone?
• What are the differences between work-from-home and on-site operations?

• Relationship-based Research Questions  

Just like the name suggests, a relationship-based research question is one that inquires into the nature of the association between two research subjects within the same demographic. These types of research questions help you to gather information pertaining to the nature of the association between two research variables. 

Relationship-based research questions are also known as correlational research questions because they seek to clearly identify the link between 2 variables. 

Read: Correlational Research Designs: Types, Examples & Methods

Examples of relationship-based research questions include: 

• What is the relationship between purchasing power and the business site?
• What is the relationship between the work environment and workforce turnover?

Examples of a Good Research Question

Since research questions lie at the core of any systematic investigations, it is important to know how to frame a good research question. The right research questions will help you to gather the most objective responses that are useful to your systematic investigation. 

A good research question is one that requires impartial responses and can be answered via existing sources of information. Also, a good research question seeks answers that actively contribute to a body of knowledge; hence, it is a question that is yet to be answered in your specific research context.

• Open-Ended Questions

 An open-ended question is a type of research question that does not restrict respondents to a set of premeditated answer options. In other words, it is a question that allows the respondent to freely express his or her perceptions and feelings towards the research subject. 

Examples of Open-ended Questions

• How do you deal with stress in the workplace?
• What is a typical day at work like for you?
• Close-ended Questions

A close-ended question is a type of survey question that restricts respondents to a set of predetermined answers such as multiple-choice questions . Close-ended questions typically require yes or no answers and are commonly used in quantitative research to gather numerical data from research participants. 

Examples of Close-ended Questions

• Did you enjoy this event?
• How likely are you to recommend our services?
• Very Likely
• Somewhat Likely
• Likert Scale Questions

A Likert scale question is a type of close-ended question that is structured as a 3-point, 5-point, or 7-point psychometric scale . This type of question is used to measure the survey respondent’s disposition towards multiple variables and it can be unipolar or bipolar in nature. 

Example of Likert Scale Questions

• How satisfied are you with our service delivery?
• Very dissatisfied
• Not satisfied
• Very satisfied
• Rating Scale Questions

A rating scale question is a type of close-ended question that seeks to associate a specific qualitative measure (rating) with the different variables in research. It is commonly used in customer experience surveys, market research surveys, employee reviews, and product evaluations. 

Example of Rating Questions

• How would you rate our service delivery?

  Examples of a Bad Research Question

Knowing what bad research questions are would help you avoid them in the course of your systematic investigation. These types of questions are usually unfocused and often result in research biases that can negatively impact the outcomes of your systematic investigation. 

• Loaded Questions

A loaded question is a question that subtly presupposes one or more unverified assumptions about the research subject or participant. This type of question typically boxes the respondent in a corner because it suggests implicit and explicit biases that prevent objective responses. 

Example of Loaded Questions

• Have you stopped smoking?
• Where did you hide the money?
• Negative Questions

A negative question is a type of question that is structured with an implicit or explicit negator. Negative questions can be misleading because they upturn the typical yes/no response order by requiring a negative answer for affirmation and an affirmative answer for negation. 

Examples of Negative Questions

• Would you mind dropping by my office later today?
• Didn’t you visit last week?
• Leading Questions  

A l eading question is a type of survey question that nudges the respondent towards an already-determined answer. It is highly suggestive in nature and typically consists of biases and unverified assumptions that point toward its premeditated responses. 

Examples of Leading Questions

• If you enjoyed this service, would you be willing to try out our other packages?
• Our product met your needs, didn’t it?

Read More: Leading Questions: Definition, Types, and Examples

How to Use Formplus as Online Research Questionnaire Tool  

With Formplus, you can create and administer your online research questionnaire easily. In the form builder, you can add different form fields to your questionnaire and edit these fields to reflect specific research questions for your systematic investigation. 

Here is a step-by-step guide on how to create an online research questionnaire with Formplus: 

• Sign in to your Formplus accoun t, then click on the “create new form” button in your dashboard to access the Form builder.

• In the form builder, add preferred form fields to your online research questionnaire by dragging and dropping them into the form. Add a title to your form in the title block. You can edit form fields by clicking on the “pencil” icon on the right corner of each form field.

• Save the form to access the customization section of the builder. Here, you can tweak the appearance of your online research questionnaire by adding background images, changing the form font, and adding your organization’s logo.

• Finally, copy your form link and share it with respondents. You can also use any of the multiple sharing options available.

Conclusion  

The success of your research starts with framing the right questions to help you collect the most valid and objective responses. Be sure to avoid bad research questions like loaded and negative questions that can be misleading and adversely affect your research data and outcomes. 

Your research questions should clearly reflect the aims and objectives of your systematic investigation while laying emphasis on specific contexts. To help you seamlessly gather responses for your research questions, you can create an online research questionnaire on Formplus.  

Connect to Formplus, Get Started Now - It's Free!

• abstract in research papers
• bad research questions
• examples of research questions
• types of research questions
• busayo.longe

You may also like:

How to do a Meta Analysis: Methodology, Pros & Cons

In this article, we’ll go through the concept of meta-analysis, what it can be used for, and how you can use it to improve how you...

Research Summary: What Is It & How To Write One

Introduction A research summary is a requirement during academic research and sometimes you might need to prepare a research summary...

How to Write a Problem Statement for your Research

Learn how to write problem statements before commencing any research effort. Learn about its structure and explore examples

How to Write An Abstract For Research Papers: Tips & Examples

In this article, we will share some tips for writing an effective abstract, plus samples you can learn from.

Formplus - For Seamless Data Collection

Collect data the right way with a versatile data collection tool. try formplus and transform your work productivity today..

What is a Statistical Question?

Identifying a statistical question requires familiarity with statistics. Statistics is a mathematical study that involves collecting, analyzing, and interpreting data that changes.

So a statistical question will ask a question in which the answer can change; data must be collected and analyzed, and the answer offers an explanation of the information.

How to recognize a statistical question?

• A question is not a statistical question if it has an exact answer. For example “How old are you?”
• A question is a statistical question if the answer is a percent, range, or average. For example “How old are the students in this room”

A statistical question is a question that has many different, or variable, answers. An example is "How much does a typical cat weigh?" Begin the activities below to practice identifying statistical questions.

Examples of Statistical Questions

• On average, how old are the dogs that live on this street?
• What proportion of the students at your school like watermelons?
• What time did the students in this class get up this morning?
• How many votes did the winning candidate for the Presidents of the Student Body receive in each of the past 20 years?
• What were the high temperatures in all of the Latin American capitals today?

Examples of Non-Statistical Questions

• What time did I get up this morning?
• How many votes did the winning candidate for the Student Body receive this year?
• What was the high temperature in Mexico City today?

We often collect data to answer questions about something. The data we collect may show variability, which means the data values are not all the same.

Some data sets have more variability than others.

Here are two sets of figures:

Set A has more figures with the same shape, color, or size. Set B shows more figures with different shapes, colors, or sizes, so set B has greater variability than set A.

Both numerical and categorical data can show variability. Numerical sets can contain different numbers, and categorical sets can contain different categories or types.

When a question can only be answered by using data and we expect that data to have variability, we call it a statistical question.

Here are some examples:

• Who is the most popular musical artist at your school?
• When do students in your class typically eat dinner?
• Which classroom in your school has the most books?

To answer the question about books, we may need to count all of the books in each classroom of a school. The data we collect would likely show variability because we would expect each classroom to have a different number of books.

In contrast, the question “How many books are in your classroom?” is not a statistical question. If we collect data to answer the question (for example, by asking everyone in the class to count books), the data can be expected to show the same value.

Likewise, if we ask all of the students at a school where they go to school, that question is not a statistical question because the responses will all be the same.

What Is Math Is Fun?

Exploring the World of Cool Math Games

10 Reasons to Start Using Math Games to Learn

Fun and Educational Math Games for Kids

Subscribe to new posts.

To make full use of our website, Switch or update for free to one of the following current browsers:

Firefox , Google Chrome oder Internet Explorer 8 and above

To make full use of our website, enable JavaScript in your browser.

Our tutorial videos allow students to learn at their own pace without any pressure or stress. They can watch, pause or rewind the videos as often as they need until they understand the content.

Our interactive exercises come in a variety of formats so students can practice in a playful way. They get feedback and hints while solving the exercises. As a result, they learn and grow from their mistakes instead of feeling frustrated.

Children in elementary school can use Sofahero to review independently and stay motivated. They master topics while going on exciting adventures, without the help of adults.

Students can use the worksheets to prepare themselves for mock tests: Simply print them out, fill them out, and check the answers using the answer key.

Real teachers provide students with fast, personalized support. They can help walk students through homework problems, review lessons, and answer questions in an easy-to-understand way.

With interactive e-books children can playfully train their active listening and comprehension skills.

Cancel at any time

Discover why over 1.6 MILLION students choose sofatutor!

• Probability and Statistics
• Representing Data

What is a Statistical Question?

Start exercises

Show worksheets

Statistical Questions

Key features of a statistical question, non-statistical vs statistical questions, example and non-example of statistical questions, statistical questions – understanding, statistical questions – real life application, statistical questions – practice, what is a statistical question– summary, what is a statistical question– frequently asked questions, learning text on the topic what is a statistical question.

Welcome to a journey into the world of statistics, specifically focusing on what makes a question statistical . As we delve into this fascinating topic, you'll learn to recognize questions that anticipate variability in data and how they play a crucial role in statistical analysis.

A statistical question expects answers with variability . This means the answer isn't just a single number or fact, but a range of possible outcomes or responses. Therefore, to be answered correctly, statistical questions require statistical methods and data analysis. Statistical questions are essential because they set the stage for gathering and analyzing data in ways that reveal trends, patterns, and insights.

Statistical questions share certain characteristics:

Anticipation of Variability: They predict that the answers will vary. There will be more than one potential answer.

Need for Data Collection: To answer these questions, data must be gathered from different sources or a group of individuals.

Potential for Analysis: These questions allow for further data exploration and interpretation.

As you may notice, understanding the difference between non-statistical questions and statistical questions is crucial. Non-statistical questions have definite answers, whereas statistical questions expect a variety of answers. Here are some further examples of non-statistical and statistical questions:

When exploring statistical questions, it's essential to understand why certain questions fall into this category and others don't. Let's dive deeper with some examples to get a clearer picture.

This question is statistical for several reasons:

Variability: It anticipates that students in the class will have different heights. Therefore, the answer isn't just one value but a range of values.

Data Collection: To answer this question, you would need to measure the height of each student, thus collecting a set of data.

Data Analysis Potential: Once you have the data (the heights), you can analyze it in various ways—finding the average height, identifying the tallest and shortest students, or even creating a graph to visually represent the height distribution.

In contrast, this question is not statistical for the following reasons.

Fixed Answer: The answer to this question is always Paris. There's no variability or range of answers.

No Data Collection Needed: This question requires no data gathering. It's a fact-based question that can be answered without engaging in data analysis.

Lack of Analysis Potential: Since the answer is a constant, there's no scope for analysis or interpretation in terms of data.

Understanding why a question is or isn't statistical helps us grasp the essence of statistical thinking, which is fundamental in data analysis and interpretation. Identifying the potential for variability in the answers is key to distinguishing statistical questions from non-statistical ones.

Test your newfound knowledge of statistical questions by answering the following questions.

A question is statistical if it anticipates variability in its answers and requires data collection for analysis.

What is the capital of Japan? (The answer is Tokyo, which is fixed and doesn't vary.)

Because it doesn't allow for a range of responses or variability in data.

Yes, it is a statistical question because it expects a variety of answers. Each student may own a different number of books, hence the data collected will have variability.

No, this is not a statistical question. The formula for calculating the area of a circle is a fixed mathematical fact (πr²), and it does not involve any variability or need for data collection.

Businesses analyze customer behavior, market trends, and sales data using statistical questions to make informed decisions about product development, pricing, and marketing strategies.

• Healthcare professionals use statistical questions to investigate disease patterns, evaluate the effectiveness of treatments, and monitor population health.
• Government agencies use statistical questions to collect data on the economy, crime rates, and demographic information in order to make informed policy decisions.

Now that you understand what makes a statistical question, it’s time to practice. Below are some questions, and you need to answer if it is a statistical question or not, and why.

Yes, this is a statistical question. This question is statistical because it anticipates variability in the data (different students likely spend different amounts of time on homework) and it can be answered by collecting data from each student in the class.

No, this is not a statistical question. This is not a statistical question because it has a fixed answer (Mount Everest) and does not involve variability or the need to collect and analyze data.

Yes, this is a statistical question. It is a statistical question as it requires data collection (counting the number of books added) and there is potential for variability from year to year.

No, this is not a statistical question. This question has a specific yes or no answer and does not involve collecting and analyzing variable data.

Yes, this is a statistical question. This question is statistical because it requires collecting data from a number of students, and there will likely be a variety of answers (indicating variability).

No, this is not a statistical question. This question can be answered with a general yes or no and does not require the collection and analysis of variable data. It's more about a known fact rather than exploring variability through data.

Key Learnings from this Text:

A statistical question expects a range of answers, not just one.

It requires the collection and analysis of data.

Understanding the difference between statistical and non-statistical questions helps in comprehending how data works in our world.

Keep exploring different questions around you and try to classify them as statistical or non-statistical. This skill will enhance your understanding of data and statistics! Now that you understand what a statistical question is, you can explore What Can Statistics Be Used For .

A statistical question is one that anticipates a variety of answers and requires data collection and analysis to be answered.

Understanding these questions is crucial for data analysis, as it helps in gathering relevant data and interpreting it effectively.

Yes, but the answer would usually be in the form of a range or average, not a single number.

They are vital in fields like market research, public health, and education, where understanding variability in data is crucial.

"How many hours per week do students in our school spend on extracurricular activities?" This expects a range of answers and is related to the students' experiences.

No, only those expecting a range or variety of numerical answers are considered statistical.

It prompts the collection of varied data, which can then be analyzed for patterns, averages, and trends.

They form the basis of surveys, allowing for the collection of diverse responses that reflect different perspectives or experiences.

No, since they involve variability and personal experiences, statistical questions don't have a single 'right' answer.

By posing open-ended questions that require data collection and analysis, and by discussing the variability in the answers.

What is a Statistical Question? exercise

What are the features that make a question statistical.

Statistical questions do not have one specific answer.

We often would need to collect and analyse data to answer a statistical question.

When asking a statistical question we would expect a variety of responses . The answers are not fixed or based on facts. To answer a statistical question we would have to collect data and analyse the results.

Are the following questions statistical or non-statistical?

Does the question have a fixed answer that would be the same no matter who was asked? If so, then it is not a statistical question .

Can you collect data based on peoples answers? If so, then it is a statistical question.

Statistical questions include:

How many books do 11 year olds read?

• This would require us to gather the responses from many 11 year olds, and analyse the results.
• The answer would vary depending on the school, and as above we would have to gather lots of data from different schools.
• This answer would vary each year, and could also vary across the state of Michigan. To get an accurate answer, we would have to measure the rainfall each day in June over several years, and in different parts of the state.

Non-statistical questions are:

• How many sides does a square have?
• In which continent is Singapore located?
• What is 10 x 7?

Is the following question statistical or non statistical, and why?

Would different people give different answers?

Does the answer need some kind of data collection?

There are two correct statements to tick.

Correct statements are:

This question is non-statistical

This question has a definite correct answer.

This is a trickier one, but it is not a statistical question. The correct answer is 4 or 5 Tuesdays , depending on which day of the week 1st June falls on. Everybody asked should say the same thing, so there would be no variety in responses.

Is the following question statistical or non-statistical, and why?

There will be lots of different responses to this question.

We could collect data from pupils in our school to answer the question.

The correct statements are:

This question is statistical.

This question allows for data collection of teenagers' responses.

This is a statistical question. There will be lots of different music preferences, and we can collect and analyse the data to discover the most popular genres.

What makes a question a statistical question?

Remember, a statistical question doesn't have one definitive answer, but a variety of answers are expected . You cannot search the internet for an answer, since it will depend on individual circumstances.

The answer requires data to be collected and analysed .

Where is the world's tallest building?

Here is an example of a non-statistical question, as there is a definite answer to this question. No data collection would be needed.

The correct answers are:

A variety of answers is expected

The answer requires data to be gathered and analysed.

If an answer is fixed or definite, then it isn't a statistical question since every person who answers would give say the same thing. This would also mean there would be no data to analyse.

How can we alter the following question to make it statistical?

We want the question to generate a range of responses, so choose a question that will have variety in answers.

To be able to analyse the results, the question shouldn't be vague. "Fit and healthy" can mean different things to different people, so how can we analyse their responses?

The question should be written clearly so that although we're expecting different answers, they should be given in the same format.

The most suitable question would be:

On average, how many hours of exercise do pupils in our school do each week?

In order to answer this question, you would need to collect data from pupils. Their range of responses could be grouped by number of hours, and we can then draw conclusions from the data.

________________________________________________________

Are pupils in your school fit and healthy?

This is a vague question. "Fit and healthy" could mean different things to different people, so the responses would not be consistent.

How much exercise do people in your town do each week?

Whilst this appears to be a good question, it doesn't specify a format for the answer. Some people might answer "a lot" or "not much", and others might answer in hours, or some people could say "3 times a week". This would make the data difficult to analyse.*

Do pupils in your class like to take part in sports or exercise?

Again, quite a vague question. When we collect the data, we're likely to get "yes" or "no" responses, so we wouldn't get a huge amount of information from their answers. Asking which sports are most popular could allow for more variation in results.

You must be logged in to be able to give a rating.

Wow, thank you! Please rate us on Google too! We look forward to it!

• Proven learning success
• Gamification
• Tutorial Videos
• Interactive books
• Give Us Feedback
• Terms & Conditions
• Privacy Policy
• Do Not Sell My Personal Information

We are using cookies and various third-party services to optimize our platform for you, to continually improve our content and offerings for you and to measure and manage our advertising. You can find detailed information in our privacy policy .

You can accept all cookies and embedded services or choose in the setting, which cookies you want to accept.

Cookies that are necessary for delivering our services or for using our website can’t be rejected.

To provide additional functionality on our platform and to continually optimize our website, we are using our own cookies and third-party services, for example Olark, Hotjar, Userlane and Amplitude.

For the analysis and optimization of our platform, our content and our offerings, we are using our own cookies as well as various third-party services, for example Google Analytics.

For measuring and controlling our marketing and managing our advertising, we are using our own cookies as well as various third-party services, for example Google Adwords/Doubleclick, Bing, Youtube, Facebook, TikTok, Pinterest, LinkedIn, Taboola and Outbrain.

educational research techniques

Research techniques and education.

Research Questions, Variables, and Statistics

Working with students over the years has led me to the conclusion that often students do not understand the connection between variables, quantitative research questions and the statistical tools

used to answer these questions. In other words, students will take statistics and pass the class. Then they will take research methods, collect data, and have no idea how to analyze the data even though they have the necessary skills in statistics to succeed.

This means that the students have a theoretical understanding of statistics but struggle in the application of it. In this post, we will look at some of the connections between research questions and statistics.

Variables are important because how they are measured affects the type of question you can ask and get answers to. Students often have no clue how they will measure a variable and therefore have no idea how they will answer any research questions they may have.

Another aspect that can make this confusing is that many variables can be measured more than one way. Sometimes the variable “salary” can be measured in a continuous manner or in a categorical manner. The superiority of one or the other depends on the goals of the research.

It is critical to support students to have a thorough understanding of variables in order to support their research.

Types of Research Questions

In general, there are two types of research questions. These two types are descriptive and relational questions. Descriptive questions involve the use of descriptive statistic such as the mean, median, mode, skew, kurtosis, etc. The purpose is to describe the sample quantitatively with numbers (ie the average height is 172cm) rather than relying on qualitative descriptions of it (ie the people are tall).

Below are several example research questions that are descriptive in nature.

• What is the average height of the participants in the study?
• What proportion of the sample is passed the exam?
• What are the respondents perceptions towards the cafeteria?

These questions are not intellectually sophisticated but they are all answerable with descriptive statistical tools. Question 1 can be answered by calculating the mean. Question 2 can be answered by determining how many passed the exam and dividing by the total sample size. Question 3 can be answered by calculating the mean of all the survey items that are used to measure respondents perception of the cafeteria.

Understanding the link between research question and statistical tool is critical. However, many people seem to miss the connection between the type of question and the tools to use.

Relational questions look for the connection or link between variables. Within this type there are two sub-types. Comparison question involve comparing groups. The other sub-type is called relational or an association question.

Comparison questions involve comparing groups on a continuous variable. For example, comparing men and women by height. What you want to know is whether there is a difference in the height of men and women. The comparison here is trying to determine if gender is related to height. Therefore, it is looking for a relationship just not in the way that many student understand. Common comparison questions include the following.male

• Is there a difference in height by gender among the participants?
• Is there a difference in reading scores by grade level?
• Is there a difference in job satisfaction in based on major?

Each of these questions can be answered using ANOVA or if we want to get technical and there are only two groups (ie gender) we can use t-test. This is a broad overview and does not include the complexities of one-sample test and or paired t-test.

Relational or association question involve continuous variables primarily. The goal is to see how variables move together. For example, you may look for the relationship between height and weight of students. Common questions include the following.

•  Is there a relationship between height and weight?
• Does height and show size explain weight?

Questions 1 can be answered by calculating the correlation. Question 2 requires the use of linear regression in order to answer the question.

The challenging as a teacher is showing the students the connection between statistics and research questions from the real world. It takes time for students to see how the question inspire the type of statistical tool to use. Understanding this is critical because it helps to frame the possibilities of what to do in research based on the statistical knowledge one has.

Share this:

Leave a reply cancel reply, discover more from educational research techniques.

Subscribe now to keep reading and get access to the full archive.

Type your email…

Continue reading

Top 99+ Trending Statistics Research Topics for Students

Being a statistics student, finding the best statistics research topics is quite challenging. But not anymore; find the best statistics research topics now!!!

Statistics is one of the tough subjects because it consists of lots of formulas, equations and many more. Therefore the students need to spend their time to understand these concepts. And when it comes to finding the best statistics research project for their topics, statistics students are always looking for someone to help them. 

In this blog, we will share with you the most interesting and trending statistics research topics in 2023. It will not just help you to stand out in your class but also help you to explore more about the world.

If you face any problem regarding statistics, then don’t worry. You can get the best statistics assignment help from one of our experts.

As you know, it is always suggested that you should work on interesting topics. That is why we have mentioned the most interesting research topics for college students and high school students. Here in this blog post, we will share with you the list of 99+ awesome statistics research topics.

Why Do We Need to Have Good Statistics Research Topics?

Table of Contents

Having a good research topic will not just help you score good grades, but it will also allow you to finish your project quickly. Because whenever we work on something interesting, our productivity automatically boosts. Thus, you need not invest lots of time and effort, and you can achieve the best with minimal effort and time. 

What Are Some Interesting Research Topics?

If we talk about the interesting research topics in statistics, it can vary from student to student. But here are the key topics that are quite interesting for almost every student:-

• Literacy rate in a city.
• Abortion and pregnancy rate in the USA.
• Eating disorders in the citizens.
• Parent role in self-esteem and confidence of the student.
• Uses of AI in our daily life to business corporates.

Top 99+ Trending Statistics Research Topics For 2023

Here in this section, we will tell you more than 99 trending statistics research topics:

Sports Statistics Research Topics

• Statistical analysis for legs and head injuries in Football.
• Statistical analysis for shoulder and knee injuries in MotoGP.
• Deep statistical evaluation for the doping test in sports from the past decade.
• Statistical observation on the performance of athletes in the last Olympics.
• Role and effect of sports in the life of the student.

Psychology Research Topics for Statistics

• Deep statistical analysis of the effect of obesity on the student’s mental health in high school and college students.
• Statistical evolution to find out the suicide reason among students and adults.
• Statistics analysis to find out the effect of divorce on children in a country.
• Psychology affects women because of the gender gap in specific country areas.
• Statistics analysis to find out the cause of online bullying in students’ lives. 
• In Psychology, PTSD and descriptive tendencies are discussed.
• The function of researchers in statistical testing and probability.
• Acceptable significance and probability thresholds in clinical Psychology.
• The utilization of hypothesis and the role of P 0.05 for improved comprehension.
• What types of statistical data are typically rejected in psychology?
• The application of basic statistical principles and reasoning in psychological analysis.
• The role of correlation is when several psychological concepts are at risk.
• Actual case study learning and modeling are used to generate statistical reports.
• In psychology, naturalistic observation is used as a research sample.
• How should descriptive statistics be used to represent behavioral data sets?

Applied Statistics Research Topics

• Does education have a deep impact on the financial success of an individual?
• The investment in digital technology is having a meaningful return for corporations?
• The gap of financial wealth between rich and poor in the USA.
• A statistical approach to identify the effects of high-frequency trading in financial markets.
• Statistics analysis to determine the impact of the multi-agent model in financial markets. 

Personalized Medicine Statistics Research Topics

• Statistical analysis on the effect of methamphetamine on substance abusers.
• Deep research on the impact of the Corona vaccine on the Omnicrone variant. 
• Find out the best cancer treatment approach between orthodox therapies and alternative therapies.
• Statistics analysis to identify the role of genes in the child’s overall immunity.
• What factors help the patients to survive from Coronavirus .

Experimental Design Statistics Research Topics

• Generic vs private education is one of the best for the students and has better financial return.
• Psychology vs physiology: which leads the person not to quit their addictions?
• Effect of breastmilk vs packed milk on the infant child overall development
• Which causes more accidents: male alcoholics vs female alcoholics.
• What causes the student not to reveal the cyberbullying in front of their parents in most cases. 

Easy Statistics Research Topics

• Application of statistics in the world of data science
• Statistics for finance: how statistics is helping the company to grow their finance
• Advantages and disadvantages of Radar chart
• Minor marriages in south-east Asia and African countries.
• Discussion of ANOVA and correlation.
• What statistical methods are most effective for active sports?
• When measuring the correctness of college tests, a ranking statistical approach is used.
• Statistics play an important role in Data Mining operations.
• The practical application of heat estimation in engineering fields.
• In the field of speech recognition, statistical analysis is used.
• Estimating probiotics: how much time is necessary for an accurate statistical sample?
• How will the United States population grow in the next twenty years?
• The legislation and statistical reports deal with contentious issues.
• The application of empirical entropy approaches with online grammar checking.
• Transparency in statistical methodology and the reporting system of the United States Census Bureau.

Statistical Research Topics for High School

• Uses of statistics in chemometrics
• Statistics in business analytics and business intelligence
• Importance of statistics in physics.
• Deep discussion about multivariate statistics
• Uses of Statistics in machine learning

Survey Topics for Statistics

• Gather the data of the most qualified professionals in a specific area.
• Survey the time wasted by the students in watching Tvs or Netflix.
• Have a survey the fully vaccinated people in the USA 
• Gather information on the effect of a government survey on the life of citizens
• Survey to identify the English speakers in the world.

Statistics Research Paper Topics for Graduates

• Have a deep decision of Bayes theorems
• Discuss the Bayesian hierarchical models
• Analysis of the process of Japanese restaurants. 
• Deep analysis of Lévy’s continuity theorem
• Analysis of the principle of maximum entropy

AP Statistics Topics

• Discuss about the importance of econometrics
• Analyze the pros and cons of Probit Model
• Types of probability models and their uses
• Deep discussion of ortho stochastic matrix
• Find out the ways to get an adjacency matrix quickly

Good Statistics Research Topics 

• National income and the regulation of cryptocurrency.
• The benefits and drawbacks of regression analysis.
• How can estimate methods be used to correct statistical differences?
• Mathematical prediction models vs observation tactics.
• In sociology research, there is bias in quantitative data analysis.
• Inferential analytical approaches vs. descriptive statistics.
• How reliable are AI-based methods in statistical analysis?
• The internet news reporting and the fluctuations: statistics reports.
• The importance of estimate in modeled statistics and artificial sampling.

Business Statistics Topics

• Role of statistics in business in 2023
• Importance of business statistics and analytics
• What is the role of central tendency and dispersion in statistics
• Best process of sampling business data.
• Importance of statistics in big data.
• The characteristics of business data sampling: benefits and cons of software solutions.
• How may two different business tasks be tackled concurrently using linear regression analysis?
• In economic data relations, index numbers, random probability, and correctness are all important.
• The advantages of a dataset approach to statistics in programming statistics.
• Commercial statistics: how should the data be prepared for maximum accuracy?

Statistical Research Topics for College Students

• Evaluate the role of John Tukey’s contribution to statistics.
• The role of statistics to improve ADHD treatment.
• The uses and timeline of probability in statistics.
• Deep analysis of Gertrude Cox’s experimental design in statistics.
• Discuss about Florence Nightingale in statistics.
• What sorts of music do college students prefer?
• The Main Effect of Different Subjects on Student Performance.
• The Importance of Analytics in Statistics Research.
• The Influence of a Better Student in Class.
• Do extracurricular activities help in the transformation of personalities?
• Backbenchers’ Impact on Class Performance.
• Medication’s Importance in Class Performance.
• Are e-books better than traditional books?
• Choosing aspects of a subject in college

How To Write Good Statistics Research Topics?

So, the main question that arises here is how you can write good statistics research topics. The trick is understanding the methodology that is used to collect and interpret statistical data. However, if you are trying to pick any topic for your statistics project, you must think about it before going any further. 

As a result, it will teach you about the data types that will be researched because the sample will be chosen correctly. On the other hand, your basic outline for choosing the correct topics is as follows:

• Introduction of a problem
• Methodology explanation and choice. 
• Statistical research itself is in the main part (Body Part). 
• Samples deviations and variables. 
• Lastly, statistical interpretation is your last part (conclusion). 

Note:   Always include the sources from which you obtained the statistics data.

Top 3 Tips to Choose Good Statistics Research Topics

It can be quite easy for some students to pick a good statistics research topic without the help of an essay writer. But we know that it is not a common scenario for every student. That is why we will mention some of the best tips that will help you choose good statistics research topics for your next project. Either you are in a hurry or have enough time to explore. These tips will help you in every scenario.

1. Narrow down your research topic

We all start with many topics as we are not sure about our specific interests or niche. The initial step to picking up a good research topic for college or school students is to narrow down the research topic.

For this, you need to categorize the matter first. And then pick a specific category as per your interest. After that, brainstorm about the topic’s content and how you can make the points catchy, focused, directional, clear, and specific. 

2. Choose a topic that gives you curiosity

After categorizing the statistics research topics, it is time to pick one from the category. Don’t pick the most common topic because it will not help your grades and knowledge. Instead of it, please choose the best one, in which you have little information, or you are more likely to explore it.

In a statistics research paper, you always can explore something beyond your studies. By doing this, you will be more energetic to work on this project. And you will also feel glad to get them lots of information you were willing to have but didn’t get because of any reasons.

It will also make your professor happy to see your work. Ultimately it will affect your grades with a positive attitude.

3. Choose a manageable topic

Now you have decided on the topic, but you need to make sure that your research topic should be manageable. You will have limited time and resources to complete your project if you pick one of the deep statistics research topics with massive information.

Then you will struggle at the last moment and most probably not going to finish your project on time. Therefore, spend enough time exploring the topic and have a good idea about the time duration and resources you will use for the project. 

Statistics research topics are massive in numbers. Because statistics operations can be performed on anything from our psychology to our fitness. Therefore there are lots more statistics research topics to explore. But if you are not finding it challenging, then you can take the help of our statistics experts . They will help you to pick the most interesting and trending statistics research topics for your projects. 

With this help, you can also save your precious time to invest it in something else. You can also come up with a plethora of topics of your choice and we will help you to pick the best one among them. Apart from that, if you are working on a project and you are not sure whether that is the topic that excites you to work on it or not. Then we can also help you to clear all your doubts on the statistics research topic. 

Frequently Asked Questions

Q1. what are some good topics for the statistics project.

Have a look at some good topics for statistics projects:- 1. Research the average height and physics of basketball players. 2. Birth and death rate in a specific city or country. 3. Study on the obesity rate of children and adults in the USA. 4. The growth rate of China in the past few years 5. Major causes of injury in Football

Q2. What are the topics in statistics?

Statistics has lots of topics. It is hard to cover all of them in a short answer. But here are the major ones: conditional probability, variance, random variable, probability distributions, common discrete, and many more. 

Q3. What are the top 10 research topics?

Here are the top 10 research topics that you can try in 2023:

1. Plant Science 2. Mental health 3. Nutritional Immunology 4. Mood disorders 5. Aging brains 6. Infectious disease 7. Music therapy 8. Political misinformation 9. Canine Connection 10. Sustainable agriculture

Related Posts

How to Find the Best Online Statistics Homework Help

Why SPSS Homework Help Is An Important aspect for Students?

A .gov website belongs to an official government organization in the United States.

A lock ( ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

• Healthy Habits
• Data and Statistics
• Resources for Child Development
• Positive Parenting Tips
• Keeping Children with Disabilities Safe

Child Development Data and Statistics

At a glance.

The following websites provide information and tools related to child development data.

Child development data and statistics

Kids Count U.S. Census Data is a national and state-by-state project of the Casey Foundation to track the status of children in the United States. Data available for analysis include family and child demographics, and measures of child educational, social, economic, and physical well-being.

National Survey of Children's Health (NSCH) examines the physical and emotional health of children ages 0-17 years of age. The survey provides data to estimate national and state-level prevalence of physical, emotional, and behavioral child health indicators as well as information on the child's family context and neighborhood environment.

National Survey on Drug Use & Health (formerly National Household Survey on Drug Abuse) is an annual nationwide survey involving interviews with approximately 70,000 randomly selected individuals aged 12 and older. The survey provides national and state-level data on the use of tobacco, alcohol, illicit drugs (including nonmedical use of prescription drugs) and mental health in the United States.

National Health Interview Survey (NHIS) has monitored the health of the nation since 1957. The NHIS is a large-scale household interview survey of a statistically representative sample of the U.S. civilian noninstitutionalized population. Interview data are collected on a broad range of health topics including, caregiver reports of child health, mental health and disability status.

National Health and Nutrition Examination Survey (NHANES) assesses the health and nutritional status of a nationally representative sample of about 5,000 adults and children in the United States. The survey combines interviews and physical examinations and includes demographic, socioeconomic, dietary, and health-related questions. The examination component consists of medical, dental, and physiological measurements, as well as laboratory tests administered by highly trained medical personnel.

National Survey of Family Growth (NSFG) gathers information on family life, marriage and divorce, pregnancy, infertility, use of contraception, and general and reproductive health.

National Vital Statistics System (NVSS) contains vital statistics from the official records of live births, deaths, fetal deaths, marriages, divorces, and annulment recorded by states and independent registration areas

Youth Risk Behavior Surveillance System (YRBSS) is an ongoing national school-based survey designed to monitor priority health-risk behaviors and the prevalence of obesity and asthma among youth and young adults in grades 9-12 in the U.S. Data were collected from students biennially beginning in 1991. Approximately 16,000 students completed the survey in the most recent data collection wave in 2009.

America's Children is a report on key national indicators of well-being published annually by the Federal Interagency Forum on Child and Family Statistics. The America's Children series makes federal data on children and families available in a nontechnical, easy-to-use format in order to stimulate discussion among data providers, policymakers, and the public.

Healthy People 2030 provides science-based, 10-year national objectives for improving the health of all Americans, including infants, children, and adolescents.

NCHS Survey Measures Catalog of the National Center on Health Statistics provides an overview of questions about child and adolescent mental health, and functioning and disability in various surveys of the NCHS Data Systems. Some of the survey measures are included in both the mental health section and the functioning and disability section of the catalog.

Feature articles

Treatment of Disruptive Behavior Problems – What Works? Learn about current evidence on the most effective treatments for behavior problems in children.

Supporting Parents To Help Children Thrive Learn how CDC helps parents get the support and information they need.

Developmental Milestones Matter! Learn about the CDC Milestone Tracker.

Helping Children Thrive in Rural Communities Information on children with mental, behavioral, and developmental disorders in rural and urban areas.

Child Development

The early years of a child’s life are very important for their health and development. Parents, health professionals, educators, and others can work together as partners to help children grow up to reach their full potential.

• SUGGESTED TOPICS
• The Magazine
• Newsletters
• Managing Yourself
• Managing Teams
• Work-life Balance
• The Big Idea
• Data & Visuals
• Reading Lists
• Case Selections
• HBR Learning
• Topic Feeds
• Account Settings
• Email Preferences

Research: What Companies Don’t Know About How Workers Use AI

• Jeremie Brecheisen

Three Gallup studies shed light on when and why AI is being used at work — and how employees and customers really feel about it.

Leaders who are exploring how AI might fit into their business operations must not only navigate a vast and ever-changing landscape of tools, but they must also facilitate a significant cultural shift within their organizations. But research shows that leaders do not fully understand their employees’ use of, and readiness for, AI. In addition, a significant number of Americans do not trust business’ use of AI. This article offers three recommendations for leaders to find the right balance of control and trust around AI, including measuring how their employees currently use AI, cultivating trust by empowering managers, and adopting a purpose-led AI strategy that is driven by the company’s purpose instead of a rules-heavy strategy that is driven by fear.

If you’re a leader who wants to shift your workforce toward using AI, you need to do more than manage the implementation of new technologies. You need to initiate a profound cultural shift. At the heart of this cultural shift is trust. Whether the use case for AI is brief and experimental or sweeping and significant, a level of trust must exist between leaders and employees for the initiative to have any hope of success.

• Jeremie Brecheisen is a partner and managing director of The Gallup CHRO Roundtable.

Partner Center

User Preferences

Content preview.

Arcu felis bibendum ut tristique et egestas quis:

• Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris
• Duis aute irure dolor in reprehenderit in voluptate
• Excepteur sint occaecat cupidatat non proident

Keyboard Shortcuts

1.2 - asking research questions, overview section  .

Suppose you desire to do a study or administer a survey. As an investigator, the most challenging task that you will confront is to decide what questions to ask and/or what measurements to obtain. In this lesson, you will be introduced to some key concepts associated with obtaining measurements. You will also learn about possible pitfalls found with survey questions.

It's All in the Wording

There are many possible pitfalls that can lead to bias when asking questions in a survey or study. In thinking about the veracity of results from a sample survey ask yourself if any of these pitfalls are likely to be a problem. It is also possible that more than one type of pitfall can happen at the same time. Examine the following examples.

Example 1.4 Deliberate Bias such as One-Sided Statements Section  

People who use a form of deliberate bias often desire to gather support for a specific cause or opinion. Consider two different wordings for a particular question:

Wording 1 : It is hard for today's college graduates to have a bright future with the way things are today in the world.

Wording 2 : Today's college graduates will have a bright future.

Although Wording 1 and Wording 2 are contradictory statements, when both questions are used in the same survey, it is not uncommon to find that people answer "agree" to both questions. This is because respondents tend to agree to one-sided statements. Listed below are revised wordings for these two questions. These choices are preferred because the statements are now at least two-sided.

Revised Wording 1 : Do you agree or disagree that it is hard for today's college graduates to have a bright future with the way things are today in the world?

Revised Wording 2 : Do you agree or disagree that today's college graduates will have a bright future?

Example 1.5. Filtering Section  

Consider two different choices of answers for a particular question:

Choice 1 : What is your opinion of our current President?

• unfavorable

Choice 2 : What is your opinion of our current President?

This example illustrates the problem of "filtering." Filtering exists when certain choices such as "undecided" or "don't know" are not included in the list of possible answers. People tend to provide an answer of "undecided" or "don't know" only when these choices are included in the list of possible answers.

Example 1.6. Importance of Order Section  

Consider two different wordings for a particular question:

Wording 1 : Pick a color: red or blue?

Wording 2 : Pick a color: blue or red?

The results in Table 1.1 are from a study conducted in a Statistics class. As you can see the results vary somewhat based on the order in which the colors are presented. Even though many people probably have a preference for one color over the other, if order does not matter, the percents should be same with each wording.

Table 1.1. Bias due to Order of Comparisons

Example 1.7. Anchoring Section  

Wording 1 : Knowing that the population of the U.S. is 316 million, what is the population of Canada?

Wording 2 : Knowing that the population of Australia is 23 million, what is the population of Canada?

This survey was conducted in Stat 100 classes where both wordings of the question were randomly distributed.  The students did not know that there were two versions of this question so each only answered the question that they received. The results of this survey are found in Figure 1.4 .

Figure 1.4. STAT 100 Survey Results

As you can see, the students were influenced by the wording of the question that they were asked to answer. People's perceptions can be severely distorted when they are provided with a reference point or an anchor. People tend to say close to the anchor because of either having limited knowledge about the topic or being distracted by the anchor. You should also consider the following three points:

• The sample sizes were large enough to detect a difference between the two groups
• Canada's population is about 35 million
• The anchor might be less distracting if the following wording were used: "What is the population of Canada when knowing that the population of the U.S. is 316 million?" but it is best to leave out the anchoring statement altogether.

Example 1.8. Unintentional Bias Section  

Wording 1 : Do you favor or oppose an ordinance that forbids surveillance cameras to be placed on Beaver Avenue?

Wording 2 : Do you favor or oppose an ordinance that does not allow surveillance cameras to be placed on Beaver Avenue?

People will tend to answer "oppose" or "no" to a question that contains words such as forbid, control , ban , outlaw , and restraint regardless of what question is actually being asked. People do not like to be told that they can't do something. So the responses to the two questions would not provide similar results. Wording 2 would be preferred over Wording 1.

Example 1.9. Unnecessary Complexity ("Double-Barreled" Problem) Section  

Consider the following question.

Question : Do you think that health care workers and military personnel should be the first to receive the smallpox vaccination?

The problem with this question is that the respondent must consider both health care workers and military personnel at the same time. The following rewording is much better.

Revised Question : Who should have priority in receiving the smallpox vaccination?

• health care workers
• military personnel
• both health care workers and military personnel

Example 1.10. Asking the Uninformed and Unnecessary Complexity (Double Negative Problem and List Problem) Section  

Consider the following question:

Question : Do you agree or disagree that children who have a Body Mass Index (BMI) at or above the 95th percentile should not be allowed to spend a lot of time watching television, playing computer games, and listening to music?

The first concern with this question is that many people may not clearly understand what the Body Mass Index (BMI) represents. BMI is a measure that is used to identify obesity and is calculated by dividing a person's weight (in kilograms) by the square of their height (in meters). (Note: many Web sites have BMI calculators.) In children and adolescents, obesity is defined as a BMI for age and gender at or above the 95th percentile. This definition should be included prior to the listing of the question on a survey.

This question can also cause problems because of a possible "double negative". Specifically, the problem is with the "disagree" choice. This choice produces a double negative because "disagree" and "should not" are both in the statement. Many respondents will not understand what they are really saying. (It is easy to make the mistake of the double negative).

Revised Question-First Revision : Do you agree or disagree that children who have a Body Mass Index (BMI) at or above the 95th percentile should spend less time watching television, playing computer games, and listening to music?

As you examine this revised question you should also note that there still is a list of three choices embedded in the questions. Respondents sometimes can get hung up on the list of choices (see the "double-barreled" problem above). For example, they may feel that watching television is a bad idea for obese children but listening to music is not.

• Skip to main content
• Keyboard shortcuts for audio player

Your Health

• Treatments & Tests
• Health Inc.
• Public Health

Why writing by hand beats typing for thinking and learning

Jonathan Lambert

If you're like many digitally savvy Americans, it has likely been a while since you've spent much time writing by hand.

The laborious process of tracing out our thoughts, letter by letter, on the page is becoming a relic of the past in our screen-dominated world, where text messages and thumb-typed grocery lists have replaced handwritten letters and sticky notes. Electronic keyboards offer obvious efficiency benefits that have undoubtedly boosted our productivity — imagine having to write all your emails longhand.

To keep up, many schools are introducing computers as early as preschool, meaning some kids may learn the basics of typing before writing by hand.

But giving up this slower, more tactile way of expressing ourselves may come at a significant cost, according to a growing body of research that's uncovering the surprising cognitive benefits of taking pen to paper, or even stylus to iPad — for both children and adults.

Is this some kind of joke? A school facing shortages starts teaching standup comedy

In kids, studies show that tracing out ABCs, as opposed to typing them, leads to better and longer-lasting recognition and understanding of letters. Writing by hand also improves memory and recall of words, laying down the foundations of literacy and learning. In adults, taking notes by hand during a lecture, instead of typing, can lead to better conceptual understanding of material.

"There's actually some very important things going on during the embodied experience of writing by hand," says Ramesh Balasubramaniam , a neuroscientist at the University of California, Merced. "It has important cognitive benefits."

While those benefits have long been recognized by some (for instance, many authors, including Jennifer Egan and Neil Gaiman , draft their stories by hand to stoke creativity), scientists have only recently started investigating why writing by hand has these effects.

A slew of recent brain imaging research suggests handwriting's power stems from the relative complexity of the process and how it forces different brain systems to work together to reproduce the shapes of letters in our heads onto the page.

Your brain on handwriting

Both handwriting and typing involve moving our hands and fingers to create words on a page. But handwriting, it turns out, requires a lot more fine-tuned coordination between the motor and visual systems. This seems to more deeply engage the brain in ways that support learning.

Shots - Health News

Feeling artsy here's how making art helps your brain.

"Handwriting is probably among the most complex motor skills that the brain is capable of," says Marieke Longcamp , a cognitive neuroscientist at Aix-Marseille Université.

Gripping a pen nimbly enough to write is a complicated task, as it requires your brain to continuously monitor the pressure that each finger exerts on the pen. Then, your motor system has to delicately modify that pressure to re-create each letter of the words in your head on the page.

"Your fingers have to each do something different to produce a recognizable letter," says Sophia Vinci-Booher , an educational neuroscientist at Vanderbilt University. Adding to the complexity, your visual system must continuously process that letter as it's formed. With each stroke, your brain compares the unfolding script with mental models of the letters and words, making adjustments to fingers in real time to create the letters' shapes, says Vinci-Booher.

That's not true for typing.

To type "tap" your fingers don't have to trace out the form of the letters — they just make three relatively simple and uniform movements. In comparison, it takes a lot more brainpower, as well as cross-talk between brain areas, to write than type.

Recent brain imaging studies bolster this idea. A study published in January found that when students write by hand, brain areas involved in motor and visual information processing " sync up " with areas crucial to memory formation, firing at frequencies associated with learning.

"We don't see that [synchronized activity] in typewriting at all," says Audrey van der Meer , a psychologist and study co-author at the Norwegian University of Science and Technology. She suggests that writing by hand is a neurobiologically richer process and that this richness may confer some cognitive benefits.

Other experts agree. "There seems to be something fundamental about engaging your body to produce these shapes," says Robert Wiley , a cognitive psychologist at the University of North Carolina, Greensboro. "It lets you make associations between your body and what you're seeing and hearing," he says, which might give the mind more footholds for accessing a given concept or idea.

Those extra footholds are especially important for learning in kids, but they may give adults a leg up too. Wiley and others worry that ditching handwriting for typing could have serious consequences for how we all learn and think.

What might be lost as handwriting wanes

The clearest consequence of screens and keyboards replacing pen and paper might be on kids' ability to learn the building blocks of literacy — letters.

"Letter recognition in early childhood is actually one of the best predictors of later reading and math attainment," says Vinci-Booher. Her work suggests the process of learning to write letters by hand is crucial for learning to read them.

"When kids write letters, they're just messy," she says. As kids practice writing "A," each iteration is different, and that variability helps solidify their conceptual understanding of the letter.

Research suggests kids learn to recognize letters better when seeing variable handwritten examples, compared with uniform typed examples.

This helps develop areas of the brain used during reading in older children and adults, Vinci-Booher found.

"This could be one of the ways that early experiences actually translate to long-term life outcomes," she says. "These visually demanding, fine motor actions bake in neural communication patterns that are really important for learning later on."

Ditching handwriting instruction could mean that those skills don't get developed as well, which could impair kids' ability to learn down the road.

"If young children are not receiving any handwriting training, which is very good brain stimulation, then their brains simply won't reach their full potential," says van der Meer. "It's scary to think of the potential consequences."

Many states are trying to avoid these risks by mandating cursive instruction. This year, California started requiring elementary school students to learn cursive , and similar bills are moving through state legislatures in several states, including Indiana, Kentucky, South Carolina and Wisconsin. (So far, evidence suggests that it's the writing by hand that matters, not whether it's print or cursive.)

Slowing down and processing information

For adults, one of the main benefits of writing by hand is that it simply forces us to slow down.

During a meeting or lecture, it's possible to type what you're hearing verbatim. But often, "you're not actually processing that information — you're just typing in the blind," says van der Meer. "If you take notes by hand, you can't write everything down," she says.

The relative slowness of the medium forces you to process the information, writing key words or phrases and using drawing or arrows to work through ideas, she says. "You make the information your own," she says, which helps it stick in the brain.

Such connections and integration are still possible when typing, but they need to be made more intentionally. And sometimes, efficiency wins out. "When you're writing a long essay, it's obviously much more practical to use a keyboard," says van der Meer.

Still, given our long history of using our hands to mark meaning in the world, some scientists worry about the more diffuse consequences of offloading our thinking to computers.

"We're foisting a lot of our knowledge, extending our cognition, to other devices, so it's only natural that we've started using these other agents to do our writing for us," says Balasubramaniam.

It's possible that this might free up our minds to do other kinds of hard thinking, he says. Or we might be sacrificing a fundamental process that's crucial for the kinds of immersive cognitive experiences that enable us to learn and think at our full potential.

Balasubramaniam stresses, however, that we don't have to ditch digital tools to harness the power of handwriting. So far, research suggests that scribbling with a stylus on a screen activates the same brain pathways as etching ink on paper. It's the movement that counts, he says, not its final form.

Jonathan Lambert is a Washington, D.C.-based freelance journalist who covers science, health and policy.

• handwriting

Numbers, Facts and Trends Shaping Your World

Read our research on:

Full Topic List

Regions & Countries

• Publications
• Our Methods
• Short Reads
• Tools & Resources

Read Our Research On:

• Half of Latinas Say Hispanic Women’s Situation Has Improved in the Past Decade and Expect More Gains

Government data shows gains in education, employment and earnings for Hispanic women, but gaps with other groups remain

Table of contents.

• Assessing the progress of Hispanic women in the last 10 years
• Views of Hispanic women’s situation in the next 10 years
• Views on the gender pay gap
• Latinas’ educational attainment
• Latinas’ labor force participation
• Latinas’ earnings
• Latinas as breadwinners in their relationships
• Bachelor’s degrees among Latinas
• Labor force participation rates among Latinas
• Occupations among working Latinas
• Earnings among Latinas
• Latinas as breadwinners in 2022
• Appendix: Supplemental charts and tables
• Acknowledgments
• The American Trends Panel survey methodology
• Methodology for the analysis of the Current Population Survey

This report explores Latinas’ economic and demographic progress in the last two decades – and their perceptions of that progress – using several data sources.

The first is a Pew Research Center survey of 5,078 Hispanic adults, including 2,600 Hispanic women. Respondents were asked whether U.S. Latinas saw progress in their situation in the last decade, whether they expected any in the future decade, and how big a problem the U.S. gender pay gap is. The survey was conducted from Nov. 6 to 19, 2023, and includes 1,524 respondents from the American Trends Panel (ATP) and an additional 3,554 from Ipsos’ KnowledgePanel .

Respondents on both panels are recruited through national, random sampling of residential addresses. Recruiting panelists by mail ensures that nearly all U.S. adults have a chance of selection. This gives us confidence that any sample can represent the whole population, or in this case the whole U.S. Hispanic population. (For more information, watch our Methods 101 explainer on random sampling.) For more information on this survey, refer to the American Trends Panel survey methodology and the topline questionnaire .

The second data source is the U.S. Census Bureau’s and Bureau of Labor Statistics’ 2003, 2008, 2013, 2018 and 2023 Current Population Survey (CPS) Monthly and Annual Social and Economic Supplement (ASEC) data series, provided through the Integrated Public Use Microdata Series (IPUMS) from the University of Minnesota.

The CPS Monthly microdata series was used only to calculate median hourly earnings for those ages 25 to 64 years old and who were not self-employed. Medians were calculated for the whole year by considering all wages reported in that year, regardless of month. Median wages were then adjusted to June 2023 dollars using the Chained Consumer Price Index for All Urban Consumers for June of each year. For more information on the demographic analysis, refer to the methodology for the analysis of the Current Population Survey .

The terms  Hispanic  and  Latino  are used interchangeably in this report.

The terms Latinas and Hispanic women are used interchangeably throughout this report to refer to U.S. adult women who self-identify as Hispanic or Latino, regardless of their racial identity.

Foreign born  refers to persons born outside of the 50 U.S. states or the District of Columbia. For the purposes of this report, foreign born also refers to those born in Puerto Rico. Although individuals born in Puerto Rico are U.S. citizens by birth, they are grouped with the foreign born because they are born into a Spanish-dominant culture and because on many points their attitudes, views and beliefs are much closer to those of Hispanics born outside the U.S. than to Hispanics born in the 50 U.S. states or D.C., even those who identify themselves as being of Puerto Rican origin.

The terms  foreign born  and  immigrant  are used interchangeably in this report. Immigrants are also considered first-generation Americans.

U.S. born  refers to persons born in the 50 U.S. states or D.C.

Second generation  refers to people born in the 50 U.S. states or D.C. with at least one immigrant parent.

Third or higher generation  refers to people born in the 50 U.S. states or D.C., with both parents born in the 50 U.S. states or D.C.

Throughout this report, Democrats are respondents who identify politically with the Democratic Party or those who are independent or identify with some other party but lean toward the Democratic Party. Similarly, Republicans are those who identify politically with the Republican Party and those who are independent or identify with some other party but lean toward the Republican Party.

White, Black  and  Asian each include those who report being only one race and are not Hispanic.

Civilians are those who were not in the armed forces at the time of completing the Current Population Survey.

Those participating in the labor force either were at work; held a job but were temporarily absent from work due to factors like vacation or illness; were seeking work; or were temporarily laid off from a job in the week before taking the Current Population Survey. In this report, the labor force participation rate is shown only for civilians ages 25 to 64.

The phrases living with children or living with their own child describe individuals living with at least one of their own stepchildren, adopted children or biological children, regardless of the children’s ages. The phrases not living with children or not living with their own child describe individuals who have no children or whose children do not live with them.

Occupation and occupational groups describe the occupational category of someone’s current job, or – if unemployed – most recent job. In this report we measure occupation among civilians participating in the labor force. Occupational groups are adapted from the U.S. Census Bureau’s occupation classification list from 2018 onward .

Hourly earnings , hourly wages and hourly pay all refer to the amount an employee reported making per hour at the time of taking the Current Population Survey where they were employed by someone else. Median hourly wages were calculated only for those ages 25 to 64 who were not self-employed. Calculated median hourly wages shared in this report are adjusted for inflation to 2023. (A median means that half of a given population – for example, Hispanic women – earned more than the stated wage, and half earned less.)

Breadwinners refer to those living with a spouse or partner, both ages 25 to 64, who make over 60% of their and their partner’s combined, positive income from all sources. Those in egalitarian relationships make 40% to 60% of the combined income. For those who make less than 40% of the combined income, their spouse or partner is the breadwinner . This analysis was conducted among both opposite-sex and same-sex couples.

Half of Latinas say the situation of Hispanic women in the United States is better now than it was 10 years ago, and a similar share say the situation will improve in the next 10 years.

Still, 39% of Latinas say that the situation has stayed the same, and 34% say it will not change in the next 10 years. Two-thirds (66%) say the gender pay gap – the fact that women earn less money, on average, than men – is a big problem for Hispanic women today, according to new analysis of Pew Research Center’s National Survey of Latinos.

At 22.2 million, Latinas account for 17% of all adult women in the U.S. today. Their population grew by 5.6 million from 2010 to 2022, the largest numeric increase of any major female racial or ethnic group. 1

Latinas’ mixed assessments reflect their group’s gains in education and at work over the last two decades, but also stalled progress in closing wage gaps with other groups.

• Hispanic women are more likely to have a bachelor’s degree today (23% in 2023) than they were in 2013 (16%). More Hispanic women than ever are also completing graduate degrees .
• Hispanic women have increased their labor force participation rate by 4 percentage points, from 65% in 2013 to 69% in 2023.
• The median hourly wage of Hispanic women has increased by 17% in the last decade. In 2023, their median hourly wage was $19.23, up from $16.47 in 2013 (in 2023 dollars).

Despite this progress, Hispanic women’s pay gaps with their peers haven’t significantly improved in recent years:

• The gender pay gap among Hispanics persists with no significant change. In 2023, Hispanic women earned 85 cents (at the median) for every dollar earned by Hispanic men, compared with 89 cents per dollar in 2013 (and 87 cents per dollar in 2003).
• Hispanic women continue to lag non-Hispanic women in earnings , with no significant improvement in the past decade. In 2023, the median Hispanic woman made 77 cents for each dollar earned by the median non-Hispanic woman, compared with 75 cents per dollar in 2013.
• The pay gap between Hispanic women and White men has changed only slightly . In 2023, Hispanic women earned 62 cents of every dollar earned by non-Hispanic White men, up from 59 cents per dollar in 2013.

In addition, Hispanic women lag Hispanic men and non-Hispanic women in labor force participation, and they lag non-Hispanic women in educational attainment. Read more in Chapter 2 .

Among Latinas who are employed, about half (49%) say their current job is best described as “just a job to get them by.” Fewer see their job as a career (30%) or a steppingstone to a career (14%).

Pew Research Center’s bilingual 2023 National Survey of Latinos – conducted Nov. 6-19, 2023, among 5,078 Hispanic adults, including 2,600 Hispanic women – explores what it’s like to be a Latina in the U.S. today. This report uses findings from our 2023 survey as well as demographic and economic data from the Current Population Survey.

The following chapters take a closer look at:

• How Latinas view the progress and future situation of Hispanic women in the U.S.
• What government data tells us about Latinas’ progress in the labor market, earnings and educational attainment
• How Latinas’ educational and economic outcomes vary

For additional survey findings on what it means to be a Latina in the U.S. today and the daily pressures they face, read our report “A Majority of Latinas Feel Pressure To Support Their Families or To Succeed at Work.”

• Latinas’ population size and growth rate from 2010 to 2022 were calculated using the 2010 and 2022 American Community Surveys, accessed through IPUMS. The rest of the demographic analysis in this post uses data from the Current Population Survey. ↩

Sign up for our weekly newsletter

Fresh data delivery Saturday mornings

Sign up for The Briefing

Weekly updates on the world of news & information

• Economics, Work & Gender
• Education & Gender
• Educational Attainment
• Gender & Work
• Gender Equality & Discrimination
• Gender Pay Gap
• Higher Education
• Hispanics/Latinos
• Hispanics/Latinos & Education

Key facts about U.S. Latinos with graduate degrees

Hispanic enrollment reaches new high at four-year colleges in the u.s., but affordability remains an obstacle, u.s. public school students often go to schools where at least half of their peers are the same race or ethnicity, what’s behind the growing gap between men and women in college completion, for u.s. latinos, covid-19 has taken a personal and financial toll, most popular, report materials.

1615 L St. NW, Suite 800 Washington, DC 20036 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries

Research Topics

• Age & Generations
• Coronavirus (COVID-19)
• Economy & Work
• Family & Relationships
• Gender & LGBTQ
• Immigration & Migration
• International Affairs
• Internet & Technology
• Methodological Research
• News Habits & Media
• Non-U.S. Governments
• Other Topics
• Politics & Policy
• Race & Ethnicity
• Email Newsletters

ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of  The Pew Charitable Trusts .

Copyright 2024 Pew Research Center

The doctor is in.... but what's behind them?

Americans have gotten used to seeing their doctors and other health care providers using telehealth video visits in the past four years. But a new study reveals that what a doctor has behind them during a telehealth visit can make a difference in how the patient feels about them and their care.

Even if the doctor is miles away from their usual in-person clinic or exam room, they should make it look like they're there, the study suggests.

Even better: sitting in an office with their diplomas hanging behind them -- or perhaps having a virtual background that's a photo of such an office. This is especially true if they haven't seen the patient before, the study shows.

A home office with a bookshelf or a plain solid-color background are both acceptable to patients, too.

But providers should use blurred or virtual backgrounds if they carry out the visit in a home environment with a kitchen or a bed in the background, the study shows.

The findings come from a survey that asked patients to react to seven different backgrounds behind a model physician, and to rate how knowledgeable, trustworthy, caring, approachable and professional the physician appeared in each, and how comfortable the patient would feel with that provider. It also asked them to consider each background for a first or returning appointment with a primary care or specialty provider.

The study is published in JAMA Network Open by a team from the University of Michigan's academic medical center, Michigan Medicine, and the VA Ann Arbor Healthcare System. More than 1,200 patients who had seen providers at one of the two health systems completed the study surveys, and the researchers compiled their responses.

Lead researcher Nathan Houchens, M.D., is an associate professor of internal medicine at U-M and associate chief of medicine at VAAAHS. His past work on how interpersonal communications affects the patient-provider relationship -- including non-verbal factors like attire and posture -- led to the new telehealth study.

"The transition to virtual care was rapid and came without specific guidance during the start of the COVID-19 pandemic, but telehealth appears to be here to stay so it's important to understand what patients prefer when it comes to the setting their provider is in," says Houchens, a hospitalist who worked with U-M and VA general internist Jennifer Meddings, M.D., M.Sc. and others on the study.

He notes that during the first year of the pandemic, providers were urged to conduct telehealth visits outside of clinics if they didn't need to go in, to reduce the chance of COVID-19 transmission.

But now, some clinics have created dedicated spaces for providers to sit in if they have telehealth appointments on days when they're also seeing patients in person. Some of those might be spaces shared with other clinicians, so a virtual background would also serve to reduce visual distractions.

Houchens notes that as telehealth increased in use and became a standard way to receive care, some guidance on "webside manner" has been suggested to guide providers in the ways in which they interact verbally over a virtual connection. But very little guidance is available about the background for their video visits.

He and his colleagues were surprised at the level of dislike that patients had for kitchen and bedroom settings, with only 2% and 3.5% saying they preferred these backgrounds respectively, compared with 35% for an office with displayed diplomas, 18% for a physician office, 14% for a plain color background, and around the same for a home office with bookshelf or an exam room.

There were also significant differences in the composite scores for how patients rated the way each background would make them feel about receiving care from the provider. The bedroom and kitchen backgrounds received far lower composite scores than any of the other five backgrounds.

Houchens and colleagues including co-author Sanjay Saint, M.D., M.P.H., have previously published work on patients' preferences for what physicians wear during clinical encounters. Just like with video visit background, these seemingly superficial factors can actually make a difference in the patient experience, he says.

"Patients have expectations of what physicians' attire and workspaces should look like. This study showed that patients prefer what have been previously termed traditional or professional attire and settings," he said. "Diplomas and credentials remind patients of the expertise they expect a physician to have, and conversely, something is lost when the background conveys a relaxed, informal home environment."

The team is currently analyzing more data from the same study, to assess other factors that affect patients' telehealth experiences -- including their access to high-speed internet and their ability to use necessary technologies.

But for now, they suggest that providers can take immediate steps to conduct virtual visits from an office or exam room. Clinics may want to make unused clinical rooms available for use by providers conducting virtual visits during in-person clinic days.

Another option is to create virtual backgrounds that will evoke these types of professional settings.

Houchens also notes that while they haven't yet studied what physicians think of the backgrounds behind patients during video visits, these may provide helpful information.

The rise of "Hospital at Home" and home-based primary care means that patients with more acute conditions may be able to see their providers virtually, and that their setting can give clues to the way physical and social factors play a role in their health. Discussing visible elements from both a provider's and a patient's virtual background -- art and other hobby-related items, for example -- can also help build rapport, Houchens notes.

"This is a reminder that patients often do care about some of the details that providers and health systems may not have emphasized," he said. "It's important to remember that our words and our nonverbal behaviors are taken to heart by those we care for, and it behooves us to care about them as well."

Meddings and Saint are members of the VA Center for Clinical Management Research and the U-M Institute for Healthcare Policy and Innovation.

In addition to Houchens, Meddings and Saint, the study's authors are Latoya Kuhn MPH, David Ratz MS, Jason M. Engle MPH of VA CCMR.

• Today's Healthcare
• Patient Education and Counseling
• Health Policy
• Wounds and Healing
• Diseases and Conditions
• Multiple Sclerosis Research
• Medical Topics
• Therapy dog
• Athletic training
• Palliative care
• Personalized medicine
• Sports medicine
• Health science

Story Source:

Materials provided by Michigan Medicine - University of Michigan . Original written by Kara Gavin. Note: Content may be edited for style and length.

Journal Reference :

• Nathan Houchens, Sanjay Saint, Latoya Kuhn, David Ratz, Jason M. Engle, Jennifer Meddings. Patient Preferences for Telemedicine Video Backgrounds . JAMA Network Open , 2024; 7 (5): e2411512 DOI: 10.1001/jamanetworkopen.2024.11512

Cite This Page :

Explore More

• Toward a Successful Vaccine for HIV
• Highly Efficient Thermoelectric Materials
• Toward Human Brain Gene Therapy
• Whale Families Learn Each Other's Vocal Style
• AI Can Answer Complex Physics Questions
• Otters Use Tools to Survive a Changing World
• Monogamy in Mice: Newly Evolved Type of Cell
• Sustainable Electronics, Doped With Air
• Male Vs Female Brain Structure
• Breeding 'Carbon Gobbling' Plants

Trending Topics

Strange & offbeat.