quantitative research in educational psychology

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In This Article Expand or collapse the "in this article" section Quantitative Research Designs in Educational Research

Introduction, general overviews.

• Survey Research Designs
• Correlational Designs
• Other Nonexperimental Designs
• Randomized Experimental Designs
• Quasi-Experimental Designs
• Single-Case Designs
• Single-Case Analyses

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• Methodologies for Conducting Education Research
• Mixed Methods Research
• Multivariate Research Methodology
• Qualitative Data Analysis Techniques
• Qualitative, Quantitative, and Mixed Methods Research Sampling Strategies
• Researcher Development and Skills Training within the Context of Postgraduate Programs
• Single-Subject Research Design
• Social Network Analysis
• Statistical Assumptions

Other Subject Areas

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Quantitative Research Designs in Educational Research by James H. McMillan , Richard S. Mohn , Micol V. Hammack LAST REVIEWED: 24 July 2013 LAST MODIFIED: 24 July 2013 DOI: 10.1093/obo/9780199756810-0113

The field of education has embraced quantitative research designs since early in the 20th century. The foundation for these designs was based primarily in the psychological literature, and psychology and the social sciences more generally continued to have a strong influence on quantitative designs until the assimilation of qualitative designs in the 1970s and 1980s. More recently, a renewed emphasis on quasi-experimental and nonexperimental quantitative designs to infer causal conclusions has resulted in many newer sources specifically targeting these approaches to the field of education. This bibliography begins with a discussion of general introductions to all quantitative designs in the educational literature. The sources in this section tend to be textbooks or well-known sources written many years ago, though still very relevant and helpful. It should be noted that there are many other sources in the social sciences more generally that contain principles of quantitative designs that are applicable to education. This article then classifies quantitative designs primarily as either nonexperimental or experimental but also emphasizes the use of nonexperimental designs for making causal inferences. Among experimental designs the article distinguishes between those that include random assignment of subjects, those that are quasi-experimental (with no random assignment), and those that are single-case (single-subject) designs. Quasi-experimental and nonexperimental designs used for making causal inferences are becoming more popular in education given the practical difficulties and expense in conducting well-controlled experiments, particularly with the use of structural equation modeling (SEM). There have also been recent developments in statistical analyses that allow stronger causal inferences. Historically, quantitative designs have been tied closely to sampling, measurement, and statistics. In this bibliography there are important sources for newer statistical procedures that are needed for particular designs, especially single-case designs, but relatively little attention to sampling or measurement. The literature on quantitative designs in education is not well focused or comprehensively addressed in very many sources, except in general overview textbooks. Those sources that do include the range of designs are introductory in nature; more advanced designs and statistical analyses tend to be found in journal articles and other individual documents, with a couple exceptions. Another new trend in educational research designs is the use of mixed-method designs (both quantitative and qualitative), though this article does not emphasize these designs.

For many years there have been textbooks that present the range of quantitative research designs, both in education and the social sciences more broadly. Indeed, most of the quantitative design research principles are much the same for education, psychology, and other social sciences. These sources provide an introduction to basic designs that are used within the broader context of other educational research methodologies such as qualitative and mixed-method. Examples of these textbooks written specifically for education include Johnson and Christensen 2012 ; Mertens 2010 ; Arthur, et al. 2012 ; and Creswell 2012 . An example of a similar text written for the social sciences, including education that is dedicated only to quantitative research, is Gliner, et al. 2009 . In these texts separate chapters are devoted to different types of quantitative designs. For example, Creswell 2012 contains three quantitative design chapters—experimental, which includes both randomized and quasi-experimental designs; correlational (nonexperimental); and survey (also nonexperimental). Johnson and Christensen 2012 also includes three quantitative design chapters, with greater emphasis on quasi-experimental and single-subject research. Mertens 2010 includes a chapter on causal-comparative designs (nonexperimental). Often survey research is addressed as a distinct type of quantitative research with an emphasis on sampling and measurement (how to design surveys). Green, et al. 2006 also presents introductory chapters on different types of quantitative designs, but each of the chapters has different authors. In this book chapters extend basic designs by examining in greater detail nonexperimental methodologies structured for causal inferences and scaled-up experiments. Two additional sources are noted because they represent the types of publications for the social sciences more broadly that discuss many of the same principles of quantitative design among other types of designs. Bickman and Rog 2009 uses different chapter authors to cover topics such as statistical power for designs, sampling, randomized controlled trials, and quasi-experiments, and educational researchers will find this information helpful in designing their studies. Little 2012 provides a comprehensive coverage of topics related to quantitative methods in the social, behavioral, and education fields.

Arthur, James, Michael Waring, Robert Coe, and Larry V. Hedges, eds. 2012. Research methods & methodologies in education . Thousand Oaks, CA: SAGE.

Readers will find this book more of a handbook than a textbook. Different individuals author each of the chapters, representing quantitative, qualitative, and mixed-method designs. The quantitative chapters are on the treatment of advanced statistical applications, including analysis of variance, regression, and multilevel analysis.

Bickman, Leonard, and Debra J. Rog, eds. 2009. The SAGE handbook of applied social research methods . 2d ed. Thousand Oaks, CA: SAGE.

This handbook includes quantitative design chapters that are written for the social sciences broadly. There are relatively advanced treatments of statistical power, randomized controlled trials, and sampling in quantitative designs, though the coverage of additional topics is not as complete as other sources in this section.

Creswell, John W. 2012. Educational research: Planning, conducting, and evaluating quantitative and qualitative research . 4th ed. Boston: Pearson.

Creswell presents an introduction to all major types of research designs. Three chapters cover quantitative designs—experimental, correlational, and survey research. Both the correlational and survey research chapters focus on nonexperimental designs. Overall the introductions are complete and helpful to those beginning their study of quantitative research designs.

Gliner, Jeffrey A., George A. Morgan, and Nancy L. Leech. 2009. Research methods in applied settings: An integrated approach to design and analysis . 2d ed. New York: Routledge.

This text, unlike others in this section, is devoted solely to quantitative research. As such, all aspects of quantitative designs are covered. There are separate chapters on experimental, nonexperimental, and single-subject designs and on internal validity, sampling, and data-collection techniques for quantitative studies. The content of the book is somewhat more advanced than others listed in this section and is unique in its quantitative focus.

Green, Judith L., Gregory Camilli, and Patricia B. Elmore, eds. 2006. Handbook of complementary methods in education research . Mahwah, NJ: Lawrence Erlbaum.

Green, Camilli, and Elmore edited forty-six chapters that represent many contemporary issues and topics related to quantitative designs. Written by noted researchers, the chapters cover design experiments, quasi-experimentation, randomized experiments, and survey methods. Other chapters include statistical topics that have relevance for quantitative designs.

Johnson, Burke, and Larry B. Christensen. 2012. Educational research: Quantitative, qualitative, and mixed approaches . 4th ed. Thousand Oaks, CA: SAGE.

This comprehensive textbook of educational research methods includes extensive coverage of qualitative and mixed-method designs along with quantitative designs. Three of twenty chapters focus on quantitative designs (experimental, quasi-experimental, and single-case) and nonexperimental, including longitudinal and retrospective, designs. The level of material is relatively high, and there are introductory chapters on sampling and quantitative analyses.

Little, Todd D., ed. 2012. The Oxford handbook of quantitative methods . Vol. 1, Foundations . New York: Oxford Univ. Press.

This handbook is a relatively advanced treatment of quantitative design and statistical analyses. Multiple authors are used to address strengths and weaknesses of many different issues and methods, including advanced statistical tools.

Mertens, Donna M. 2010. Research and evaluation in education and psychology: Integrating diversity with quantitative, qualitative, and mixed methods . 3d ed. Thousand Oaks, CA: SAGE.

This textbook is an introduction to all types of educational designs and includes four chapters devoted to quantitative research—experimental and quasi-experimental, causal comparative and correlational, survey, and single-case research. The author’s treatment of some topics is somewhat more advanced than texts such as Creswell 2012 , with extensive attention to threats to internal validity for some of the designs.

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College of Education and Human Development

Department of Educational Psychology

Quantitative methods in education

Solve problems in education through research.

Students in Quantitative Methods in Education engage in the science and practice of educational measurement and statistics, primarily through the development and application of statistical and psychometric methods. All QME students will engage in coursework addressing fundamental topics related to statistics, educational measurement, research methods, and foundations in education (e.g., learning and cognition, social development). Students will also undertake additional coursework and complete a set of milestones that will specialize their knowledge and scholarship in educational measurement or statistics. Upon matriculation, graduates will be equipped to help inform educational policy, practice, and curriculum and—most importantly—help schools and students succeed.

• Test publishing firms
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• Public school systems
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Quote from V.N. Vimal Rao, PhD '23

The strong theoretical and methodological foundation I developed in QME and EPSY supports my research and my mentoring of student researchers, while the teaching experience and knowledge of educational psychology I gained supports my teaching and mentoring of teaching assistants. V.N. Vimal Rao, PhD '23 Teaching Assistant Professor in the Department of Statistics University of Illinois Urbana Champaign

Submit your MA or PhD application for the fall semester following the deadlines below.

To be considered for fellowships and departmental financial assistance, you must submit all application materials to the program and the Graduate School by the December 1 deadline.

If you're not seeking a fellowship or departmental financial aid, you have until March 1 to submit your application materials.

MA curriculum (33 credits)

PhD curriculum (72 credits)

The QME program strives to provide funding opportunities to all incoming students. While we can’t typically guarantee funding, over the last five years, we have been able to fund over 95% of our students that were looking for funding (including our MA students)!

Visit the College of Education and Human Development's Finance and Funding page for information on tuition.

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Submit your application materials by the December 1 deadline, and you’ll automatically be considered for Graduate School fellowships and departmental awards based on scholastic achievement. Notification of awards will be sent in March.

Note: Spring, summer, and fall (March deadline) applicants will not qualify for fellowships.

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Get paid to work as a teaching assistant, graduate instructor or research assistant. Graduate assistantships are available through the department, College of Education and Human Development, and the University.

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The Department of Educational Psychology offers a minor in educational psychology with an emphasis in quantitative methods in education.

Quote from Rik Lamm, PhD '23

My background in the QME program has equipped me with the skills necessary for my current role as a Research, Evaluation, and Assessment Scientist for Bloomington Public Schools. These include developing non-cognitive surveys such as student climate surveys and parent engagement surveys, as well as analyzing data from academic assessments such as the MCAs. Additionally, QME has equipped me with the skills to interpret complex data in order to predict longitudinal trends. This ability leads to the development of research-driven strategies that benefit both students and teachers. Rik Lamm, PhD '23 Research, Evaluation, and Assessment Scientist Bloomington Public Schools

Faculty and staff

Chia-yi chiu.

Yackel Professor of Educational Measurement and Assessment

Assistant professor

Nidhi Kohli

Royal and Virginia Anderson Professor of Quantitative Methods in Education; Program Coordinator

Chelsey Legacy

Teaching assistant professor

Assistant Professor

Suzanne Loch

Senior teaching specialist

Michael Rodriguez

CEHD Dean; Campbell Leadership Chair in Education and Human Development; co-founding director of Educational Equity Resource Center

Andrew Zieffler

Teaching professor

Adjunct faculty and program affiliates

Adjunct faculty, claudio violato.

Assistant dean, Medical School

Program affiliates

Adam rothman.

Associate professor, School of Statistics  

Quote from José Palma, PhD '21

It is the combination of psychometric research and applied focus, in addition to knowledge gained from my academic journey, that makes me a competitive and atypical educational measurement researcher today. José Palma, PhD '21 ACES Faculty Fellow and Assistant Professor Texas A&M University

Corissa Rohloff, PhD student in Ed Psych, awarded Russell W. Burris Memorial Fellowship

Corissa Rohloff, PhD candidate in the quantitative methods in education program, has been awarded the Russell W. Burris Memorial Fellowship.

QME recognizes students in year end celebration

Students in the Department of Educational Psychology’s quantitative methods in education (QME) program were recognized for their contributions to the program during the 2022-23 academic year.

Kohli speaks at two international conferences

Dr. Nidhi Kohli, Royal and Virginia Anderson Professor of Quantitative Methods in Education (QME) and Program Coordinator for the QME program in the Department of Educational Psychology, was invited to present at two conferences this summer: the International Meeting of Psychometric Society and the International Indian Statistical Association.

PhD in Educational Psychology: Quantitative Methods

The Department of Educational Psychology offers the master of science and doctor of philosophy degrees in educational psychology. The programs for the M.S. and Ph.D. in educational psychology provide comprehensive knowledge of the field and intensive specialization in one of three areas of study and research: human development, learning sciences, quantitative methods. The program also offers a Ph.D. in School Psychology.

The department provides training in research. Many faculty members in the department conduct controlled research studies with human participants; schools and other agencies in the Madison area cooperate in facilitating such research projects. Principal research facilities include the School of Education's Wisconsin Center for Education Research and the multidisciplinary Waisman Center.

Areas of Specialization 

Quantitative methods.

Professors: Bolt, Kaplan, Kim, Pustejovsky, Wollack 

Educational research has a strong tradition of employing state-of-the-art statistical and psychometric (psychological measurement) techniques. Researchers in all areas of education develop measuring instruments, design and conduct experiments and surveys, and analyze data resulting from these activities. Because of this tradition, quantitative methods have long been an area of specialization within educational psychology. Graduates in this area teach, serve as consultants to educational researchers, and conduct research on statistics and psychometrics in education-related fields. Within the program, the quantitative methods area offers the two major specializations of statistics and measurement.

The study of quantitative methods takes advantage of the range of resources at the University of Wisconsin–Madison and includes coursework in statistics, mathematics, and computer sciences, and in other units of the School of Education.

School Psychology

The Department of Educational Psychology also administers a Ph.D. in School Psychology and M.S. in School Psychology.

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Students are eligible to compete for UW–Madison fellowships. A limited number of teaching and project assistantships are available within the department, and prospective students are encouraged to refer to the instructions for fellowships and assistantships contained in the program application information.

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Review the Graduate School minimum academic progress and degree requirements , in addition to the program requirements listed below.

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Mode of instruction definitions.

Accelerated: Accelerated programs are offered at a fast pace that condenses the time to completion. Students typically take enough credits aimed at completing the program in a year or two.

Evening/Weekend: ​Courses meet on the UW–Madison campus only in evenings and/or on weekends to accommodate typical business schedules.  Students have the advantages of face-to-face courses with the flexibility to keep work and other life commitments.

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Required courses, quantitative methods pathway 1.

• Educational Statistics and Research Methodology subarea

These pathways are internal to the program and represent different curricular paths a student can follow to earn this degree. Pathway names do not appear in the Graduate School admissions application, and they will not appear on the transcript.

• Educational Measurement subarea

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The  Graduate School’s Academic Policies and Procedures  provide essential information regarding general university policies. Program authority to set degree policies beyond the minimum required by the Graduate School lies with the degree program faculty. Policies set by the academic degree program can be found below.

Major-Specific Policies

Prior coursework, graduate work from other institutions.

With program approval, students are allowed to count no more than 9 credits of graduate coursework from other institutions. Coursework earned ten years or more prior to admission to a doctoral degree is not allowed to satisfy requirements. 

UW–Madison Undergraduate

No credits from a UW–Madison undergraduate degree are allowed to count toward the degree.

UW–Madison University Special

With program approval, students are allowed to count no more than 9 credits of coursework numbered 300 or above taken as a UW–Madison University Special student. Coursework earned ten or more years prior to admission to a doctoral degree is not allowed to satisfy requirements. 

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This program follows the Graduate School's Advisor policy and  Committees policy .

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This program follows the Graduate School's Time Limits policy.

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The following School of Education Student Grievance Policy and associated procedures are designed for use in response to individual student grievances regarding faculty or staff in the School of Education.

Any individual student who feels they have been treated unfairly by a School of Education faculty or staff member has the right to file a grievance about the treatment and receive a timely response addressing their concerns. Any student, undergraduate or graduate, may use these grievance procedures, except employees whose complaints are covered under other campus policies. The grievance may concern classroom treatment, mentoring or advising, program admission or continuation, course grades (study abroad grade complaints are handled through International Academic Programs ), or issues not covered by other campus policies or grievance procedures. 

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• Students are strongly encouraged to first talk with the person against whom the concern is directed.  Many issues can be settled informally at this level.  If students are unable to resolve concerns directly or without additional support, step 2 or 3 should be pursued.
• If the concern is directed against a teaching assistant (TA), and the student is not satisfied, the student should contact the TA's supervisor, who is usually the course professor.  The course professor will attempt to resolve the concern informally.
• If the concern involves a non-TA instructor, staff member, professor, academic department, or School of Education office or unit, the student should contact the chair of the department or the director of the office or unit, or their designee. The chair or director, or their designee, will attempt to resolve the concern informally. If the concern is about the department chair or office/unit director, the student should consult the School of Education Senior Associate Dean for guidance.
• If the concern remains unresolved after step 2, the student may submit a formal grievance to the chair or director in writing within 30 business days 1 of the alleged unfair treatment. To the fullest extent possible, a formal written grievance shall contain a clear and concise statement of the issue(s) involved and the relief sought.  
• On receipt of a written grievance, the chair or director will notify the person at whom the grievance is directed with a copy of the written grievance. The person at whom the complaint is directed may submit a written response, which would be shared with the student.
• On receipt of a written grievance, the chair or director will refer the matter to a department, office, or unit committee comprised of at least two members. The committee may be an existing committee or one constituted for this purpose. The committee, or delegates from the committee, may meet with the parties involved and/or review any material either party shares with the committee.  
• The committee will provide a written description of the facts of the grievance and communicate recommendations to the department chair or office/unit head regarding how the grievance should be handled.

For the purpose of this policy, business days refers to those days when the University Offices are open and shall not include weekends, university holidays, spring recess, or the period from the last day of exams of fall semester instruction to the first day of spring semester instruction. All time limits may be modified by mutual consent of the parties involved.

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• The subcommittee will then make a written recommendation to the Dean of the School of Education, or their designee, who will render a decision. The dean or designee’s written decision shall be made within 30 business days from the date when the written appeal was filed with the Senior Associate Dean.  For undergraduate students, the dean or designee’s decision is final.

Further appealing a School of Education decision – graduate students only

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For the Ph.D. program, the department offers assistantships to incoming students.

• Acquire a strong foundation in current and past theories, research findings, and methodologies in their program area. Use critical thinking skills to synthesize existing knowledge, evaluate strengths and limitations in existing theory and research, and identify issues in need of additional inquiry - including conceptual and methodological approaches available to address these issues.
• Demonstrate a knowledge of and sensitivity to human diversity in terms of individual abilities and orientations and sociocultural backgrounds.
• Retrieve, evaluate, and interpret professional and scientific literature; use this information to develop or adapt theoretical frameworks and derive testable hypotheses or predictions for their own research / program evaluation projects.
• Learn to design realistic and feasible research or assessment projects in their program area and to prepare necessary protocols that are sensitive to the backgrounds of individuals who are the focus of their work.
• Conduct independent research and analyze and interpret resulting data.
• Create clear and concise reports of their research or program evaluations that are appropriate to the intended audiences, which may include fellow scholars (via scholarly journals), practitioners (via practitioner journals or reports), and lay audiences (via online or other published reports).
• Communicate effectively in collaborative work, instructional activities, and/or consultation settings with students and professional colleagues.
• Conduct research or program implementation / evaluation in accordance with ethical standards established in their field of inquiry.

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Research and Evaluation in Education and Psychology Integrating Diversity With Quantitative, Qualitative, and Mixed Methods

• Donna M. Mertens - Gallaudet University, USA
• Description

See what’s new to this edition by selecting the Features tab on this page. Should you need additional information or have questions regarding the HEOA information provided for this title, including what is new to this edition, please email [email protected] . Please include your name, contact information, and the name of the title for which you would like more information. For information on the HEOA, please go to http://ed.gov/policy/highered/leg/hea08/index.html .

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Supplements

"Mertens's Research and Evaluation text is a well-rounded textbook that students enjoy reading because the ideas are presented in such a way that they connect to each student's area of study. If you want your students to read the chapter, this is the research methods book for your class."

"A methods text grounded in research that describes in detail the history behind different methodologies and their historical underpinnings. It has a wide range of study samples to help students make sense of the content."

"Good survey text for research methods"

"A very good and resourceful book for training scientist-practitioners in the mental health profession."

"This is a solid master's level introductory textbook that addresses the material that helping professionals need to be able to work in the field and to be able to evaluate and consume current research."

"This book provides many good examples/research studies that can be used in classroom discussions."

"The author does an excellent job utilizing various features (e.g., charts, tables, checklists, 'extending your thinking,' textbox, critical questions, research and evaluation examples) that facilitate reader learning, engagement, critical reflection on gained knowledge and various aspects of the research process, commitment to quality and ethics in research, and willingness to make connections and extend one's thinking. It is well-written and organized, concise, and straightforward."

• A new introduction covers basic terminology more completely.
• Chapters have been expanded to cover five major research paradigms—Post-positivist, Constructivist, Transformative, Pragmatic, and Indigenous—offering more research models for students to follow.
• The Indigenous Paradigm has been included in each chapter as a fifth paradigm , along with sample studies based in Indigenous communities, discussion of sampling, data collection, and use of data rooted in Indigenous community beliefs and values.
• Community engagement has been highlighted throughout the book to bring more focus to this important aspect of research.
• Where to From Here? Sections in Chapters 4-10 offer different ways to read the text that aren't linear, offering the author's suggestions on how to customize how students tackle research projects and learn about methods.
• Recent advances in mixed methods are included throughout the book, including examples of joint displays of quantitative and qualitative data for sampling and dissemination of results.
• Updated guidance from the American Psychological Association addresses research with communities of color, immigrant groups, Indigenous communities, the LGBTQ community, and people with disabilities.
• Increased focus on the issues relating to cultural competency means the Sixth Edition explicitly aligns with the accreditation requirements of the American Psychological Association and the National Council of Accreditation of Teacher.
• Increased attention to research that focuses on finding appropriate solutions with communities rather than research that only identifies problems.
• Examples have been thoroughly updated to reflect research on the effects of the COVID-19 pandemic, digital research, intersectionality, mixed methods, and more.
• Postpositivist, constructivist, transformative, pragmatic, and Indigenous paradigms discussed, showing how researchers’ views of the world underlie their choice of research approaches.
• Conducting research in culturally complex communities is emphasized throughout.
• A step-by-step overview of the entire research process.
• Detailed explanation of how to write a literature review and plan a dissertation .
• Chapters include valuable pedagogical features , such as start-of-chapter objectives, end-of-chapter summaries, electronic resources, and opportunities for “Extending Your Thinking ” through discussion questions and activities.
• An outline for the preparation of a research proposal found in the appendix can be used by students who will be preparing a research proposal.

Sample Materials & Chapters

Ch01_A Brief History of Research Paradigms, Ethical Practice, and Contested Terr

Ch02_Evaluation

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Educational Psychology — Research, Measurement & Statistics

The Educational Psychology (EPSY) PhD with a concentration in Research Methodology, Measurement, and Statistics (RMS) is designed to prepare scholars and researchers in both quantitative and qualitative methodology that prepares professional data scientists in education and psychology.

How is the Program Structured? Students are initially exposed to the same foundational courses in intermediate statistics, research methods, and Qualitative inquiry that all other doctoral students acquire in the College of Education. A copy of our degree plan is here.

Your PhD will be in “Educational Psychology” with a concentration in Research Methodology, Measurement, and Statistics.

All Educational Psychology doctoral students are exposed to foundational courses in Educational Psychology (Learning Theory, Foundations of Educational Psychology, and Human Development, Research Methods, and Intermediate Statistics) as part of the Core portion of the EPSY PhD, similar to doctoral students in the other two concentrations (Human Development and Family Science, and Gifted/Talented).

Beyond these foundational experiences, RMS students will receive more advanced training, primarily in quantitative methods, however additional coursework includes advanced data analysis in qualitative investigations. Our goal is for students to pursue careers in data analytics and data science for education and psychology. Entry to advanced training begins with a course in Multiple Regression. A two-course sequence is usually required in measurement and psychometrics, and students also receive additional training in latent variable modeling through coursework in multivariate statistics and structural equation models. Training is provided in courses such as Multi-level Modeling, Simulation and the use of advanced software (R) for statistical analysis. Coursework is followed by a comprehensive examination prior to completion of a dissertation under a faculty member of the RMS program.

Is there a deadline to apply? Applications should be complete by March 30th to begin the program in Fall. For the current year, the application deadline has been extended, as we are presently accepting applications.

How do I enter the program?

Admission to the program occurs through a standard application process, and once admitted students are assigned an initial advisor to guide them through coursework. We encourage you to reach out to the RMS faculty through email with any additional questions you may have or to let us know about your areas of interest or to set up a virtual meeting to discuss your interests (see faculty profiles below for contact information). These meetings and communication can be important, as faculty may advocate for you in the department admissions process.

After you are admitted, you will be assigned a faculty member as an advisor. It is possible to change the initial advisor as students become more familiar with their research interests and how those interests align with faculty.

What is involved in the application/admissions process?

Students are admitted to the RMS program through a departmental process. First, complete all application materials for the Toulouse Graduate School, and for the Department. After all your application materials are complete, the Department’s Doctoral Policies Committee will review your application and ask for input from RMS faculty to make their decision about your application.

How does the committee decide?

The Doctoral Policies Committee considers each applicant wholistically. Your written statement in your application should say a lot about who you are, and why you are interested in the RMS program, as well as why you feel the program content is relevant to your interests, and if possible, how you think the program would benefit you or your career interests.

There is no minimum GRE score required. We generally like to see GRE scores around the 50th percentile – particularly on the quantitative portion, but many successful students have been admitted with lower GRE scores based on aptitude, experience, and interest in the program (this can be communicated in your written statement or through recommendation letters).

Because the program is focused on research, we are looking for students that have potential and interest in doing research in education and psychology; as well as contributing to research methods – how research is conducted – methodologically.

Your recommendation letters should be from individuals that can speak to your potential as a researcher. Letters from experienced researchers/scholars are usually preferred, as these are the individuals that can speak to your suitability and potential.

The committee strongly considers input from RMS faculty, so reaching out and making contact can be very beneficial in the decision-making process. If you like, try setting up a visit to explore the department and program further.

How much math/statistics background do I need?

We take students with a sound background in Algebra. You do not need to have an abundance of statistics training to enter the program, as this can be provided in the foundational coursework. If you have a desire and interest in learning statistics, that is really all that is required. Often, students with applied research interests find that they are some of the most adept at bridging the gap between applied researchers and methodological investigations that support applied researchers to do what they do better.

That said, it is possible for students to enter the program with considerable training in statistics or mathematics and pursue a more advanced course of study. Some students will benefit from courses in probability provided by the Math department.

Is it necessary for me to have a research agenda or know what I want to do before I start the program?

Many, if not most students, enter the program with no research agenda, but an interest in learning methods that will improve understanding about learning, education, or educational programs. Some students are more interested in psychology, but most students begin to develop new interests after being exposed to the Core portion of the Educational Psychology PhD program as they interact with faculty and other students, including students in other concentrations. Some students opt to pursue interests in areas they would never have known about until their exposure to the program, for example, we have some students that have gone on to pursue successful careers as psychometricians. Prior to their exposure to the program, they didn’t have an idea about that area of work or what it entails.

What options are available for the RMS PhD program?

We have four different ways that students can participate in the program.

(1) Students may enter the program with a Master’s degree and complete the program with 66 hours (minimum) of coursework which includes a dissertation. This is the traditional route. (2) Students may enter the program with a Bachelor’s degree and complete the program with 78 hours (minimum) of coursework which includes a dissertation. This is referred to as our “passthrough” program, as students do not earn a Master’s degree and pass directly to a terminal doctorate degree. This is becoming a popular option for many students. (3) Students pursuing a PhD in other programs at UNT may receive a Minor in RMS that appears on your transcript. This option involves coursework only. (4) PhD students at UNT can opt to obtain a 2nd PhD in RMS with 36 hours beyond the first PhD.

How long does it take to finish?

For students that wish to pursue the program full time, it is possible to complete the coursework in approximately three years (depending on course sequencing and timing related to when the student begins).

For many students that have full-time jobs, it may take longer to complete the program. This is perfectly acceptable, as long as students adhere to the graduate school requirements. All work to be credited toward the doctoral degree beyond the master’s degree must be completed within a period of 8 years from the date doctoral credit is first earned.

We encourage applications from part-time, working students. All course instruction is provided in the evening hours since UNT serves a large local DFW population, and many students opt to pursue the program in addition to holding a full-time job elsewhere. Flexibility in course

scheduling is provided to accelerate students through the program or permit students to take courses at a pace that allows the program to fit with busy family and/or work lives.

What are the options for attending full-time vs. part-time?

Our doctoral program is designed to accommodate full time and part-time students. Full-time students benefit from the opportunity to work in the department as Graduate Assistants on a paid assistantship that includes additional tuition support. This makes obtaining a doctoral degree very attractive from a cost perspective. In addition, full-time students, through their assistantships, work closely with faculty in the department and have access to mentorship in teaching and scholarship. However, some students have good-paying full-time jobs in the local area or need to support a family while obtaining their degree. All coursework in the program is offered in the evening, and students can take a reduced course load over a longer period of time to obtain their degree if they choose.

For full-time students, there are competitive opportunities for Graduate Assistantships (Research or Teaching). Assistantships usually require students to work 20 hours per week and provide tuition compensation in addition to a stipend for the work performed. These positions allow students to gain additional experience working alongside faculty on research projects, providing consulting in the College’s Office of Research Consulting (ORC), on grant-related activities, and experience in teaching.

What can I do when I complete the degree?

This preparation supplies most of the necessary skills for pursuing an academic career, although approximately half of our graduates opt to pursue careers in corporations conducting research or in development (such as in test development) or in school districts directing assessment, evaluation, or accountability functions.

Applying to a graduate program at UNT is a two-step process.  

• First, you will need to apply online through the Toulouse Graduate School (TGS) at  www.goapplytexas.org . Submit application fee and official transcripts from all schools attended to Toulouse Graduate School as outlined in the directions at  https://tgs.unt.edu/future-students/graduate-admissions-traditional .   
• Second, you will need to complete a departmental application. The departmental application for doctoral students includes three recommendation letters (signed/on letterhead), a resume/CV, and a personal statement. You can view the details of these requirements at  http://www.coe.unt.edu/educational-psychology/graduate-admissions . Our department requires all items and scores within two months of the submitted application. 

1. 36 hour minimum beyond the 1st PhD at UNT. Plus 9 hour EPSY Core courses if needed (5550, 6040, 5123). 2. If students have had Core equivalents (see below), these hours do not need to be made up. 3. If students have had RMS concentration area equivalents, these hours do need to be made up to get to the 36 hour minimum. It is up to the advisor and student to determine whether the courses should be retaken in our program (e.g., to deepen understanding or to learn from a different perspective in an area) or whether other RMS-related courses should be substituted (e.g., to expand content and experience). 4. No courses that are included on another degree plan can be applied to the 2nd PhD degree plan. Courses that are not on another degree plan can be transferred in and counted for equivalents as determined by the advisor. No more than 9 hours of RMS-related courses that are not included on another degree plan can be transferred in. 5. Students must take and pass the written and oral portions of the qualifying exam like all RMS students. This exam can be taken within the last 9 hours in the final semester of coursework. 6. Students are expected to engage in research with faculty and other graduate students and contribute to the academic culture of the program and department.

Degree Plan

Leveling and Core Courses, if Needed: Required leveling (9 hours), only if not previously taken. These do not count toward the 36 hour minimum and do not need to be taken if students have had equivalents, per advisor approval. It is generally assumed that students will have this content in the first PhD. 5210 – educational statistics (introductory; prerequisite to 6010) 6010 – statistics for educational research (intermediate) 6020 – research methods in education

Core courses (9 hours). These do not need to be taken if students have had equivalents, per advisor approval. 5550 – learning theories in education 6040 – foundations of educational psychology 5123 – human development across the lifespan

Required Courses: Concentration area (27 hours) 6005 – statistical theory and simulation 6210 – multiple regression and related methods 6220 – classical and modern measurement theory 6230 – theory and application of hierarchical linear modeling 6240 – technology in research 6250 – item response theory 6270 – structural equation modeling 6280 – qualitative research in education 6290 – multivariate statistics

Dissertation (9 hours) 6950 – dissertation

The doctoral program in educational psychology requires a minimum of 63 (78 hours if admitted without a Master’s degree) hours of course work and research experience.

Those entering the program without a master’s degree are expected to fulfill the following requirements, plus two core courses in the MS with a major in educational psychology, and a minimum of three additional graduate-level courses with the advisor’s approval.

Note:  The following requirements are for students entering the program having completed a related master’s degree.

78 hour doctoral degree plan 63 hour doctoral degree plan

Educational psychology requirement, 9 hours

• EPSY 5550 - Learning Theories
• EPSY 6040 - Foundations of Educational Psychology
• EPSY 5123 - Human Development Across the Life Span

Major requirements, 21 hours

• EPSY 6010 - Statistics for Educational Research
• EPSY 6020 - Research Methods in Education
• EPSY 6210 - Multiple Regression Analysis and Related Methods
• EPSY 6220 - Advanced Testing and Measurement
• EPSY 6230 - Theory and Application of Hierarchical Linear Modeling
• EPSY 6280 - Qualitative Research in Education
• EPSY 6290 - Multivariate Statistics in Education

Concentration, 21 hours

Research, measurement and statistics.

• EPSY 6005 - Statistical Theory and Simulations
• EPSY 6240 - Technology in Research
• EPSY 6250 - Item Response Theory
• EPSY 6270 - Structural Equation Modeling
• plus 3 elective courses in research, measurement and statistics from inside or outside of the department, with advisor’s approval

Supervised research practice, 3 hours

• EPSY 6030 - Practicum, Field Problem or Internship

Dissertation, 9 hours minimum

• EPSY 6950 - Doctoral Dissertation  (3, 6 or 9 hours per semester)

Minor in Research Methodology, Measurement, and Statistics (RMS)

Prerequisites Beginning the minor assumes students have taken the equivalents of EPSY 6010 and 6020 in their own program or elsewhere.

12 hour minor

Required: EPSY 6210 – Multiple Regression and Related Methods EPSY 6290 – Multivariate Statistics

And select 6 hours from the following: EPSY 6005 – Statistical Theory and Simulation EPSY 6220 – Classical and Modern Measurement Theory EPSY 6230 – Theory and Application of Hierarchical Linear Modeling EPSY 6250 – Item Response Theory EPSY 6270 – Structural Equation Modeling

All courses are 3 hours.

Substitutions All courses are to be taken through the Department of Educational Psychology.

Transcript The minor will officially appear on students’ transcripts if the minor is designated on their degree plans and posting of the minor is requested of the Registrar’s office.

Associate Professor

Dr. Chen engages in doing research and teaching in the area of quantitative methods. Her primary quantitative research interests include Growth Mixture Modeling (GMM), Hierarchical Linear Modeling (HLM), and Structural Equation Modeling (SEM), and the application of these methods in longitudinal data analyses and mediation analysis. She is also interested in the application of these methods in educational and family-based data. Her interested substantive areas include children’s psychosocial functioning and self-regulation, school-based prevention, teacher-student relationship and peer relations, and the intersection of family and cultural contexts in shaping Asian American adolescent development.

Robin K. Henson

Robin’s background is a bit of winding road, but one he considers divinely guided by God. His early background was in youth ministry with undergraduate majors in Biblical studies and psychology from Evangel University (Springfield, MO). This evolved into a focus on counseling and masters degrees in general and clinical psychology, and eventually LPC licensure. Robin then earned a PhD in educational psychology at Texas A&M University, and this led to his current research and teaching focus on statistics, measurement, and research methodology. The LPC licensure was latter allowed to lapse due to a focus on methodology, but Robin still periodically works with counseling applications. More recently, Robin has completed a doctor of ministry (DMin) degree from Liberty University with a cognate in expository preaching and teaching. Robin is married to an incredible woman and has two awesome, young adult kids. In his spare time, Robin enjoys outdoor activities and being involved at Midway Church.

Darrell M. Hull

Professor Hull came to UNT from Baylor University in 2007 and presently coordinates the Research Methodology, Measurement, and Statistics PhD program in the Department of Educational Psychology where he teaches courses in advanced measurement and psychometrics including Classical Test Theory, Generalizability Theory, and Item Response Theory. He also has methodological research interests in experimental and quasi-experimental design and analysis of field trial studies in education, and has more than 25 years of experience conducting program evaluations of educational programs and interventions. His substantive interests include Positive Youth Development and STEM education based on more than 30 years of work in STEM education, and with youth and adolescents in developing countries in the Caribbean region. He was named a Fulbright Scholar in 2019 for this PYD and methods work in evaluation and held a visiting appointment at the University of the West Indies in Jamaica.

Jihyun Lee is an assistant professor in the Research, Measurement and Statistics program in the Department of Educational Psychology at the University of North Texas. She earned her Ph.D. in 2022 in the Quantitative Methods program in the Educational Psychology department at the University of Texas at Austin.

Her research has been anchored in the overarching question, "How can we conduct valid quantitative research to investigate psychological and behavioral phenomena?" To address this question, she studies quantitative methods and statistical modeling used for research within the behavioral and social sciences, including education and psychology. Her specialized work in quantitative methods includes meta-analysis, missing data analysis, and latent variable modeling. 

In addition, she has actively collaborated with applied researchers in various research fields. Her work is intended to further enhance the quantitative research that investigates educational, psychological, clinical, and social issues.  

Nicole Sankofa

Nicole Sankofa is an Assistant Professor of Educational Psychology at the University of North Texas. She graduated from Spelman College with a double major in Psychology and Women's Studies and earned a PhD in Educational Psychology from The Ohio State University. She uses a transformativist paradigm and qualitative methodologies to examine the role of self-determination on adolescent/adult development, psychological well-being, and academic outcomes across school, work, and juvenile detention settings. Dr. Sankofa is the course steward for the sequence of qualitative doctoral courses in the Department of Educational Psychology.

James O. Uanhoro

James Uanhoro is an assistant professor in the Research, Measurement and Statistics program in the Department of Educational Psychology at the University of North Texas. He received his Ph.D. in 2021 in the Quantitative, Research, Evaluation and Measurement program in the Department of Educational Studies at Ohio State University.

Uanhoro's work focuses on the similarities between multilevel regression models and commonplace measurement models — structural equation models, classical test theory and item response models. Specifically, he attempts to leverage insights from both groups of models to better understand patterns in complex data analysis contexts. Underlying much of this work is Bayesian data analysis, which he also applies in research collaborations with social scientists. Finally, James has an interest in building easy-to-use statistical tools that allow researchers better understand patterns in their data, and better communicate insights from their studies.

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How Scientific Is Educational Psychology Research? The Increasing Trend of Squeezing Causality and Recommendations from Non-intervention Studies

• REFLECTION ON THE FIELD
• Published: 15 March 2023
• Volume 35 , article number  37 , ( 2023 )

Cite this article

• Anna C. Brady   ORCID: orcid.org/0000-0003-1361-2412 1 ,
• Marlynn M. Griffin 1 ,
• Ariah R. Lewis 2 ,
• Carlton J. Fong 3 &
• Daniel H. Robinson 4  

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The field of educational psychology has been moving away from intervention and experimental methods and toward observational and correlational methods (Hsieh et al., Journal of Educational Psychology, 97(4), 523–529, 2005 ; Reinhart et al., Journal of Educational Psychology, 105(1), 241–247, 2013 ; Robinson et al., American Educational Research Journal, 44(2), 400–413, 2007 ). Additionally, there has been an increase in the percentage of observational and correlational articles that include recommendations for practice. The present study updated previous data to 2020 by examining methodologies and recommendations for practice in articles published in five empirical educational psychology journals ( Journal of Educational Psychology , American Educational Research Journal , Cognition and Instruction , Journal of Experimental Education , and Contemporary Educational Psychology ). The percentage of articles employing experimental methods has continued to decrease (20%), whereas qualitative methods have increased (22%). Across correlational, qualitative, and mixed method articles, two-thirds included recommendations for practice—up from 46% in 2010. Implications are discussed.

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Should researchers make causal inferences and recommendations for practice on the basis of nonexperimental studies?

Michael P. Grosz

Final Remarks

The Design(s) of Educational Research: Description and Interpretation

Alexander, P. A. (2013). In praise of (reasoned and reasonable) speculation: A response to Robinson et al.’s moratorium on recommendations for practice. Educational Psychology Review, 25 (3), 303–308. https://doi.org/10.1007/s10648-013-9234-2

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Brady, A.C., Griffin, M.M., Lewis, A.R. et al. How Scientific Is Educational Psychology Research? The Increasing Trend of Squeezing Causality and Recommendations from Non-intervention Studies. Educ Psychol Rev 35 , 37 (2023). https://doi.org/10.1007/s10648-023-09759-9

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Qualitative vs Quantitative Research Methods & Data Analysis

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What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis.

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded.

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

Using Science to Inform Educational Practices

Quantitative and Qualitative Approaches to Research

When designing a study, typically, researchers choose a quantitative or qualitative research design. In some cases, a mixed-method approach may be appropriate. Which approach used will develop on the research question and the type of information sought. Quantitative methods may be better for understanding what is happening, while qualitative methods may be better for understanding the hows and why of a phenomenon.

Video 1.  Types of Research explains the difference between qualitative and quantitative research. A closed-captioned version of this video is available here .

Quantitative Research

Quantitative research typically starts with a focused research question or hypothesis, collects a small amount of data from each of a large number of individuals, describes the resulting data using statistical techniques, and draws general conclusions about some large population. The strength of quantitative research is its ability to provide precise answers to specific research questions and to draw general conclusions about human behavior; however, it is not nearly as good at  generating   novel and interesting research questions. Likewise, while quantitative research is good at drawing general conclusions about human behavior, it is not nearly as good at providing detailed descriptions of the behavior of particular groups in particular situations. And it is not very good at all at communicating what it is actually like to be a member of a particular group in a particular situation. But the relative weaknesses of quantitative research are the relative strengths of qualitative research.

Qualitative Research

Although this is by far the most common approach to conducting empirical research in psychology, there is a vital alternative called qualitative research . Qualitative research can help researchers to generate new and interesting research questions and hypotheses. Qualitative researchers generally begin with a less focused research question, collect large amounts of relatively “unfiltered” data from a relatively small number of individuals, and describe their data using nonstatistical techniques. They are usually less concerned with drawing general conclusions about human behavior than with understanding in detail the  experience   of their research participants. Qualitative research can also provide rich and detailed descriptions of human behavior in the real-world contexts in which it occurs. Similarly, qualitative research can convey a sense of what it is actually like to be a member of a particular group or in a particular situation—what qualitative researchers often refer to as the ‘lived experience’ of the research participants.

Mixed-Methods

Given their differences, it may come as no surprise that quantitative and qualitative research do not coexist in complete harmony. Some quantitative researchers criticize that qualitative methods lack objectivity, are challenging to evaluate, and do not allow generalization to other people or situations. At the same time, some qualitative researchers criticize that quantitative methods overlook the richness of behavior and experience, and instead answer simple questions about easily quantifiable variables. However, many researchers from both camps now agree that the two approaches can and should be combined into what has come to be called mixed-methods research (Todd, Nerlich, McKeown, & Clarke, 2004). One approach to combining quantitative and qualitative research is to use qualitative research for hypothesis generation and quantitative research for hypothesis testing. A second approach to combining quantitative and qualitative research is referred to as triangulation. The idea is to use both quantitative and qualitative methods simultaneously to study the same general questions and to compare the results. If the results of the quantitative and qualitative methods converge on the same general conclusion, they reinforce and enrich each other. If the results diverge, then they suggest an interesting new question: Why do the results diverge, and how can they be reconciled?

Video 2.3.1.  What are Qualitative and Quantitative Variables explains the difference between quantitative and qualitative variables that may be used in research.

Becoming Familiar with Research

An excellent way to become more familiar with these research approaches, both quantitative and qualitative, is to look at journal articles, which are written in sections that follow these steps in the scientific process. Most psychological articles and many papers in the social sciences follow the writing guidelines and format dictated by the American Psychological Association (APA). In general, the structure follows: abstract (summary of the article), introduction or literature review, methods explaining how the study was conducted, results of the study, discussion and interpretation of findings, and references.

The Aftermath of Teenage Suicide: A Qualitative Study of the Psychosocial Consequences for the Supervising Family

Per Lindqvist and his colleagues (2008), wanted to learn how the families of teenage suicide victims cope with their loss. They did not have a specific research question or hypothesis, such as, what percentage of family members join suicide support groups? Instead, they wanted to understand the variety of reactions that families had, with a focus on what it is like from  their  perspectives. To do this, they interviewed the families of 10 teenage suicide victims in their homes in rural Sweden. The interviews were relatively unstructured, beginning with a general request for the families to talk about the victim and ending with an invitation to talk about anything else that they wanted to tell the interviewer. One of the most important themes that emerged from these interviews was that even as life returned to “normal,” the families continued to struggle with the question of why their loved one committed suicide. This struggle appeared to be especially difficult for families in which the suicide was most unexpected. This relationship can now be explored using quantitative research. But it is unclear whether this question would have arisen at all without the researchers sitting down with the families and listening to what they themselves wanted to say about their experience.

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