NEW: updated eResources, 'Case Studies for Teaching on Race, Racism and Black Lives Matter.' Please see Support Material tab to download the new resources.
This book presents an integrated approach to learning about research design alongside statistical analysis concepts. Strunk and Mwavita maintain a focus on applied educational research throughout the text, with practical tips and advice on how to do high-quality quantitative research.
Design and Analysis in Educational Research teaches research design (including epistemology, research ethics, forming research questions, quantitative design, sampling methodologies, and design assumptions) and introductory statistical concepts (including descriptive statistics, probability theory, sampling distributions), basic statistical tests (like z and t), and ANOVA designs, including more advanced designs like the factorial ANOVA and mixed ANOVA, using SPSS for analysis. Designed specifically for an introductory graduate course in research design and statistical analysis, the book takes students through principles by presenting case studies, describing the research design principles at play in each study, and then asking students to walk through the process of analyzing data that reproduce the published results. An online eResource is also available with data sets.
This textbook is tailor-made for first-level doctoral courses in research design and analysis, and will also be of interest to graduate students in education and educational research.
Table of Contents
- Basic Issues in Quantitative Educational Research
- Sampling & Basic Issues in Research Design
- Basic Educational Statistics
- Introducing the Null-Hypothesis Significance Test
- Comparing a Single Sample to the Population using the one-sample z-test and one-sample t-test
- Comparing two samples means: The independent samples t-test
- Independent samples t-test Case studies
- Comparing more than two means: The one-way ANOVA
- One-way ANOVA Case Studies
- Comparing means across two independent variables: The factorial ANOVA
- Factorial ANOVA Case Studies
- Comparing two points from the same sample (within-subjects comparison) using the paired samples t-test
- Paired samples t-test Case studies
- Comparing more than two points from within the same sample: The within-subjects ANOVA
- Within-subjects ANOVA case studies
- Mixed between- and within-subjects designs using the mixed ANOVA
- Mixed ANOVA Case Studies
- Quantitative Methods for Social Justice and Equity: Theoretical and practical considerations
- A1 - z Table
- A2 - t Table
- A3 - F Table
- A4 - HSD Table
- B1 – Statistical Notation and Formulas
NULL HYPOTHESIS SIGNIFICANCE TESTING
CONSIDERING EQUITY IN QUANTITATIVE RESEARCH
Kamden K. Strunk is an Associate Professor of Educational Research at Auburn University, where he primarily teaches quantitative methods. His research focuses on intersections of racial, sexual, and gender identities, especially in higher education. He is also a faculty affiliate of the Critical Studies Working Group at Auburn University.
Mwarumba Mwavita is an Associate Professor of Research, Evaluation, Measurement, and Statistics at Oklahoma State University, where he teaches quantitative methods. He is also the Founding Director of the Center for Educational Research and Evaluation (CERE) at Oklahoma State University.
At the request of the authors, I reviewed samples from the text Design and Analysis in Educational Research: ANOVA Designs in SPSS. The samples I reviewed for this text were well done and easy to read. It is clear the authors have worked to write in a way that learners of all levels can understand and benefit from the content. Notations are commonly recognized, clear, and easy to follow. Figures and tables are appropriate and useful. I also found the SPSS tutorials to be helpful and easy to follow. I especially appreciate that the authors took the time not only to address important topics and steps for conducting NHST and various ANOVA designs, but also to address social justice and equity issues in quantitative research as well as epistemologies and how they connect to research methods. These are important considerations, and ones that are not included in many design/analysis textbooks.
Overall, I felt this book captured the important elements of basic educational statistics and ANOVA designs, the formulas for how they are calculated, as well as how to run the analysis in a commonly and widely used statistical software (SPSS) in a way that is approachable and clear. This text seems to capture the elements often found in multiple, separate sources (e.g., epistemology, research design, analysis, use of statistical software, and considerations for social justice/equity) and combines them in one text.
This is so helpful, useful, and needed!
Sara R. Gordon, Ph.D.
Center for Leadership and Learning
Arkansas Tech University
The ability to analyze data has never been more important given the volume of information available today. A challenge is ensuring that individuals understand the connectedness between research design and statistical analysis. Strunk and Mwavita introduce fundamental elements of the research process and illustrate statistical analyses in the context of research design. This provides readers with tangible examples of how these elements are related and can affect the interpretation of results.
Many statistical analysis and research design textbooks provide depth, but may not situate scenarios in an applied context. Strunk and Mwavita provide illustrative examples that are realistic and accessible to those seeking a strong foundation in good research practices.
Again, thank you for this opportunity. I genuinely believe this is a textbook I would feel comfortable using with my own students and look forward to seeing it in print.
Forrest C. Lane, Ph.D.
Associate Professor and Chair
Department of Educational Leadership
Strunk and Mwavita provide a sound introductory text that is easily accessible to readers learning applied analysis for the first time.
The chapters flow easily through traditional topics of null hypothesis testing and p-values. The chapters include hand calculations that assist students in understanding where the variance is and case studies at the end to develop writing skills related to each analysis. In addition, SPSS is integrated toward the end of the chapters after readers have seen and learned to interpret the techniques by hand. Finally, the length of the book is more manageable for readers as a first introduction to educational statistics.
James Schreiber, Ph.D.
Professor, School of Nursing