This book covers the computational aspects of psychometric methods involved in developing measurement instruments and analyzing measurement data in social sciences. It covers the main topics of psychometrics such as validity, reliability, item analysis, item response theory models, and computerized adaptive testing. The computational aspects comprise the statistical theory and models, comparison of estimation methods and algorithms, as well as an implementation with practical data examples in R and also in an interactive ShinyItemAnalysis application.
- Statistical models and estimation methods involved in psychometric research
- Includes reproducible R code and examples with real datasets
- Interactive implementation in ShinyItemAnalysis application
The book is targeted toward a wide range of researchers in the field of educational, psychological, and health-related measurements. It is also intended for those developing measurement instruments and for those collecting and analyzing data from behavioral measurements, who are searching for a deeper understanding of underlying models and further development of their analytical skills.
1. Introduction 2. Validity 3. Internal structure of test and factor analysis 4. Reliability 5. Traditional item analysis 6. Item analysis with regression models 7. Item response theory models 8. More complex IRT models 9. Differential item functioning 10. Outlook on applications and more advanced psychometric topics Appendix A. Introduction to R Appendix B. Descriptive statistics Appendix C. Distributions of random variables Appendix D. Measurement data in ShinyItemAnalysis Appendix E. Exercises
"This book is an excellent combination of introductory and recent advanced psychometric developments with implemented and illustrated examples using R. The book is clearly written and covers man different topics but is especially interesting as R code are given so the reader can learn how to perform the described analyses. Several datasets are given, and the examples are well explained to help readers to learn the methods. The methods in the book include, but are not limited to classical test theory, item response theory (IRT), item analysis, and differential item functioning. I highly recommend this book, both as course book when teaching psychometrics but also for researchers who wants to perform advanced analyses."
- Marie Wiberg, Professor, Umeå University
"This book is both a comprehensive introduction to psychometrics and a practical guide to implement the methods using R. It covers a wide range of topics, always providing real-case applications and the R code needed to analyse the data. Remarkable strengths of the book are the large variety of datasets used as examples and the fine balance between theory and applications. The book is suitable as a textbook for Psychometrics courses, and it can be of interest to researchers as well."
- Michela Battauz, Associate Professor, University of Udine.
"I have been instructionally using the R package ShinyItemAnalysis, written by the lead author and her team, for several years and am pleased to see this book appear. It is written in a manner that is clear and inviting to the reader and the mathematics necessary to understand the text is mostly within the reach of someone who has taken introductory statistics, regression, and categorical data. Similarly, the necessary R is also kept fairly basic. In addition to item response theory (IRT), the text contains content on topics such as regression-based item analysis, item bias, classical test theory, and computerized adaptive testing. As such, it would be an excellent addition to an IRT course using R. An instructor using a different text but needing examples and exercises in R would also benefit, as would a motivated scholar undertaking self-study. In sum, I highly recommend this book."
- Jay Verkuilen, Associate Professor, The City University of New York.
“This book stands as an exceptional resource for researchers and students with a keen interest in the fields of Psychometrics and measurement, with a thorough, accessible and interactive approach to allow the reader to understand these complex concepts and the tools associated with them. The book tackles a challenging list of topics and provides guidance and explanation on how to grasp all the steps of the analysis using graphical and computational tools. The teaching of the methods is accomplished with the use of well prepared and documented R code and information on packages as well as the ShinyItemAnalysis application. The book is suitable to researchers and students at all levels including researchers new to the fields of measurement and computational psychometrics but also advanced users who are interested in implementing and learning more advanced tools. A well written book with an extensive material for learning and practice.”
- Irini Moustaki, Professor in Social Statistics, London School of Economics.
"Impressively, Martinková and Hladká appeals to a broad audience of researchers, ranging from beginners to experts from a variety of disciplines. They introduce key concepts in psychometrics through both written descriptions and sample code, with interactive companion material in the application ShinyItemAnalysis and on github. Examples draw from a variety of data sets that cover entrance exams, learning assessments, the process of peer review, and peoples’ personalities, among others. I appreciate their helpful approach of starting with the basics—here, analyses such as t-tests, ANOVAs, and classical testing theory—and then move on to more complex ideas like Item Response Theory and Differential Item Functioning analysis. Martinková and Hladká explain the basics for historical reasons, as well as because they can be sufficient; when they cover more complex analyses, they also advise researchers about when those techniques might be necessary. Within each chapter, the authors discuss the underlying analytical problems and concepts, then dive into the mathematics of those concepts, and conclude with practical application of the concepts. In addition to the examples mentioned in the text, the authors include a final section with exercises for each chapter. I recommend Martinková and Hladká for anyone teaching or learning psychometrics."
- Rebecca M. Price, Professor, School of Interdisciplinary Arts and Sciences, University of Washington Bothell