This book helps readers apply testing and measurement theories and features 22 self-contained modules which instructors can match to their courses. Each module features an overview of a measurement issue and a step-by-step application of that theory.

Kenneth S. Shultz is Professor of Psychology at California State University, San Bernardino, USA. He teaches classes in I-O psychology, research methods, psychological testing, and statistics. He has more than 55 peer-reviewed articles, four books, and 15 book chapters. He was the recipient of the 2014-2015 John M. Pfau Outstanding Professor Award at CSUSB.

David J. Whitney is Professor of Psychology at California State University, Long Beach, USA. He teaches classes in I-O psychology, psychological testing, introductory statistics, and Autism Spectrum Disorder. With 25 peer-reviewed publications, he is among the most cited researchers from his university.
Michael J. Zickar is Sandman Professor of Industrial-Organizational Psychology at Bowling Green State University, Ohio, USA. He teaches classes in I-O psychology, psychometrics, and the history of psychology. He has published more than 60 peer-reviewed articles. He is also a Fellow of the Society for Industrial-Organizational Psychology.

About the Authors

Preface

Introduction

Module 1. Introduction and Overview

Module 2. Statistics Review for Psychological Measurement

Module 3. Psychological Scaling

Module 4. Test Preparation and Specification

Reliability, Validity, and Test bias

Module 5. Reliability Overview: Classical Test Theory

Module 6. Estimating Reliability

Module 7. Content Validation

Module 8. Criterion-Related Validation

Module 9. Construct Validation

Module 10. Validity Generalization and Psychometric Meta-Analysis

Module 11. Test Bias, Unfairness, and Equivalence

Practical Issues in Test Construction

Module 12. Developing Tests of Maximal Performance

Module 13. Classical Test Theory Item Analysis

Module 14. Scoring Tests

Module 15. Developing Measures of Typical Performance

Module 16. Response Biases

Advanced Topics

Module 17. Combining Predictors Using Multiple Regression

Module 18. Exploratory Factor Analysis

Module 19. Confirmatory Factor Analysis

Module 20. Item Response Theory

Module 21. Applications of Item Response Theory: Computer Adaptive Testing and Differential Item Functioning

Module 22. Generalizability Theory

Appendix A. Course-Long Exercise on Psychological Scale Development

Appendix B. Data Set Descriptions

Glossary of Key Terms

References

Author Index

Subject Index