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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.
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.
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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.
Inhaltsangabe
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
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
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