Introduction to Psychometric Theory
Published September 22nd 2010 by Routledge – 347 pages
This new text provides a state-of the-art introduction to educational and psychological testing and measurement theory that reflects many intellectual developments of the past two decades. The book introduces psychometric theory using a latent variable modeling (LVM) framework and emphasizes interval estimation throughout, so as to better prepare readers for studying more advanced topics later in their careers. Featuring numerous examples, it presents an applied approach to conducting testing and measurement in the behavioral, social, and educational sciences. Readers will find numerous tips on how to use test theory in today’s actual testing situations.
To reflect the growing use of statistical software in psychometrics, the authors introduce the use of Mplus after the first few chapters. IBM SPSS, SAS, and R are also featured in several chapters. Software codes and associated outputs are reviewed throughout to enhance comprehension. Essentially all of the data used in the book are available on the website. In addition instructors will find helpful PowerPoint lecture slides and questions and problems for each chapter.
The authors rely on LVM when discussing fundamental concepts such as exploratory and confirmatory factor analysis, test theory, generalizability theory, reliability and validity, interval estimation, nonlinear factor analysis, generalized linear modeling, and item response theory. The varied applications make this book a valuable tool for those in the behavioral, social, educational, and biomedical disciplines, as well as in business, economics, and marketing. A brief introduction to R is also provided.
Intended as a text for advanced undergraduate and/or graduate courses in psychometrics, testing and measurement, measurement theory, psychological testing, and/or educational and/or psychological measurement taught in departments of psychology, education, human development, epidemiology, business, and marketing, it will also appeal to researchers in these disciplines. Prerequisites include an introduction to statistics with exposure to regression analysis and ANOVA. Familiarity with SPSS, SAS, STATA, or R is also beneficial. As a whole, the book provides an invaluable introduction to measurement and test theory to those with limited or no familiarity with the mathematical and statistical procedures involved in measurement and testing.
"[Introduction to Psychometric Theory] does a good job of ‘mentoring’ you from study design all the way to analysing and interpreting your data. … The book easily passes the ‘Did I wish I had used this book during my PhD?’ test, and some of the more advanced chapters have dropped several pennies for me." – Chris Beeley in The Psychologist
"Introduction focuses on measurement of unobserved constructs and builds on latent variable modeling to produce a refreshingly new and integrative presentation of psychometric theory. Indeed, the latent variable approach serves well to integrate classical test theory, generalizability theory, and item response theory. It is a must read (if textbooks can be so described!) for scholars as well as students of psychometric theory and practice." - Richard J. Shavelson, Stanford University, USA
"The market is … begging for this book…The existing texts are either too dated [or] too inaccessible … The authors …. capture critical intellectual developments of the last decade … they effectively exploit computational advances to put all of this in an applied context, thereby grounding the material in real world examples. The quality of the scholarship … is simply first-rate.… The coverage is right on…The writing is superb and accessible.…. The ability to work through each example using the authors’ datasets is invaluable …I would … adopt this text and use it to reinvigorate my own course. It promises to provide a great opportunity to refresh the way we teach this material." – Scott L. Thomas, Claremont Graduate University, USA
"This text will make a unique and important contribution to the field. It is extremely well-written. … An excellent text for a … course in psychometric theory. … The references are current, reflecting the most recent work in classical test theory. The software applications … are a unique and powerful asset." - Jennifer Rose, Wesleyan University, USA
"I … congratulate the authors on tackling the complex area of psychometric theory … so well and putting so much effort into making … their book … accessible to a wide audience. … I … would certainly adopt this book … [for] a graduate-level course … in educational and psychological measurement. … One of the strengths … is the provision of code in the text as well as … data files and codes on the website. … What makes the book most unique is the unified treatment of multiple latent-variable methods." - André A. Rupp, University of Maryland, USA
„ It was very easy to read … anyone would be able to read this and understand [it]… the examples were very useful. … Most [competing] books have not been revised or can be challenging… the strength of this book is its breadth." – Robert Henson, The University of North Carolina at Greensboro, USA
1. Measurement, Measuring Instruments, and Psychometric Theory. 2. Basic Statistical Concepts and Relationships. 3. An Introduction to Factor Analysis. 4. Introduction to Latent Variable Modeling and Confirmatory Factor Analysis. 5. Classical Test Theory. 6. Reliability. 7. Procedures for Estimating Reliability. 8. Validity. 9. Generalizability Theory. 10. Introduction to Item Response Theory. 11. Fundamentals and Models of Item Response Theory. Chapter Notes. Appendix. A Brief Introduction to Some Graphics Applications of R in Item Response Modeling.
Tenko Raykov is Professor of Measurement and Quantitative Methods at Michigan State University. He received his Ph.D. in Mathematical Psychology from Humboldt University in Berlin. He teaches courses in psychometric theory, multivariate statistics, latent variable and structural equation modeling, and multilevel modeling at Michigan State University. He serves on the editorial board of Psychological Methods, Structural Equation Modeling, the British Journal of Mathematical and Statistical Psychology, and Multivariate Behavioral Research.
George A. Marcoulides is Professor of Statistics at the University of California – Riverside. He is the Series Editor of the Quantitative Methodology Series, Editor of the Structural Equation Modeling journal, and on the editorial board of several other measurement and statistics journals.