This edited volume features cutting-edge topics from the leading researchers in the areas of latent variable modeling. Content highlights include coverage of approaches dealing with missing values, semi-parametric estimation, robust analysis, hierarchical data, factor scores, multi-group analysis, and model testing. New methodological topics are illustrated with real applications. The material presented brings together two traditions: psychometrics and structural equation modeling. Latent Variable and Latent Structure Models' thought-provoking chapters from the leading researchers in the area will help to stimulate ideas for further research for many years to come.
This volume will be of interest to researchers and practitioners from a wide variety of disciplines, including biology, business, economics, education, medicine, psychology, sociology, and other social and behavioral sciences. A working knowledge of basic multivariate statistics and measurement theory is assumed.
Table of Contents
Contents: Preface. D.J. Bartholomew, Old and New Approaches to Latent Variable Modelling. I. Moustaki, C. O'Muircheartaigh, Locating "Don't Know," "No Answer" and Middle Alternatives on an Attitude Scale: A Latent Variable Approach. L.A. van der Ark, B.T. Hemker, K. Sijtsma, Hierarchically Related Nonparametric IRT Models, and Practical Data Analysis Methods. P. Tzamourani, M. Knott, Fully Semiparametric Estimation of the Two-Parameter Latent Trait Model for Binary Data. P. Rivera, A. Satorra, Analyzing Group Differences: A Comparison of SEM Approaches. R.D. Wiggins, A. Sacker, Strategies for Handling Missing Data in SEM: A User's Perspective. T. Raykov, S. Penev, Exploring Structural Equation Model Misspecifications Via Latent Individual Residuals. J-Q. Shi, S-Y. Lee, B-C. Wei, On Confidence Regions of SEM Models. P. Filzmoser, Robust Factor Analysis: Methods and Applications. M. Croon, Using Predicted Latent Scores in General Latent Structure Models. H. Goldstein, W. Browne, Multilevel Factor Analysis Modelling Using Markov Chain Monte Carlo Estimation. J-P. Fox, C.A.W. Glas, Modelling Measurement Error in Structural Multilevel Models.
"If you have a working knowledge of latent variables, and by now let us hope that most psychologists have, then there is much that one is likely to learn from this book."
—British Journal of Mathematical and Statistical Psychology
"...this volume stimulates the reader not only by presenting methods and data analysis that lead to these results, but also by indicating which questions may be worth asking....Thus, I conclude that the volume presented by Marcoulides and Moustaki can have great appeal (a) to experienced users of latent variable models, latent structure models, and item response theory, because solutions are proposed to important questions, and (b) to researchers who work to further develop methods in these areas because they are provided with an overview of the state of the art. This overview is selective. It reflects the editors' perceptions of what is of interest-one more reason to consider this volume."
—Contemporary Psychology APA REVIEW OF BOOKS
"As for quality of scholarship, it is easily at the highest level....The book will appeal primarily to academicians and researchers--anyone trying to stay current with state-of-the-art matters in structural modeling."
—Dr. Keith Widaman
University of California at Davis