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E-raamat: Latent Variable and Latent Structure Models

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Based on material presented at the 22nd conference of the Society for Multivariate Analysis in the Behavioral Sciences (July 2000), these 12 papers describe original research and developments concerned with latent variable modeling and structural equation modeling. Each chapter assumes that the reader has already mastered basic multivariate statistics and measurement theory. Topics covered include hierarchically related nonparametric IRT models and practical data analysis methods, fully semiparametric estimation of the two-parameter latent trait model for binary data, exploring structural equation model misspecifications via latent individual residuals, and using predicted latent scores in general latent structure models. Annotation c. Book News, Inc., Portland, OR (booknews.com)

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.

Arvustused

"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

Preface iii
About the Authors v
Old and New Approaches to Latent Variable Modelling
1(14)
David J. Bartholomew
Locating `Don't Know', `No Answer' and Middle Alternatives on an Attitude Scale: A Latent Variable Approach
15(26)
Irini Moustaki
Colm O'Muircheartaigh
Hierarchically Related Nonparametric IRT Models, and Practical Data Analysis Methods
41(22)
L. Andries van der Ark
Bas T. Hemker
Klaas Sijtsma
Fully Semiparametric Estimation of the Two-Parameter Latent Trait Model for Binary Data
63(22)
Panagiota Tzamourani
Martin Knott
Analysing Group Differences: A Comparison of SEM Approaches
85(20)
Pilar Rivera
Albert Satorra
Strategies for Handling Missing Data in SEM: A User's Perspective
105(16)
Richard D. Wiggins
Amanda Sacker
Exploring Structural Equation Model Misspecifications via Latent Individual Residuals
121(14)
Tenko Raykov
Spirinov Penev
On Confidence Regions of SEM Models
135(18)
Jian-Qing Shi
Sik-Yum Lee
Bo-Cheng Wei
Robust Factor Analysis: Methods and Applications
153(42)
Peter Filzmoser
Using Predicted Latent Scores in General Latent Structure Models
195(30)
Marcel Croon
Multilevel Factor Analysis Modelling Using Markov Chain Monte Carlo Estimation
225(20)
Harvey Goldstein
William Browne
Modelling Measurement Error in Structural Multilevel Models
245(26)
Jean-Paul Foz
Cees A.W. Glas
Author Index 271(6)
Subject Index 277


George A. Marcoulides, Irini Moustaki