Generalized Latent Variable Modeling

Anders Skrondal, Sophia Rabe-Hesketh

“… an extremely useful resource for statisticians working in medical and biological sciences and social sciences such as economics and psychology. Most statisticians apply some form of latent variable modeling in their research, and this book presents the latest developments in the field in a clear and engaging way.” — Fiona Steele, University of Bristol, in Statistical Methods in Medical Research,, 2008, Vol. 17 “… an elegant and illuminating unification of concepts and models from diverse disciplines. The final application chapters deal with a broad collection of interesting applications to areas, such as meta-analyses, disease mapping, confirmatory factor analysis, and case-control studies. The book is well worth acquiring and would be a suitable text for advanced graduate courses.” —ISI Short Book Reviews“Written by well-known experts in biostatistics and educational statistics, it presents a uniform approach to enriching both theoretical and applied latent variables modeling that also can be used in any branch of natural science or technical and engineering application. … Numerous interesting examples … are considered. … Written in a very friendly and mathematically clear language, rigorous but not overloaded with redundant pure statistical derivations, the book could be exceptionally useful for practitioners. … This book is a really enjoyable and useful reading for graduate students and researchers along with [those] from any field who wish to use modern statistical techniques to solve practical problems.” —Technometrics, May 2005, Vol. 47, No. 2 “This is perhaps the only book that uses the ‘latent’ modeling framework to address a range of data analytical situations. … it provided a great introduction to this field.” —Dr. S.V. Subramanian, Harvard University “This is a very impressive book … an excellent book. I have no hesitation in recommending readers to buy this book.” —The Stata Journal, 2005 “Who will profit from reading this book? On the one hand, it is a book written for people who like to construct and read about very general theories and modeling strategies. It is also a very useful book for statisticians who have specialized in one area … and would like to learn more about another area. The book itself is very well-written. The presentation is concise; many issues are well illustrated graphically. [T]he authors have written an excellent, imaginative, and authoritative text on the difficult topic of modeling the problems of multivariate outcomes with different scaling levels, different units of analysis, and different study designs simultaneously.” —Biometrics, March 2005 “It has two fundamental features that make it one of the most comprehensive reference books in the field: an up-to-date guide to multilevel and structural latent variable modeling and estimation, plus a multidisciplinary set of illustrative examples … these are extremely enlightening for experienced practitioners in the many areas in which latent variable modeling can be used to analyze data … to my knowledge, the present book is the first to provide a truly unifying generalized approach to latent variable modeling … I find the book to be an exceedingly valuable reference that would be ideal for graduate-level courses on generalized latent variable modeling. It is very straightforward to build from it a comprehensive course where the statistical section is complemented with a multidisciplinary set of easily replicated examples, because both the data sets and the software are available online … the book’s impressive breadth and depth make it an essential reference for any researchers interested in understanding the state-of-the-art methods and potential applications in latent multilevel, longitudinal, and structural equation modeling.” —Journal of the American Statistical Association“[This book] provides a useful summary and references… . [It] illustrates the close connection between models for discrete choice data common in econometrics and IRT.” —Psychometrika

• METHODOLOGY: The Omni-Presence of Latent Variables. Modeling Different Response Processes. Classical Latent Variable Models. General Model Framework. Identification and Equivalence. Estimation. Assigning Values to Latent Variables. Model Specification and Inference. APPLICATIONS: Dichotomous Responses. Ordinal Responses. Counts. Durations and Survival. Comparative Responses. Multiple Processes and Mixed Responses.