Advances in Latent Class Analysis

Gregory R. Hancock, University of MarylandJeffrey R. Harring, University of MarylandGeorge B. Macready, University of Maryland

• Preface.Biographic Sketch of Chauncey Mitchell Dayton.Acknowledgments.Chapter 1. On the Measurement of Noncompliance Using (Randomized) Item Response Models; Ulf Böckenholt, Maarten Cruyff, Peter G. M. van der Heijden, and Ardo van den Hout.Chapter 2. Understanding Latent Class Model Selection Criteria by Concomitant-Variable Latent Class Models; José G. Dias.Chapter 3. Comparison of Multidimensional Item Response Models: Multivariate Normal Ability Distributions Versus Multivariate Polytomous Ability Distributions; Shelby J. Haberman, Matthias von Davier, and Yi-Hsuan Lee.Chapter 4. Nonloglinear Marginal Latent Class Models; Jacques A. Hagenaars, Wicher Bergsma, and Marcel Croon.Chapter 5. Mixture of Factor Analyzers for the Clustering and Visualization of High-Dimensional Data; Geoffrey J. McLachlan, Jangsun Baek, and Suren I. Rathnayake.Chapter 6. Multimethod Latent Class Analysis; Fridtjof W. Nussbeck and Michael Eid.Chapter 7. The Use of Graphs in Latent Variable Modeling: Beyond Visualization; Frank Rijmen.Chapter 8. Logistic Regression With Floor and Ceiling Effects: Fixed and Random Effects Models; David Rindskopf and Patrick E. Shrout.Chapter 9. Model Based Analysis of Incomplete Data Using the Mixture Index of Fit; Tamás Rudas, Emese Verdes, and Juraj Medzihorsky.Chapter 10. A Systematic Investigation of Within-Subject and Between-Subject Covariance Structures in Growth Mixture Models; Junhui Liu and Jeffrey R. Harring.Chapter 11. Latent Class Scaling Models for Longitudinal and Multilevel Data Sets; Jeroen K. Vermunt.Chapter 12. Modeling Structured Multiple Classification Latent Classes in Multiple Populations; Xueli Xu and Matthias von Davier.About the Editors.

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