Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models(Chapman & Hall/CRC Interdisciplinary Statistics)

This book unifies and extends latent variable models, including multilevel or generalized linear mixed models, longitudinal or panel models, item response or factor models, latent class or finite mixture models, and structural equation models. Following a gentle introduction to latent variable modeling, the authors clearly explain and contrast a wide range of estimation and prediction methods from biostatistics, psychometrics, econometrics, and statistics. They present exciting and realistic applications that demonstrate how researchers can use latent variable modeling to solve concrete problems in areas as diverse as medicine, economics, and psychology. The examples considered include many nonstandard response types, such as ordinal, nominal, count, and survival data. Joint modeling of mixed responses, such as survival and longitudinal data, is also illustrated. Numerous displays, figures, and graphs make the text vivid and easy to read. About the authors: Anders Skrondal is Professor and Chair in Social Statistics, Department of Statistics, London School of Economics, UK Sophia Rabe-Hesketh is a Professor of Educational Statistics at the Graduate School of Education and Graduate Group in Biostatistics, University of California, Berkeley, USA.

Table of Contents:
METHODOLOGY THE OMNI-PRESENCE OF LATENT VARIABLES Introduction `True’ variable measured with error Hypothetical constructs Unobserved heterogeneity Missing values and counterfactuals Latent responses Generating flexible distributions Combining information Summary MODELING DIFFERENT RESPONSE PROCESSES Introduction Generalized linear models Extensions of generalized linear models Latent response formulation Modeling durations or survival Summary and further reading CLASSICAL LATENT VARIABLE MODELS Introduction Multilevel regression models Factor models and item response models Latent class models Structural equation models with latent variables Longitudinal models Summary and further reading GENERAL MODEL FRAMEWORK Introduction Response model Structural model for the latent variables Distribution of the disturbances Parameter restrictions and fundamental parameters Reduced form of the latent variables and linear predictor Moment structure of the latent variables Marginal moment structure of observed and latent responses Reduced form distribution and likelihood Reduced form parameters Summary and further reading IDENTIFICATION AND EQUIVALENCE Introduction Identification Equivalence Summary and further reading ESTIMATION Introduction Maximum likelihood: Closed form marginal likelihood Maximum likelihood: Approximate marginal likelihood Maximizing the likelihood Nonparametric maximum likelihood estimation Restricted/Residual maximum likelihood (REML) Limited information methods Maximum quasi-likelihood Generalized Estimating Equations (GEE) Fixed effects methods Bayesian methods Summary Appendix: Some software and references ASSIGNING VALUES TO LATENT VARIABLES Introduction Posterior distributions Empirical Bayes (EB) Empirical Bayes modal (EBM) Maximum likelihood Relating the scoring methods in the `linear case’ Ad hoc scoring methods Some uses of latent scoring and classification Summary and further reading Appendix: Some software MODEL SPECIFICATION AND INFERENCE Introduction Statistical modeling Inference (likelihood based) Model selection: Relative fit criteria Model adequacy: Global absolute fit criteria Model diagnostics: Local absolute fit criteria Summary and further reading APPLICATIONS DICHOTOMOUS RESPONSES Introduction Respiratory infection in children: A random intercept model Diagnosis of myocardial infarction: A latent class model Arithmetic reasoning: Item response models Nicotine gum and smoking cessation: A meta-analysis Wives’ employment transitions: Markov models with unobserved heterogeneity Counting snowshoe hares: Capture-recapture models with heterogeneity Attitudes to abortion: A multilevel item response model Summary and further reading ORDINAL RESPONSES Introduction Cluster randomized trial of sex education: Latent growth curve model Political efficacy: Factor dimensionality and item-bias Life satisfaction: Ordinal scaled probit factor models Summary and further reading COUNTS Introduction Prevention of faulty teeth in children: Modeling overdispersion Treatment of epilepsy: A random coefficient model Lip cancer in Scotland: Disease mapping Summary and further reading DURATIONS AND SURVIVAL Introduction Modeling multiple events clustered duration data Onset of smoking: Discrete time frailty models Exercise and angina: Proportional hazards random effects and factor models Summary and further reading COMPARATIVE RESPONSES Introduction Heterogeneity and `Independence from Irrelevant Alternatives’ Model structure British general elections: Multilevel models for discrete choice and rankings Post-materialism: A latent class model for rankings Consumer preferences for coffee makers: A conjoint choice model Summary and further reading MULTIPLE PROCESSES AND MIXED RESPONSES Introduction Diet and heart disease: A covariate measurement error model Herpes and cervical cancer: A latent class covariate measurement error model for a case-control study Job training and depression: A complier average causal effect model Physician advice and drinking: An endogenous treatment model Treatment of liver cirrhosis: A joint survival and marker model Summary and further reading REFERENCES INDEX AUTHOR INDEX