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