• Publications
  • Influence
Multilevel and Longitudinal Modeling Using Stata
Preface Linear Variance-Components Models Introduction How reliable are expiratory flow measurements? The variance-components model Modeling the Mini Wright measurements Estimation methods Assigning
Multilevel modelling of complex survey data
Summary.  Multilevel modelling is sometimes used for data from complex surveys involving multistage sampling, unequal sampling probabilities and stratification. We consider generalized linear mixed
Reliable Estimation of Generalized Linear Mixed Models using Adaptive Quadrature
A multilevel version ofthismethodin gllamm is implemented, a program that fits a large class of multileVEL latent variable models including multilesvel generalized linear mixed models, and it is shown that adaptive quadrature works well in problems where ordinary quadratures fails.
Multilevel and longitudinal modeling using Stata. 2nd edition
This text is a Stata-specific treatment of generalized linear mixed models, also known as multilevel or hierarchical models. These models are "mixed" in the sense that they allow fixed and random
Generalized latent variable models: multilevel, longitudinal, and structural equation models
METHODOLOGY THE OMNI-PRESENCE OF LATENT VARIABLES Introduction 'True' variable measured with error Hypothetical constructs Unobserved heterogeneity Missing values and counterfactuals Latent responses
Generalized multilevel structural equation modeling
Maximum likelihood estimation and empirical Bayes latent score prediction within the GLLAMM framework can be performed using adaptive quadrature in gllamm, a freely available program running in Stata.
Multilevel and Longitudinal Modeling Using Stata, Second Edition
Multilevel and Longitudinal Modeling Using Stata, Second Edition discusses regression modeling of clustered or hierarchical data, such as data on students nested in schools, patients in hospitals, or
Prediction in multilevel generalized linear models
This work discusses prediction of random effects and of expected responses in multilevel generalized linear models and presents approximations and suggests using parametric bootstrapping to obtain standard errors.