Ordered categorical responses can be analyzed with different kinds of logistic regression models, the most popular being the cumulative logit or proportional odds model. Alternatively, ordinal probit models can be specified. When the data have a nested structure, with repeated observations for...

Models for handling sample selection or informative missingness have been developed for both cross sectional and longitudinal or panel data. For cross sectional data, Heckman (1979) suggested a joint model for the response and sample selection processes where the disturbances of the processes...

We consider multilevel models for longitudinal data where membership in the highest level units changes over time. The application is a four-year study of Korean students who are in middle school during the first two waves and in high school during the second two waves, where middle schools and...

This talk will describe some programs to fit generalized beta of the second kind, Singh-Maddala, Dagum, and lognormal distributions to data on income or indeed any other skewed variable of interest. The programs allow the key distributional parameters to vary with covariates, and also handle svy...

Social scientists are increasingly fitting multilevel models to datasets in which a large number of individuals (N ~ several thousands) are nested within each of a small number of countries (C ~ 25). The researchers are particularly interested in “country effectsâ€, as summarized by...

Group-level variance estimates of zero often arise when fitting multilevel or hierarchical linear models, especially when the number of groups is small. For situations where zero variances are implausible a priori, we propose a maximum penalized likelihood approach to avoid such boundary...

An additive multilevel item structure (AMIS) model with random residuals is proposed. The model includes multilevel latent regressions of item discrimination and item difficulty parameters on covariates at both item and item category levels with random residuals at both levels. The AMIS model is...

In this paper, we consider the problem of missing values of a continuous response variable that cannot be assumed to be missing at random. The example considered here is an analysis of pupil's subjective engagement at school using longitudinal survey data, where the engagement score from wave 3...

When fitting hierarchical regression models, maximum likelihood (ML) estimation has computational (and, for some users, philosophical) advantages compared to full Bayesian inference, but when the number of groups is small, estimates of the covariance matrix (Σ) of group-level varying...

This manual describes a Stata program gllamm that can estimate Generalized Linear Latent and Mixed Models (GLLAMMs). GLLAMMs are a class of multilevel latent variable models for (multivariate) responses of mixed type including continuous responses, counts, duration/survival data, dichotomous,...