Data on twins or on other types of family structures (for example, nuclear families, siblings, cousins) can be used to estimate the proportion of variability in observed traits (or phenotypes) that is due to genes. The models are essentially multivariate regression models with residual...

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...

This presentation focuses on predicted probabilities for multilevel models for dichotomous or ordinal responses. In a three-level model, for instance with patients nested in doctors nested in hospitals, predictions for patients could be for new or existing doctors and, in the latter case, for...

gllamm is a program to fit generalised linear latent and mixed models. Since gllamm6 appeared in the STB (sg129), a large number of new features have been added. Two important extensions will be discussed: 1) More response processes can now be modelled including ordered and unordered categorical...

This presentation focuses on predicted probabilities for multilevel models for dichotomous or ordinal responses. In a three-level model, for instance, with patients nested in doctors nested in hospitals, predictions for patients could be for new or existing doctors and, in the latter case, for...

This report summarises the 'wishes and grumbles' session from the Ninth UK Stata Users Group meeting.

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...

gllamm can estimate both conventional and unconventional latent class models. Models are specified using discrete latent variables whose values determine the conditional response distributions for the classes. A new feature of gllamm is that latent class probabilities can depend on covariates....

Survey data are often analyzed using multilevel or hierarchical models. For example, in education surveys, schools may be sampled at the first stage and students at the second stage and multilevel models used to model within-school and between-school variability. An important aspect of most...