LIRT: SAS macros for longitudinal IRT models

Item response theory models are often applied when a number items are used to measure a unidimensional latent variable. Originally proposed within educational research, they are now also being used when focus is on e.g. physical functioning or psychological well-being. Modern applications often need more general models, typically models for multidimensional latent variables or longitudinal models for repeated measurements. This paper describes a collection of SAS macros that can be used for fitting data to and simulating from a two-dimensional generalized partial credit model. Macros for plotting item characteristic curves and estimating person locations are also included. The macros encompass both Rasch type models and models with item discrimination parameters and handle both dichotomous and polytomous item response formats. The models are sufficiently flexible to accommodate changes in item parameters across time points and local dependence between responses at different time points.