Rasch Models for Longitudinal Data

Abstract

The chapter gives an overview of Rasch models for the measurement of change across repeated observations of the same individuals and items. The models described herein include extensions of the original Rasch model that allow one to analyze multidimensional latent constructs and to incorporate heterogeneity of change across individuals. In particular, the use of mixture-distribution Rasch models in longitudinal research allows one to model quantitative interindividual differences in a latent trait at each occasion, together with qualitative interindividual differences in the course of development. A mover–stayer mixed-Rasch model can be specified as a special case that reflects the assumption that change over time occurs for some latent subpopulation but not for another. An empirical example illustrates that the mover–stayer mixed-Rasch model can provide a parsimonious and viable account of observed heterogeneity of change.