Formulating latent growth using an explanatory item response model approach.

Abstract

In this paper, we present a way to extend the Hierarchical Generalized Linear Model (HGLM; Kamata (2001), Raudenbush (1995)) to include the many forms of measurement models available under the formulation known as the Random Coefficients Multinomial Logit (MRCML) Model (Adams, Wilson and Wang, 1997), and apply that to growth modeling. First, we review two different traditions in modeling growth studies: the first is based in the hierarchical linear modeling (HLM) tradition, and the second, which is the topic of this paper, is rooted in the Rasch measurement tradition - this is the linear Latent Growth Item Response Model (LG-IRM). Going beyond the linear case, the LG-IRM approach allows us t..