Using latent growth modeling to understand longitudinal effects in MIS theory: A primer

Abstract

The use of structural equation modeling (SEM) has grown dramatically in the field of management information systems (MIS) in the past twenty years, but SEM's focus has been primarily on cross-sectional data sets. Functionally, SEM has been used to test measurement and path models, but the SEM approach has not been applied to repeated measures designs. In this article, we describe latent growth models (LGMs), an extension of SEM, which focuses on how observed and/or latent variables change over time. The purpose of this paper is to provide a primer on the use of LGMs, as well as to advocate for its use to extend MIS theory. We illustrate several flexible applications of LGMs using longitudinal data, including conditional, unconditional, and dual growth models. We discuss the advantages of using LGMs over other more traditional longitudinal approaches, and highlight areas in MIS where researchers can use this technique effectively. © 2011 by the Association for Information Systems.