This tutorial introduces Latent Growth Modeling (LGM) as a promising new method for analyzing longitudinal data when interested in understanding the process of change over time. Given the need to go beyond cross-sectional models in IS research, explore complex longitudinal IS phenomena, and test Information Systems (IS) theories over time, LGM is proposed as a complementary method to help IS researchers propose time-dependent hypotheses and make longitudinal inferences about IS theories. The tutorial leader will explain the importance of theorizing patterns of change over time, how to propose longitudinal hypotheses, and how LGM can help test such hypotheses. All three tutorial facilitators will describe the tenets of LGM and offer guidelines for applying LGM in IS research including framing time-dependent hypotheses that can be readily tested with LGM. The three tutorial facilitators will also explain how to use LGM in SAS 9.2 with a hands-on application that will attempt to model the complex longitudinal relationship between IT and firm performance using longitudinal data from Fortune 1000 firms. The tutorial facilitators will also draw comparisons with other existing methods for modeling longitudinal data and they will also discuss the advantages and disadvantages of LGM for identifying longitudinal patterns in data.
Gu, Bin and Pavlou, Paul A., "Tutorial on Latent Growth Models for Longitudinal Data Analysis" (2010). AMCIS 2010 Proceedings. 64.