This paper introduces Latent Growth Modeling (LGM) as a feasible method for analyzing longitudinal data to understand the process of change over time. Given the need to go beyond cross-sectional models, explore longitudinal Information Systems (IS) phenomena, and test IS theories over time, LGM is proposed as a complementary method to help IS researchers propose and evaluate time-centric hypotheses and make longitudinal inferences.

The paper first describes the basic tenets of LGM and offers guidelines for using LGM in IS research, including framing hypotheses with time as a central component and implementing LGM models to test these hypotheses. The application of LGM in IS research is illustrated by modeling the longitudinal relationship between two IT variables (IT infrastructure and IT labor) and firm performance with 2001-2004 data from Fortune 1000 firms. Comparisons with other methods for analyzing longitudinal data reveal the advantages of LGM for studying time-dependent relationships and growth patterns.