Abstract
Geared towards capturing change, longitudinal research is able to provide insight into a variety of phenomena of interest to IS researchers, especially IT adoption and use. However, its potential is constrained by the data analysis methods typically used. In this paper, I introduce an advanced technique – Latent Curve Modeling – and demonstrate how this technique supports longitudinal data analysis using system use data collected at an international management consulting firm. Latent Curve Modeling helps capture temporal patterns better than existing methods, and provides methods to identify the causes of change in patterns. With rich information in the discussion of the technique and the results of the empirical tests, I recommend it as a valuable option for IS researchers who are interested in research involving temporal changes.
Recommended Citation
Qureshi, Israr, "Latent Growth Modeling and Latent Class Analysis" (2009). PACIS 2009 Proceedings. 39.
https://aisel.aisnet.org/pacis2009/39