chapter  11
Testing Change Over Time: The Latent Growth Curve Model
Pages 32

Latent growth curve (LGC) modeling within the framework of structural equation modeling (SEM) is now considered one of the most powerful and informative approaches to the analysis of longitudinal data (see, e.g., Curran & Hussong, 2003). Whereas this methodological approach enables researchers to test for differences in developmental trajectories across time, conventional repeated measures analyses (e.g., analysis of variance [ANOVA], analysis of covariance [ANCOVA], and multivariate analysis of covariance [MANOVA]) fail to provide this opportunity. More specifically, although these traditional statistical strategies are capable of describing an individual’s developmental trajectory, they are incapable of capturing individual differences in these trajectories over time (Curran & Hussong, 2003; Duncan & Duncan, 1995; Fan, 2003; Willett & Sayer, 1994). Thus, they are increasingly becoming perceived as somewhat inadequate in that they prevent researchers from seeking answers to interesting and important questions bearing on such differences. For example, it might be interesting to ask, “Is there a difference in the rate of change in one’s perceived body image for breast cancer patients who have undergone lumpectomy as opposed to mastectomy surgery?”