ABSTRACT
Structural equation modeling (SEM) is one of the most flexible and commonly used tools in the statistical toolbox of the social scientist. Latent growth curve modeling (LGM), the subject of this chapter, is one application of SEM to the analysis of change. In LGM, repeated measures of a variable (hereafter, Y) are treated as indicators of latent variables, called basis curves, that represent aspects of change-typically intercept and linear slope factors. Values of the time metric (e.g., age, day, or wave of measurement) are built into the factor loading matrix to reflect the form of the hypothesized trajectory, or trend over time. There are many extensions of this idea, but these are the basic elements common to all applications of LGM. LGM contains elements of both variable-centered and personcentered approaches (Curran & Willoughby, 2003), in that a sample-level summary of change is provided, yet individual differences in initial status and change are also considered.
