Modeling and Testing Change: An Introduction to the Latent Growth Curve Model

The purposes of this article are threefold: (a) to outline the basic concepts associated with latent growth curve (LGC) modeling; (b) to demonstrate the modeling and testing of LGC models based on three relatively simple, albeit increasingly complex, examples; and (c) to illustrate the modeling mechanism used in testing for the tenability of key statistical assumptions associated with LGC modeling. Based on 3-wave data comprising an original sample of 601 adolescents (Grades 8, 9, and 10) and using a multiple-sample approach that takes into account missing data resulting from time-related attrition, we "walk" the reader through the various stages of the model specification and testing processes. Based on self-rating scores of perceived ability as the outcome variable, we begin with a single-domain LGC model of perceived math ability and then follow with a more complex multiple-domain model that includes perceived ability in math, language, and science. Our final application extends the multiple-domain model to include the predictor variable of gender. We conclude by summarizing several advantages of LGC modeling over the more traditional methods used in the measurement of change.