Whether estimating growth models in a structural equation or multilevel modeling framework, the researcher must choose how to numerically code the passage of time. In this episode of Office Hours, Patrick explores the implications of scaling time within the general growth curve model. Patrick begins by revisiting the interpretation of the intercept of a regression line and then applies this to a growth curve. He then examines the implications of using time in the raw chronological metric and compares this to manually rescaling time so that the value of zero represents the beginning, middle, or end of a trajectory process. He uses an example of modeling developmental trajectories of aggressive behavior during childhood and refers to a fully worked example. He also refers to two papers as recommended reading on this topic:
Biesanz, J.C., Deeb-Sossa, N., Aubrecht, A.M., Bollen, K.A., & Curran, P.J. (2004). The role of coding time in estimating and interpreting growth curve models. Psychological Methods, 9, 30-52.
Hancock, G. R., & Choi, J. (2006). A vernacular for linear latent growth models. Structural Equation Modeling, 13, 352-377
To see all episodes in this series, see our Growth Modeling playlist.