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The effect of an independent variable on random slopes in growth modeling with latent variables is conventionally used to examine predictors of change over the course of a study. This article demonstrates that the same effect of a covariate on growth can be obtained by using final status centering for parameterization and regressing the random intercepts (or the intercept factor scores) on both the independent variable and a baseline covariate–the framework used to study change with classical regression analysis. Examples are provided that illustrate the application of an intercept-focused approach to obtain effect sizes–the unstandardized regression coefficient, the standardized regression coefficient, squared semi-partial correlation, and Cohen’s  f2 –that estimate the same parameters as respective effect sizes from a classical regression analysis. Moreover, statistical power to detect the effect of the predictor on growth was greater when using random intercepts than the conventionally used random slopes.

Regression-Equivalent Effect Sizes for Latent Growth Modeling and Associated Null Hypothesis Significance Tests