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In a prior episode of Office Hours, Patrick discussed predicting growth by time-invariant covariates (TICs), predictors for which the numerical values are constant over time. In this episode, Patrick describes the inclusion of time-varying covariates (TVCs), predictors with numerical values that can differ across time. Examples of TVCs are numerous and include time-specific measures of depression, anxiety, substance use, marital status, onset of diagnosis, or dropout from treatment, among many others. When TICs are included in a growth model, the time-invariant predictors are used to directly predict the growth factors (e.g., intercept, slope). In contrast, when TVCs are included in a growth model, the effects of the time-varying predictors bypass the growth factors and directly influence the repeated measures. There are many ways that TVC influences can be included in the model, and models can be further extended to include both TICs and TVCs simultaneously. Patrick works through a hypothetical example and concludes with a summary of strengths and limitations of these models.

To see all episodes in this series, see our Growth Modeling playlist on YouTube.