Growth Models with Time-Varying Covariates

CenterStat June 20, 2017

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.

Categories: Office Hours Videos

CenterStat

The Center for Statistical Training by Curran-Bauer Analytics provides livestream and on-demand workshops on advanced quantitative methods for researchers in the social, health, and behavioral sciences.

What exactly qualifies as intensive longitudinal data and why am I not able to use more traditional growth models to study stability and change over time?

This post considers the unique features of intensive longitudinal data (ILD) relative to other more traditional data structures and how we can appropriately analyze ILD given these features

Patrick Curran and Dan Bauer October 14, 2022

I fit a multilevel model and got the warning message “G Matrix is Non-Positive Definite.” What does this mean and what should I do about it?

Received the cryptic warning message “G matrix is non-positive definite”? Learn what this means and what to do about it.

Patrick Curran and Dan Bauer July 22, 2021

My advisor told me I should group-mean center my predictors in my multilevel model because it might “make my effects significant” but this doesn’t seem right to me. What exactly is involved in centering predictors within the multilevel model?

How to specify multilevel models to obtain within- and between-group effects through centering lower-level predictors.

Patrick Curran and Dan Bauer March 16, 2021

My advisor told me to use principal components analysis to examine the structure of my items and compute scale scores, but I was taught not to use it because it is not a “true” factor analysis. Help!

We explain the difference between principal components analysis and exploratory factor analysis

Patrick Curran and Dan Bauer October 12, 2021

What are ROC curves and how are these used to aid decision making?

ROC curves are used to identify cut points on continuous measures at which to make binary decisions. An example is a depression screener used to identify people who may qualify for a major depression diagnosis. This post explains the concepts behind ROC curves and how they are used for decision making.

Patrick Curran and Dan Bauer September 18, 2023

Related Articles