A natural extension of linear regression models where the coefficients can vary over time. This flexible approach allows the mean trajectory and effects of covariates to vary with time without assuming parametric (e.g., linear or quadratic) functions.
Covariates with values that change over time (unlike covariates such as gender, which are time-invariant). For example if you study teen drinking behavior, stress will be a time-varying covariate, because each teen’s stress level can vary from moment-to-moment and day-to-day.
Covariates with values that remain the same over the course of a study. Examples include gender, race/ethnicity, family structure, family history of drug abuse, and intervention condition (for experiments with a single point of randomization).
The effect of a covariate may change over time, whether or not the covariate itself varies over time. For example, in a smoking cessation intervention study, assignment to a nicotine replacement therapy condition might show an immediate effect on cravings, but that effect may diminish with time.
The effect of a covariate is constant across the study. In a study of smoking behavior that follows students from age 12 to age 18, if boys were 15% more likely than girls to smoke during the entire study, gender would have an effect, but that effect would be time-invariant.
Repeated sampling of study participants’ experiences, emotions, behaviors, and/or situations in real time, within the context of their life (not a laboratory). Smartphones are often used to collect EMA.
Intensive longitudinal data (ILD) are data with many measurements over time. A limit of 40 observations is often used a threshold, but the characteristics of the data are much more important than the number of observations.
Time-Varying Effect Model (TVEM)
Methodology Center research on ILD focuses on the time-varying effect model (TVEM), which lets researchers see changes in relationships between variables without making assumptions about the nature of those relationships.
Thanks to smartphones and other technological advances, ILD are becoming more and more common. This rich data can offer scientists the ability to answer many interesting questions that were previously unanswerable.
Introductory Example: The Dynamics of Quitting Smoking
TVEM makes it possible for scientists to observe change over time in the factors that influence an outcome. For example, attempts to quit smoking are influenced by a broad range of factors, including mood, belief in one's ability to quit, and stress level. Using TVEM, we can model the changes in these relationships. This allows us to determine when and under what circumstances an individual might need additional support in order to succeed.