Modeling Behavior Change Over Time
I am interested in modeling change over time in risky sexual behavior during adolescence, but I cannot decide how to code my outcome variable. I could create a dummy variable at each time point that indicates whether or not the individual has had intercourse, a count variable for the number of partners, or a continuous measure of the proportion of times they used a condom, but none of these approaches seems to capture the complex nature of the behavior. — Uni Dimensional
You are right that all of these dimensions (having intercourse, number of partners, proportion of times used a condom) may be important to take into account in a model of sexual risk behavior. It’s difficult to imagine a single composite score that could reflect these different aspects of behavior. One approach you might consider is modeling the outcome as a latent class variable. Latent class analysis (LCA) is a measurement model that identifies underlying subgroups in a sample based on a set of categorical indicators. In your study, each indicator can correspond to a different dimension or aspect of behavior. This approach would yield a set of subgroups of individuals, each characterized by a particular behavioral pattern. Although each individual’s true class membership is unknown, the model provides estimates of an individual’s probability of membership in each class.
Latent class analysis can be extended to longitudinal data. This approach, called latent transition analysis (LTA), is used to estimate transitions over time in latent class membership. In other words, development in discrete latent variables over two or more times can be examined. Once a latent transition model is selected, covariates can be incorporated in the model as predictors of initial status or as predictors of change over time in the behavior. This would allow you to address questions such as “Does parental permissiveness in middle school predict sexual behavior in middle school, or transitions to more risky behavior in high school?” In addition, the model can be extended to include multiple groups in order to determine whether characteristics such as ethnicity moderate the relation between parental permissiveness and transitions to more risky behavior.
Lanza & Collins (2008) demonstrated the use of latent transition analysis to model change over time in dating and sexual risk behavior across three time points during emerging adulthood. Data were from three waves of the National Longitudinal Survey of Youth 1997. Four categorical indicators of the latent variable were assessed each year: the number of dating partners in the past year (0, 1, 2 or more), past-year sex (yes, no), the number of sexual partners in the past year (0, 1, 2 or more), and unsafe sex, that is, sex without a condom, in the past year (yes, no). The following five latent classes were identified at each time: Non-daters (18.6% at Time 1), Daters (28.9%), Monogamous (11.7%), Multi-partner safe (23.1%), and Multi-partner exposed (17.7%). Transition probabilities showed that members of the higher-risk Multi-partner exposed latent class were most stable in their behavior across time. Interestingly, individuals who were most likely to transition into this higher-risk latent class were members of the Monogamous latent class, suggesting that the Monogamous group might be an important target for prevention efforts. Alcohol, cigarette, and marijuana use all were significantly related to sexual risk behavior at Time 1, although alcohol and marijuana use were stronger predictors of membership in the higher-risk Multi-partner exposed latent class than cigarette use. Past-year drunkenness predicted transitions from the Non-daters and Daters latent classes to the Multi-partner exposed latent class.
PROC LCA & PROC LTA, SAS® procedures for latent class and latent transition analysis, are available free of charge from The Methodology Center website.
Lanza, S. T., & Collins, L. M. (2008). A new SAS procedure for latent transition analysis: Transitions in dating and sexual risk behavior. Developmental Psychology, 44(2) 446-456.