Date of Award
Doctor of Philosophy in Psychology
A more nuanced understanding of individuals' patterns of alcohol use during adolescence, a key developmental period for the onset of use, is of critical importance for refining preventive interventions in this population. To this end, growth mixture modeling (GMM) is a statistical technique that may be used to identify latent subgroups of individuals who exhibit distinct patterns of alcohol use over time. Decisions regarding the timing and interval of survey assessments are particularly challenging in the context of GMM. Latent subgroups exhibit different trends in alcohol use, and these trends must be adequately captured by the survey assessments. Accordingly, the specific aims of the current research were to investigate how measurement timing (i.e., timing and spacing of assessments) affected the identification of the latent subgroups with: (1) an applied study using alcohol data from the National Longitudinal Survey of Youth (NLSY) 1997 and (2) a Monte Carlo simulation study. Participants from the NLSY1997 were 15 and 16 years old at Wave 1 (n = 2686, 49.44% female). Alterations in measurement timing were examined using five different assessment configurations: all 12 waves, two-year intervals, uneven intervals, the first six waves, and the last seven waves. The outcome, the number of drinks consumed per month, was assessed at each of 12 waves that spanned 11.5 years. The results of the applied study with the NLSY data were used as population parameters in the simulation study. The experimental factors investigated in the simulation study were measurement timing and sample size. First, the applied study revealed that the five-class GMM results were very similar when using all 12 waves versus two-year intervals. Only four participants were misclassified (i.e., assigned to subgroups with different average alcohol trajectories). Second, the five-class GMM results when comparing all 12 waves to either the configuration with uneven intervals or the first six waves showed some degree of discrepancy with approximately 14% of the sample being misclassified. Third, the largest discrepancy in the five-class GMM results was observed when comparing the 12 wave and last seven wave configurations with 62% of the sample being misclassified. The simulation study showed that the 95% coverage estimates of the parameters (i.e., factor means, factor variances, factor covariance) were greater than .90 for four of the five assessment configurations, with the exception being the last seven waves. Three of the five assessment configurations produced average estimates of the parameters that were close to the population values. There was less precision in the parameter estimates, as indicated by larger average standard error estimates, for the configurations using the first six waves and the last seven waves. Collectively, these findings strongly suggest that the developmental window under investigation (i.e., all 12 waves versus the first six or last seven waves) had the most substantial impact on the reliability and validity of the five-class GMM solution. The sensitivity of the GMM solution to the timing of the survey assessments (i.e., developmental window) suggests that the latent classes should not be interpreted as representing subgroups that are present in the population. Instead, the identification of latent subgroups is sensitive to variations in research design, which include, but may not be limited to, measurement timing. It is important to better understand how these complex statistical approaches may be artifactually influenced by variations in research design. It may then be possible to have more informed evaluations of how prevention and intervention programs can alter individuals' patterns of alcohol use.
Fairlie, Anne M., "Measurement Timing In Growth Mixture Modeling of Alcohol Trajectories" (2012). Open Access Dissertations. Paper 1094.