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Author: Fairlie, Anne M.
Resulting in 2 citations.
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Fairlie, Anne M. |
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Measurement Timing in Growth Mixture Modeling of Alcohol Trajectories Ph.D. Dissertation, University of Rhode Island, 2012 Cohort(s): NLSY97 Publisher: ProQuest Dissertations & Theses (PQDT) Keyword(s): Adolescent Behavior; Alcohol Use; Modeling Permission to reprint the abstract has not been received from the publisher. 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. |
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Bibliography Citation
Fairlie, Anne M. Measurement Timing in Growth Mixture Modeling of Alcohol Trajectories. Ph.D. Dissertation, University of Rhode Island, 2012. |
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Fairlie, Anne M. Bernstein, Michael Walls, Theodore A. Wood, Mark D. |
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Effects of Measurement Timing on Subgroup Identification Using Growth Mixture Modeling: An Empirical Application to Alcohol Use Psychology of Addictive Behaviors 33,3 (May 2019): 232-242 Cohort(s): NLSY97 Publisher: American Psychological Association (APA) Keyword(s): Alcohol Use; Modeling, Latent Class Analysis/Latent Transition Analysis; Modeling, MIxture Models/Finite Mixture Models Growth mixture modeling (GMM) identifies latent classes exhibiting distinct longitudinal patterns on an outcome. Subgroups identified by GMM may be artifactually influenced by measurement timing (e.g., timing of the initial assessment, length of the interval from the first to the last assessment, and total number of assessments) as well as the theoretically posited developmental patterns of the behavior. The current study investigated this possibility using alcohol data from the 1997 National Longitudinal Survey of Youth (n = 2686; 49.44% female; 71.84% White). Three assessment configurations were examined: all 12 waves, first 6 waves, and last 7 waves. Five subgroups were identified using all 12 waves: Normative (71.33%), Low-Increasing (8.45%), Low-Steady (8.97%), High-Slowly Decreasing (7.67%), and Extreme-Sharply Decreasing (3.57%). When comparing participants’ subgroup membership for all 12 waves to the first six waves, 14% of the sample was differentially classified. When comparing all 12 waves to the last seven waves, 62% of the sample was differentially classified. Alterations in the timing of the initial assessment had a substantial impact on latent class estimation, underscoring the importance of selecting the developmental window a priori based on theory and empirical knowledge. The time-bounded nature of mixture modeling solutions (i.e., a selected developmental window within the course of a phenomenon) suggests that the latent subgroups should not be interpreted as representing subgroups that are present in the population. Future directions and strategies for testing alternative interpretations are presented. (PsycINFO Database Record (c) 2019 APA, all rights reserved) |
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Bibliography Citation
Fairlie, Anne M., Michael Bernstein, Theodore A. Walls and Mark D. Wood. "Effects of Measurement Timing on Subgroup Identification Using Growth Mixture Modeling: An Empirical Application to Alcohol Use." Psychology of Addictive Behaviors 33,3 (May 2019): 232-242.
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