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A Monte Carlo Evaluation of Growth Mixture Models: Effects of Varying Distributional Parameters on Grouping Outcomes

Shader, Tiffany M
http://orcid.org/0000-0002-0564-0555
http://rave.ohiolink.edu/etdc/view?acc_num=osu1567982510705056

Abstract Details

2019, Doctor of Philosophy, Ohio State University, Psychology.
Statistical methods designed to group individuals into homogenous subsets have existed for decades. Historically, when research identifies weakness in one method, new methods are developed in attempts to provide more valid subgroups. At present, growth mixture modeling (GMM) and its variants, which group individuals based on similar longitudinal growth trajectories, is popular. However, research addressing the validity of latent subgroups is limited. This Monte Carlo simulation study tests the efficiency of GMM in correctly identifying known numbers of subgroups in data sets across various conditions. GMM was applied to randomly simulated data, and to data containing 1, 2, 3, and 4 latent trajectory classes. Effects of varying skew and kurtosis were also examined, as were effects of sample size, intercept effect size, and proportions within latent subgroups. Growth patterns were set at zero (no growth), linear growth, and quadratic growth. A total of 2,090 combinations of parameters were examined, with 1,000 replications each. Results were mixed. When data were completely random, GMM often failed to converge, or identified one group with errors in model fit. When one group was simulated with varying skew and kurtosis, GMM often identified multiple groups. When two groups were simulated, GMM performed well with steep linear growth or quadratic growth. When 3 to 4 groups were simulated, GMM was most effective when intercept effect sizes and sample sizes were large. In other cases, GMM often underestimated the correct number of groups when the true number was between 2 and 4. These results indicate significant limitations of GMM that have not been articulated to date, and suggest more care should be used with the method in the developmental and psychopathology literatures.
Theodore Beauchaine (Advisor)
173 p.
Clinical Psychology; Statistics
Methods; developmental psychopathology; growth mixture modeling; longitudinal analysis

Recommended Citations

Citations

  • Shader, T. M. (2019). A Monte Carlo Evaluation of Growth Mixture Models: Effects of Varying Distributional Parameters on Grouping Outcomes [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1567982510705056

    APA Style (7th edition)

  • Shader, Tiffany. A Monte Carlo Evaluation of Growth Mixture Models: Effects of Varying Distributional Parameters on Grouping Outcomes . 2019. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1567982510705056.

    MLA Style (8th edition)

  • Shader, Tiffany. "A Monte Carlo Evaluation of Growth Mixture Models: Effects of Varying Distributional Parameters on Grouping Outcomes ." Doctoral dissertation, Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1567982510705056

    Chicago Manual of Style (17th edition)

osu1567982510705056
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© 2019, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.