1-155 - Mixture Modeling in Applied Developmental Research: Challenges and Opportunities

Session Type: Exchange Symposium

Integrative Statement

The complex phenomena that define human development often require an analytic framework that allows for population heterogeneity with a focus on individual differences. As such, developmental researchers are increasingly employing person-centered analytic approaches for their investigations. Finite mixture modeling (FMM) is one of the more popular analytic frameworks for person-centered analysis. FMMs assume that the overall population is comprised of two or more (latent) subpopulations, mirroring the formulation of some notable developmental theories which posit substantively-distinct developmental differences across individuals in the overall population.

Despite their utility for person-centered analyses, effectively applying FMMs in real data settings presents challenges for developmental researchers. These challenges exist, in part, because FMM methodology is an active area of statistical research and, as such, guidelines and best practices for applications are rapidly evolving with few accessible resources for applied researchers.

The goal of this exchange symposium is to stimulate discussion regarding how developmental researchers and methodologists can productively collaborate in identifying, translating, and implementing statistical best practices as they relate to the use of mixture modeling in developmental research. The symposium includes four short presentations highlighting some of the contemporary challenges in applied developmental research with mixture models: Appropriate handling of missing data; identifying the number and nature of the latent subpopulations; evaluating longitudinal invariance of the subpopulations; and identifying antecedents and consequences of subpopulation membership. Each paper includes a substantive example with implications for clinicians, educators, and parents to emphasize the transdisciplinary opportunities inherent in addressing these methodological challenges for applied mixture modeling.

Sub Unit

• Methods, History, Theory

Chair

• Sarah D. Lynne, University of Florida

Individual Presentations

• Towards Best Practices for Treating Missing Data in Mixture Models: Results from Latent Profile Analysis - Presenting Author: Marcus Waldman, Harvard Graduate School of Education; Non-Presenting Author: Katherine Masyn, Georgia State University

• Measurement Invariance in Multiple-Group and Longitudinal Mixture Models - Presenting Author: Katherine Masyn, Georgia State University

• Growth Mixture Modeling of the Impact of Adolescent Mothers’ Parenting Stress Trajectories on Children’s Self-Regulation - Presenting Author: Meera Menon, Tufts University; Non-Presenting Author: Sara K Johnson, Tufts University

• Using Latent Transition Analysis to Understand Professional Learning’s Impact on Teachers Knowledge, Beliefs, and Practices - Presenting Author: Emily Hanno, Harvard University