When the Sidekick Becomes the Hero: A Multilevel SEM Approach to Modeling Variability

Abstract: 

Important research questions about intraindividual variability (e.g., consistency across repeated measurements) or intragroup variability (e.g., cohesiveness among members within a team), rather than means, have increasingly become the focal interest across the social, behavioral, and health sciences. To address the research needs in modeling random variability as the key construct, Feng and Hancock (2020, 2022) proposed a multilevel SEM-based modeling approach where variability can be modeled as a random variable (MSEM-RV). This modeling framework is a highly flexible analytical tool that can model variability in observed measures or latent constructs, variability as the predictor or the outcome, as well as the between-subject comparison of variability across observed groups. To address the methodological challenges of detecting the unobserved sub-populations that differ in intraindividual variability or intragroup variability, or sub-populations that differ in the various processes and mechanisms involving intraindividual variability or intragroup variability, this framework was further extended to a finite-mixture MSEM for modeling unobserved heterogeneity in random variability (MMSEM-RV; Feng, 2023).