Growth mixture model (GMM) is a flexible statistical technique for analyzing longitudinal data when there are unknown heterogeneous subpopulations with different growth trajectories. When individuals are nested within clusters, multilevel growth mixture model (MGMM) should be used to account for the clustering effect. A review of recent literature shows that a higher level of nesting was described in 43% of articles using GMM, none of which used MGMM to account for the clustered data. We conjecture that researchers sometimes ignore the higher level to reduce analytical complexity, but in other situations, ignoring the nesting is unavoidable. This Monte Carlo study investigated whether the correct number of classes can still be retrieved when a higher level of nesting in MGMM is ignored. We investigated six commonly used model selection indices: Akaike information criterion (AIC), consistent AIC (CAIC), Bayesian information criterion (BIC), sample size-adjusted BIC (SABIC), Vuong-Lo-Mendell-Rubin likelihood ratio test (VLMR), and adjusted Lo-Mendell-Rubin likelihood ratio test (ALMR). Results showed that accuracy of class enumeration decreased for all six indices when the higher level is ignored. BIC, CAIC, and SABIC were the most effective model selection indices under the misspecified model. BIC and CAIC were preferable when sample size was large and/or intraclass correlation (ICC) was small, whereas SABIC performed better when sample size was small and/or ICC was large. In addition, SABIC and VLMR/ALMR tended to overextract the number of classes when there are more than two subpopulations and the sample size is large.
The current study examined whether interpersonal behaviors and perceptions could be conceptualized and measured as relationship-general versus relationship-specific tendencies. To address this goal, we examined (1) the similarities (or concordance) in support-seeking across individuals' social relationships and (2) how support-seeking may be related to relationship closeness at the relationship-general and relationship-specific levels. Participants were recruited from a regional university and granted course credits for participation. The final sample included 189 undergraduate students with age ranging from 18 to 21 years (M = 18.81; SD = .95), with a total of 66% females and 87% White-Americans. Results suggest that individuals expressed similar rates of support-seeking across parental, best-friend, and romantic relationships. Supporting our hypotheses, a relationship-general correlation suggests that individuals who are more likely to seek social support also perceived their social relationships as generally more intimate. Controlling for relationship-general tendencies, results also suggest that support-seeking was related to relationship closeness for each type of relationship. Theoretical and methodological implications are discussed.
Glaman, Ryan. Comparing Three Approaches for Handling a Fourth Level of Nesting Structure in Cluster-Randomized Trials. Doctor of Philosophy (Educational Psychology), August 2017, 41 pp., 5 tables, references. 45 titles. This study compared 3 approaches for handling a fourth level of nesting structure when analyzing data from a cluster-randomized trial (CRT). CRTs can include 3 levels of nesting: repeated measures, individual, and cluster levels. However, above the cluster level, there may sometimes be an additional potentially important fourth level of nesting (e.g., schools, districts, etc., depending on the design) that is typically ignored in CRT data analysis. The current study examined the impact of ignoring this fourth level, accounting for it using a model-based approach, and accounting it using a design-based approach on parameter and standard error (SE) estimates. Several fixed effect and random effect variance parameters and SEs were biased across all 3 models. In the 4-level model, most SE biases decreased as the number of level 3 clusters increased and as the number of level 4 clusters decreased. Also, random effect variance biases decreased as the number of level 3 clusters increased. In the 3-level and complex models, SEs became more biased as the weight level 4 carried increased (i.e., larger intraclass correlation, more clusters at that level). The current results suggest that if a meaningful fourth level of nesting exists, future researchers should account for it using design-based approach; the modelbased approach is not recommended. If the fourth level is not practically important, researchers may ignore it altogether.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.