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 purpose of this research was to analyze participants' perceptions of the impact of premarital and relationship education workshops offered across the state of Texas. Regional marriage coalition leaders conducted online and telephone interview surveys of 1,109 participants between 6 and 24 months after participating in the workshops. Research questions included whether participants perceived the workshops as helping to improve their relationship skills and quality, whether these evaluations differed by demographics, and how participant relationship status changed after the workshop. A large majority of participants reported their relationship skills had improved as a result of the workshop. Workshop impact scores generally did not differ by gender, age, cohabitation status, and socioeconomic status. However, Hispanic participants reported somewhat higher workshop impact scores.