The purpose of this study was to gain a clearer understanding of the relation between parental relationship qualities and overall emerging adulthood (EA) marijuana use processes. The present study drew from an ethnically and socioeconomially diverse sample of EAs (ages 19-22) and their parents (n = 470) from the Pacific Northwest region. This study used parent-report and child-report data to capture measures of parenting and EA marijuana use outcomes. Latent Class Growth Analysis (LCGA) was used to model trajectories of marijuana use and risk factor analyses were used to examine how marijuana group membership varied by covariates and parental relationship qualities. Results revealed that lower levels of family cohesion and quality of parent-child communication were more likely to predict membership in the high-using groups and moderate-decreasing user groups in comparison to low-to-non users. Results also indicated that lower levels of frequency of parent-child communication were more likely to predict membership in the high-users group compared to the low-to-non users. Regarding parent knowledge of marijuana use, trends toward congruence and underestimation of EA marijuana use predicted membership in the high-using and moderate-decreasing groups compared to the low-to-non users. Study results indicate EAs in their early 20s may be more likely to engage in healthy decision-making regarding marijuana use in an environment that includes warm, supportive parent-child relationships where parents are aware of their EAs use without focusing on their EA’s perceptions of risk. (PsycInfo Database Record (c) 2021 APA, all rights reserved).Traditionally, researchers have used time series and multilevel models to analyze intensive longitudinal data. However, these models do not directly address traits and states which conceptualize the stability and variability implicit in longitudinal research, and they do not explicitly take into account measurement error. An alternative to overcome these drawbacks is to consider structural equation models (state-trait SEMs) for longitudinal data that represent traits and states as latent variables. Most of these models are encompassed in the latent state-trait (LST) theory. These state-trait SEMs can be problematic when the number of measurement occasions increases. As they require the data to be in wide format, these models quickly become overparameterized and lead to nonconvergence issues. For these reasons, multilevel versions of state-trait SEMs have been proposed, which require the data in long format. To study how suitable state-trait SEMs are for intensive longitudinal data, we carried out a simulation study. We compared the traditional single level to the multilevel version of three state-trait SEMs. The selected models were the multistate-singletrait (MSST) model, the common and unique trait-state (CUTS) model, and the trait-state-occasion (TSO) model. Furthermore, we also included an empirical application. Our results indicated that the TSO model performed best in both the simulated and the empirical data. To conclude, we highlight the usefulness of state-trait SEMs to study the psychometric properties of the questionnaires used in intensive longitudinal data. Yet, these models still have multiple limitations, some of which might be overcome by extending them to more general frameworks. SP600125 (PsycInfo Database Record (c) 2021 APA, all rights reserved).The validation of the assessment of depression across ethnic groups is critical yet deficient for American Indian (AI) adults. Therefore, we assessed the psychometric properties of the Center for Epidemiological Studies-Depression (CES-D) in AI elders and tested differences in depression constructs between gender. Participants were 817 AI adults (68% women), mean age 73.2 years (SD = 6.1, range 64-95) for women and 72.6 years (SD = 5.3, range 65-90) for men., in the Cerebrovascular Disease and Its Consequences in AIs Study. We evaluated the factor structure of the 20-item and 12-item CES-D and tested measurement invariance between gender. Results demonstrated a poor fit for the 20-item CES-D and partial gender measurement invariance of the 12-item CES-D. AI female elders had significantly higher depression levels than AI male elders on the Depressed Affect subscale, the Somatic Symptoms subscale, and the Well-Being (reverse-coded) subscale. Further replication is needed, and we recommend future psychometric work with the 12-item CES-D with AI elders. (PsycInfo Database Record (c) 2021 APA, all rights reserved).Objective Behavioral health organizations must respond to the needs of increasing numbers of multicultural populations, as the world population continues to diversify. The goal of this research was to develop a measure to assess the multicultural competence of a behavioral health agency using a quick and efficient but comprehensive strategy that utilizes input from multiple staff members. Method The Organizational Multicultural Competence Assessment (OMCA) was developed through a review of existing cultural competence assessment measures and item generation from researchers and policy makers. 469 staff from all departments of a U.S. state-operated and funded behavioral health facilities were asked by the CEO of their agency to complete the 45-item survey. Findings Principal components analysis revealed seven factors that accounted for 64% of the variance in item responses Governance, Policies, and Procedures; Staff Training and Service Delivery; Addressing Stigma and Discrimination; Accessibility of Services; Community Relationships; Quality, Monitoring, and Evaluation; and Human Resource Development. Items within factors showed high internal reliability. Conclusions and Implications for Practice This measure may be used on an ongoing basis as a quality improvement tool to assess an agency or system’s multicultural competence and adherence to the CLAS standards. Future research can investigate the relationship between scores on this measure and organization-level recovery oriented, client health, and person-centered outcomes. (PsycInfo Database Record (c) 2021 APA, all rights reserved).