Diep Nguyen

Measurement Invariance Testing with Many Groups: A Comparison of Five Approaches

With the increasing use of international survey data especially in cross-cultural and multinational studies, establishing measurement invariance (MI) across a large number of groups in a study is essential. Testing MI over many groups is methodologically challenging, however. We identified 5 methods for MI testing across many groups (multiple group confirmatory factor analysis, multilevel confirmatory factor analysis, multilevel factor mixture modeling, Bayesian approximate MI testing, and alignment optimization) and explicated the similarities and differences of these approaches in terms of their conceptual models and statistical procedures. A Monte Carlo study was conducted to investigate the efficacy of the 5 methods in detecting measurement noninvariance across many groups using various fit criteria. Generally, the 5 methods showed reasonable performance in identifying the level of invariance if an appropriate fit criterion was used (e.g., Bayesian information criteron with multilevel factor mixture modeling). Finally, general guidelines in selecting an appropriate method are provided.

Comparing the Performance of Approaches for Testing the Homogeneity of Variance Assumption in One-Factor ANOVA Models

Various tests to check the homogeneity of variance assumption have been proposed in the literature, yet there is no consensus as to their robustness when the assumption of normality does not hold. This simulation study evaluated the performance of 14 tests for the homogeneity of variance assumption in one-way ANOVA models in terms of Type I error control and statistical power. Seven factors were manipulated: number of groups, average number of observations per group, pattern of sample sizes in groups, pattern of population variances, maximum variance ratio, population distribution shape, and nominal alpha level for the test of variances. Overall, the Ramsey conditional, O’Brien, Brown–Forsythe, Bootstrap Brown–Forsythe, and Levene with squared deviations tests maintained adequate Type I error control, performing better than the others across all the conditions. The power for each of these five tests was acceptable and the power differences were subtle. Guidelines for selecting a valid test for assessing the tenability of this critical assumption are provided based on average cell size.

Screening for depression among youth with HIV in an integrated care setting

ABSTRACT Youth living with HIV (YLWH) are at risk for depression. Depressive symptoms can impact treatment engagement, health outcomes, and quality of life. Early identification of symptoms can guide treatment planning. This study aimed to identify trends in depressive symptoms for YLWH in a specialty-care clinic and follow-up clinical treatment procedures. An archival review of a clinical database provided depression screening information for a sample of 130 YLWH between 11 and 25 years old in the southeastern United States. Findings indicated that approximately 24% of the sample screened positive for depression-risk. Most commonly endorsed symptoms included fatigue (54.3%) and sleep difficulties (48.5%). Depressive symptoms did not differ significantly by age, gender, race, ethnicity, or sexual orientation. Youth who acquired HIV behaviorally were more likely to endorse the critical item (i.e., self-harm and/or suicidal ideation) than youth who acquired HIV perinatally. Forty-percent of the sample (i.e., 51 youth) had a follow-up treatment plan. YLWH who endorsed the critical item were more likely to receive follow-up action when compared to those who did not endorse the item. Despite limitations of the study, findings have important implications for clinical care and future research.