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Effective self-report screening tools for emerging adults are understudied. The present study examined the latent structure of the Depression, Anxiety, and Stress Scales (DASS-21) with U.S. undergraduates. Data were collected from 1,413 undergraduates surveyed online. Three models were tested: (a) a one-factor model, (b) the original correlated three-factor model, and (c) a bifactor model that included a general negative affectivity factor and three specific factors of depression, anxiety, and stress. The bifactor model with three specific orthogonal factors yielded the best fit. All items loaded onto the general negative affectivity factor. This study provides an important evaluation of alternative models of the latent structure of the DASS among U.S. undergraduates, with results supporting it as an assessment of general distress for emerging adults.

The purpose of the present study is to compare performances of mixture modeling approaches (i.e., one-step approach, three-step maximum-likelihood approach, threestep BCH approach, and LTB approach) based on diverse sample size conditions. To carry out this research, two simulation studies were conducted with two different models, a latent class model with three predictor variables and a latent class model with one distal outcome variable. For the simulation, data were generated under the conditions of different sample sizes (100, 200, 300, 500, 1,000), entropy (0.6, 0.7, 0.8, 0.9), and the variance of a distal outcome (homoscedasticity, heteroscedasticity). For evaluation criteria, parameter estimates bias, standard error bias, mean squared error, and coverage were used. Results demonstrate that the three-step approaches produced more stable and better estimations than the other approaches even with a small sample size of 100. This research differs from previous studies in the sense that various models were used to compare the approaches and smaller sample size conditions were used. Furthermore, the results supporting the superiority of the threestep approaches even in poorly manipulated conditions indicate the advantage of these approaches.

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The present study aims to compare the robustness under various conditions of latent class analysis mixture modeling approaches that deal with auxiliary distal outcomes. Monte Carlo simulations were employed to test the performance of four approaches recommended by previous simulation studies: maximum likelihood (ML) assuming homoskedasticity (ML_E), ML assuming heteroskedasticity (ML_U), BCH, and LTB. For all investigated simulation conditions, the BCH approach yielded the most unbiased estimates of class-specific distal outcome means. This study has implications for researchers looking to apply recommended latent class analysis mixture modeling approaches in that nonnormality, which has been not fully considered in previous studies, was taken into account to address the distributional form of distal outcomes.