BackgroundRelations among and interactions between exposure to armed conflict, alcohol misuse, low socioeconomic status, gender (in)equitable decision-making, and intimate partner violence (IPV) represent serious global health concerns. Our objective was to determine extent of exposure to these variables and test pathways between these indicators of interest.MethodsWe surveyed 605 women aged 13 to 49 who were randomly selected via multistage sampling across three districts in Northeastern Uganda in 2016. We used Mplus 7.4 to estimate a moderated structural equation model of indirect pathways between armed conflict and intimate partner violence for currently partnered women (n = 558) to evaluate the strength of the relationships between the latent factors and determine the goodness-of-fit of the proposed model with the population data.ResultsMost respondents (88.8%) experienced conflict-related violence. The lifetime/ past 12 month prevalence of experiencing intimate partner violence was 65.3%/ 50.9% (psychological) and 59.9%/ 43.8% (physical). One-third (30.7%) of women’s partners reportedly consumed alcohol daily. The relative fit of the structural model was superior (CFI = 0.989; TLI = 0.989). The absolute fit (RMSEA = 0.029) closely matched the population data. The partner and joint decision-making groups significantly differed on the indirect effect through partner alcohol use (a1b1 = 0.209 [0.017: 0.467]).ConclusionsThis study demonstrates that male partner alcohol misuse is associated with exposure to armed conflict and intimate partner violence—a relationship moderated by healthcare decision-making. These findings encourage the extension of integrated alcohol misuse and intimate partner violence policy and emergency humanitarian programming to include exposure to armed conflict and gendered decision-making practices.

This study examined, using data from the National Longitudinal Transition Study–2, the impact of constructs associated with self-determination (i.e., autonomy, self-realization, and psychological empowerment measured while youth were in secondary school) on postschool—(a) employment and payment/benefits, (b) education, (c) independent living, and (d) social engagement—outcomes. Findings suggest that up to 8 years after youth exited school, autonomy, self-realization, and psychological empowerment predict postschool outcomes. Psychological empowerment showed a strong relationship with employment wages and benefits, and autonomy and self-realization contributed to predicting independent living and postsecondary education enrollment. Implications for future research and practice are discussed.

As global mental health research and programming proliferate, research that prioritizes women’s voices and examines marginalized women’s mental health outcomes in relation to exposure to violence at community and relational levels of the socioecological model is needed. In a mixed methods, transnational study, we examined armed conflict exposure, intimate partner violence (IPV), and depressive symptoms among 605 women in Northeastern Uganda. We used analysis of variance to test between groups of women who had experienced no IPV or armed conflict, IPV only, armed conflict only, and both; and linear regression to predict depressive symptoms. We used rapid ethnographic methods with a subsample ( n = 21) to identify problem prioritization; and, to characterize women’s mental health experiences, we conducted follow up in-depth interviews ( n = 15), which we analyzed with grounded theory methods. Thirty percent of the sample met the cut-off for probable major depressive disorder; women exposed to both IPV and armed conflict had significantly higher rates of depression than all other groups. While women attributed psychological symptoms primarily to IPV exposure, both past-year IPV and exposure to armed conflict were significantly associated with depressive symptoms. Women identified socioeconomic neglect as having the most impact and described three interrelated mental health experiences that contribute to thoughts of escape, including escape through suicide. Policy efforts should be interprofessional, and specialists should collaborate to advance multi-pronged interventions and gender-informed implementation strategies for women’s wellbeing. Additional online materials for this article are available on PWQ’s website at http://journals.sagepub.com/doi/suppl/10.1177/0361684319864366

Abstract. The authors discuss limitations of two popular measurement procedures, the Likert scale and conventional pretest-posttest self-report design. Both techniques have limits and yet are often combined, leading to restricted fidelity for measuring change. The authors go on to discuss two innovations in measurement that provide researchers with greater assessment fidelity: Visual analog scales and the retrospective pretest design. Moreover, when used in combination, these innovations measurement techniques provide dramatic increases in the power to detect and quantify change.Keywords. Attitudes, polytomous items, MRG, college students.Resumen. Se discuten las limitaciones de dos procedimientos de medición extremadamente populares; la escala Likert y los diseños pre-post tradicionales. Ambos métodos tienen limitaciones; estos métodos son comúnmente combinados obteniendo como resultado una fidelidad limitada para la medición del cambio. Se discute sobre dos innovaciones en medición que proveen al investigador una mayor fidelidad: escalas visuales análogas y el diseño pre-test retrospectivo. Además, cuando se usan de manera combinada, estas innovaciones en medición proveen notables incrementos en el poder de detección y la cuantificación de cambios.Palabras clave. Actitudes, items politómicos, MRG, estudiantes universitarios.

A simulation based comparative study was designed to compare two alternative approaches to structural equation modeling—generalized structured component analysis (GSCA) with the alternating least squares (ALS) estimator vs. covariance structure analysis (CSA) with the maximum likelihood (ML) estimator or the weighted least squares mean and variance adjusted (WLSMV) estimator—in terms of parameter recovery with ordinal observed variables. The simulated conditions included the number of response categories in observed variables, distribution of ordinal observed variables, sample size, and model misspecification. The simulation outcomes focused on average root mean square error (RMSE) and average relative bias (RB) in parameter estimates. The results indicated that, by and large, GSCA-ALS recovered structural path coefficients more accurately than CSA-ML and CSA-WLSMV in either a correctly or incorrectly specified model, regardless of the number of response categories, observed variable distribution, and sample size. In terms of loadings, CSA-WLSMV outperformed GSCA-ALS and CSA-ML in almost all conditions. Implications and limitations of the current findings are discussed, as well as suggestions for future research.