Accuracy and Utility of the AUDIT-C with Adolescent Girls and Young Women (AGYW) Who Engage in HIV Risk Behaviors in South Africa.

Hazardous drinking is a risk factor associated with sexual risk, gender-based violence, and HIV transmission in South Africa. Consequently, sound and appropriate measurement of drinking behavior is critical to determining what constitutes hazardous drinking. Many research studies use internal consistency estimates as the determining factor in psychometric assessment; however, deeper assessments are needed to best define a measurement tool. Rasch methodology was used to evaluate a shorter version of the Alcohol Use Disorders Identification Test, the AUDIT-C, in a sample of adolescent girls and young women (AGYW) who use alcohol and other drugs in South Africa (n =100). Investigations of operational response range, item fit, sensitivity, and response option usage provide a richer picture of AUDIT-C functioning than internal consistency alone in women who are vulnerable to hazardous drinking and therefore at risk of HIV. Analyses indicate that the AUDIT-C does not adequately measure this specialized population, and that more validation is needed to determine if the AUDIT-C should continue to be used in HIV prevention intervention studies focused on vulnerable adolescent girls and young women.

Synthesizing single-case studies: a Monte Carlo examination of a three-level meta-analytic model.

Numerous ways to meta-analyze single-case data have been proposed in the literature; however, consensus has not been reached on the most appropriate method. One method that has been proposed involves multilevel modeling. For this study, we used Monte Carlo methods to examine the appropriateness of Van den Noortgate and Onghena's (2008) raw-data multilevel modeling approach for the meta-analysis of single-case data. Specifically, we examined the fixed effects (e.g., the overall average treatment effect) and the variance components (e.g., the between-person within-study variance in the treatment effect) in a three-level multilevel model (repeated observations nested within individuals, nested within studies). More specifically, bias of the point estimates, confidence interval coverage rates, and interval widths were examined as a function of the number of primary studies per meta-analysis, the modal number of participants per primary study, the modal series length per primary study, the level of autocorrelation, and the variances of the error terms. The degree to which the findings of this study are supportive of using Van den Noortgate and Onghena's (2008) raw-data multilevel modeling approach to meta-analyzing single-case data depends on the particular parameter of interest. Estimates of the average treatment effect tended to be unbiased and produced confidence intervals that tended to overcover, but did come close to the nominal level as Level-3 sample size increased. Conversely, estimates of the variance in the treatment effect tended to be biased, and the confidence intervals for those estimates were inaccurate.

Estimating individual treatment effects from multiple-baseline data: a Monte Carlo study of multilevel-modeling approaches.

While conducting intervention research, researchers and practitioners are often interested in how the intervention functions not only at the group level, but also at the individual level. One way to examine individual treatment effects is through multiple-baseline studies analyzed with multilevel modeling. This analysis allows for the construction of confidence intervals, which are strongly recommended in the reporting guidelines of the American Psychological Association. The purpose of this study was to examine the accuracy of confidence intervals of individual treatment effects obtained from multilevel modeling of multiple-baseline data. Monte Carlo methods were used to examine performance across conditions varying in the number of participants, the number of observations per participant, and the dependency of errors. The accuracy of the confidence intervals depended on the method used, with the greatest accuracy being obtained when multilevel modeling was coupled with the Kenward-Roger method of estimating degrees of freedom.