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Project INTEGRATE: An Integrative Study of Brief Alcohol Interventions for College Students
- Project INTEGRATE: An Integrative Study of Brief Alcohol Interventions for College Students
- Mun, Eun-Young; de la Torre, Jimmy; Atkins, David C.; White, Helene R.; Ray, Anne E.; Kim, Su-Young; Jiao, Yang; Clarke, Nickeisha; Huo, Yan; Larimer, Mary E.; Huh, David
- Ewha Authors
- SCOPUS Author ID
- Issue Date
- Journal Title
- PSYCHOLOGY OF ADDICTIVE BEHAVIORS
- 0893-164X; 1939-1501
- vol. 29, no. 1, pp. 34 - 48
- integrative data analysis; meta-analysis; brief motivational interventions; alcohol interventions; college students
- EDUCATIONAL PUBLISHING FOUNDATION-AMERICAN PSYCHOLOGICAL ASSOC
- SSCI; SCOPUS
- This article provides an overview of a study that synthesizes multiple, independently collected alcohol intervention studies for college students into a single, multisite longitudinal data set. This research embraced innovative analytic strategies (i.e., integrative data analysis or meta-analysis using individual participant-level data), with the overall goal of answering research questions that are difficult to address in individual studies such as moderation analysis, while providing a built-in replication for the reported efficacy of brief motivational interventions for college students. Data were pooled across 24 intervention studies, of which 21 included a comparison or control condition and all included one or more treatment conditions. This yielded a sample of 12,630 participants (42% men; 58% first-year or incoming students). The majority of the sample identified as White (74%), with 12% Asian, 7% Hispanic, 2% Black, and 5% other/mixed ethnic groups. Participants were assessed 2 or more times from baseline up to 12 months, with varying assessment schedules across studies. This article describes how we combined individual participant-level data from multiple studies, and discusses the steps taken to develop commensurate measures across studies via harmonization and newly developed Markov chain Monte Carlo (MCMC) algorithms for 2-parameter logistic item response theory models and a generalized partial credit model. This innovative approach has intriguing promises, but significant barriers exist. To lower the barriers, there is a need to increase overlap in measures and timing of follow-up assessments across studies, better define treatment and control groups, and improve transparency and documentation in future single intervention studies.
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