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Project INTEGRATE: An Integrative Study of Brief Alcohol Interventions for College Students

Title
Project INTEGRATE: An Integrative Study of Brief Alcohol Interventions for College Students
Authors
Mun, Eun-Youngde la Torre, JimmyAtkins, David C.White, Helene R.Ray, Anne E.Kim, Su-YoungJiao, YangClarke, NickeishaHuo, YanLarimer, Mary E.Huh, David
Ewha Authors
김수영
SCOPUS Author ID
김수영scopus
Issue Date
2015
Journal Title
PSYCHOLOGY OF ADDICTIVE BEHAVIORS
ISSN
0893-164XJCR Link1939-1501JCR Link
Citation
vol. 29, no. 1, pp. 34 - 48
Keywords
integrative data analysismeta-analysisbrief motivational interventionsalcohol interventionscollege students
Publisher
EDUCATIONAL PUBLISHING FOUNDATION-AMERICAN PSYCHOLOGICAL ASSOC
Indexed
SSCI; SCOPUS WOS
Abstract
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.
DOI
10.1037/adb0000047
Appears in Collections:
사회과학대학 > 심리학전공 > Journal papers
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