Integrative Data Analysis for Research in Developmental Psychopathology

Abstract

Researchers from many disciplines have increasingly called for changes in research practices to be more transparent and rigorous. One of the changes prominently discussed is the utilization of quantitative research synthesis from multiple single studies, which we broadly refer to as integrative data analysis (IDA). The present chapter discusses how IDA can be particularly helpful for research in developmental psychopathology. We provide a broad overview of evidence‐based quantitative approaches, covering different types of data, different analytical models, and complex research synthesis methods. We highlight the promise of utilizing individual participant‐level data from multiple single studies or networks of single studies for analyzing multiple related parameters (or multiple end points) within the framework of quantitative research synthesis. We provide several data examples to illustrate some of these methods. Through the use of innovative strategies for IDA, we can strengthen our research practices and expand our knowledge base cumulatively.

Do drinking motives distinguish extreme drinking college students from their peers?

“…IDA studies pool raw participant-level data from multiple studies and analyze them as a single data set, and can provide many of the same benefits of multi-site trials at a fraction of the cost if study-level heterogeneity can be properly accounted (see Hussong, Curran, & Bauer, 2013; Mun et al, 2015; Mun, Jiao, & Xie, 2016 for detailed discussions). Project INTEGRATE includes data from 24 independent trials at U.S. colleges.…”

A Structural Equation Modeling Approach to Meta-analytic Mediation Analysis Using Individual Participant Data: Testing Protective Behavioral Strategies as a Mediator of Brief Motivational Intervention Effects on Alcohol-Related Problems

“…The mediator variable, PBS, was measured using five different scales across the original studies, which were subsequently harmonized and made commensurate by using a generalized partial credit model (Muraki, 1992), which is an extension of the hierarchical two-parameter logistic item response theory (2-PL IRT) model that we reported for alcohol-related problems (Huo et al, 2015). The measurement work to establish PBS trait scores can be found in Mun et al (2015Mun et al ( , 2016. With respect to the motivating data, studies 2, 8a, 8b, 8c, and 9 used the 10-item Protective Behavioral Strategies (PBS; American College Health Association, 2001) measure; studies 16, 18, and 21 used the 15-item Protective Behavioral Strategies Scale (PBSS; Martens et al, 2005); and studies 12 and 22 used the sevenitem Drinking Restraining Strategies (DRS; Wood et al, 2007) measure.…”

“…With respect to the motivating data, studies 2, 8a, 8b, 8c, 9, 16, and 21 used the Rutgers Alcohol Problem Index (RAPI; White & Labouvie, 1989); studies 8a, 8b, 8c, 9, 12, 16, and 22 used the Young Adult Alcohol Problems Screening Test (YAAPST; Hurlbut & Sher, 1992); study 12 also used the Alcohol Dependence Scale (Skinner & Allen, 1982;Skinner & Horn, 1984); study 18 used the Brief Young Adult Alcohol Consequences Questionnaire (BYAACQ; Kahler et al, 2005); and study 21 used the Alcohol Use Disorders Identification Test (AUDIT; Saunders et al, 1993). For readers interested in the technical details regarding how the measures of PBS and alcohol problems used in the motivating data were made commensurate, the harmonization work is discussed extensively in earlier reports (Huo et al, 2015;Mun et al, 2015Mun et al, , 2016Mun et al, , 2019.…”

Is motivational interviewing effective at reducing alcohol misuse in young adults? A critical review of Foxcroft et al. (2014).

“…To understand how between-study differences are modeled in meta-analysis, one must also appreciate the difference between fixed-effects and random-effects models, the two most common models for metaanalysis (see also Mun, Jiao, & Xie, in press for a review). Under the fixed-effects model, we assume there is one true effect size θ underlying all studies and thus any differences in observed effect sizes are due to sampling error $s_i^2$ , which varies from study to study.…”