Integrative data analysis: The simultaneous analysis of multiple data sets.

Abstract

There are both quantitative and methodological techniques that foster the development and maintenance of a cumulative knowledge base within the psychological sciences. Most noteworthy of these techniques is meta-analysis, which allows for the synthesis of summary statistics drawn from multiple studies when the original data are not available. However, when the original data can be obtained from multiple studies, many advantages stem from the statistical analysis of the pooled data. The authors define integrative data analysis (IDA) as the analysis of multiple data sets that have been pooled into one. Although variants of IDA have been incorporated into other scientific disciplines, the use of these techniques is much less evident in psychology. In this article the authors present an overview of IDA as it may be applied within the psychological sciences, discuss the relative advantages and disadvantages of IDA, describe analytic strategies for analyzing pooled individual data, and offer recommendations for the use of IDA in practice.

Adolescent substance use and educational attainment: An integrative data analysis comparing cannabis and alcohol from three Australasian cohorts

“…We integrated participant-level data rather than using the standard meta-analytic approach of combining study-level estimates. This approach increases sample size and statistical precision to investigate less common patterns of substance use (such as frequent use at a young age), provides the opportunity to include a wide range of potential confounding factors, and augments our ability to generalize findings to the region and internationally more realistically than is possible for any individual study (Curran and Hussong, 2009;Hofer and Piccinin, 2009). We build on earlier work which found that adolescent cannabis use was negatively associated with attaining secondary schooland…”

Accelerating Progress: A New Era of Research on Character Development

“…As one potential solution to this challenge, Callina et al (2017, in this special section) discuss the applicability of integrative data analysis (cf. Curran and Hussong 2009) to the articulation of developmental trajectories for specific character strengths. Using data from four studies with independent samples, the team was able to identify shared items for the assessment of hopeful future expectations.…”

Programs for Preventing Depression in Adolescence: Who Benefits and Who Does Not? An Introduction to the Supplemental Issue

“…However, the statistical power of these methods for detecting and studying effect heterogeneity is very limited, and can also lead to fallacious results when we assume that trial-level variables operate in the same was as individual-level moderators (Dagne et al 2016). Recent advances in statistical methods that can be applied to individuallevel data aggregated over two or more studies, referred to as integrative data analysis (Curran and Hussong 2009) or individual patient or participant meta-analysis (Stewart and Parmar 1993), have much greater power to detect effect heterogeneity and characterize its sources (Dagne et al 2016). Such methods have only recently been applied to trials data, and quantitative methods are still being developed.…”