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

Both quantitative and methodological techniques exist 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 are much less evident in psychology.In this paper the authors present an overview of IDA as it may be applied within the psychological sciences; a discussion of the relative advantages and disadvantages of IDA; a description of analytic strategies for analyzing pooled individual data; and offer recommendations for the use of IDA in practice.The cornerstone of any field of scientific inquiry is the pursuit of a body of cumulative knowledge, yet the psychological sciences have often fallen short of this goal (e.g., Gans, 1992;Hunter & Schmidt, 1996;Meehl, 1978;Schmidt, 1996).This is not for want of trying.Both quantitative and methodological techniques have been developed to help build a cumulative knowledge base.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 (e.g., Cooper, in press;Glass, 1976;Rothstein, Sutton, & Borenstein, 2005;Smith & Glass, 1977).One of the original motivations for meta-analysis was that these techniques would further support the creation of a cumulative knowledge within the social sciences, particularly in psychology (e.g., Hunter & Schmidt, 1996;Schmidt, 1984).There is no doubt that meta-analysis has substantially advanced our science toward this goal.Because the focus of meta-analysis is on the synthesis of summary statistics drawn from multiple studies, this approach is ideal when the original individual data used in prior analyses is inaccessible or no longer exists.However, as we discuss in greater detail below, there are many advantages to fitting models directly to the original raw data instead of synthesizing the relevant summary statistics when the original individual data are available for analysis (e.g., Berlin, Santanna, Schmid, Szczech, & Feldman, 2002;Lambert, Sutton