Integrative Data Analysis: The Simultaneous Analysis of Multiple Data Sets

By P. Curran, Andrea M. Hussong. Psychological methods, 2009

803 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/dc28cf149abe0f79ff08c2ebda811705315e79d8

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Citations in the corpus

Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and math (2020, neutral): “This increased power is particularly important for subgroups that are numerically small in any single study; our data on underrepresented students are notable in this regard (73, 74).”
Receptive vocabulary differences in monolingual and bilingual children (2009, background): “In aggregated datasets, sample heterogeneity increases and if the hypothesized effect persists, the effect is deemed robust and issues of biased sampling are diminished (Curran & Hussong, 2009).”
The Hedonic Character of Nostalgia: An Integrative Data Analysis (2020, methodology): “This meta-analytic technique combines the raw data from a set of studies into a single, larger dataset on which multilevel models are subsequently fitted (Curran & Hussong, 2009; see also Hussong et al., 2010; Lorenz et al., 1997; McArdle et al., 2000).”
Small Sample Size Solutions (2020, methodology): “…or Item Response Theory – IRT) to handle different measures (at least some of which have to exhibit measurement invariance across studies), as described in the literature on integrative data analysis (Curran & Hussong, 2009; Curran et al., 2008; McArdle, Grimm, Hamagami, Bowles, & Meredith, 2009).”
Measurement Invariance Conventions and Reporting: The State of the Art and Future Directions for Psychological Research (2016, background): “Could groups be analyzed separately and then aggregated as if across separate studies (e.g., Curran & Hussong, 2009)?”