The DOI matches its Crossref record; 115 of the 115 references it cites were found in a registry or an index.
Integrative data analysis: The simultaneous analysis of multiple data sets
Open accessAbstract
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.
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“First, IPD metaanalyses provide substantially more statistical power. 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).”
From Active learning narrows achievement gaps for underrepresented students in undergraduate science, technology, engineering, and math (2020) · cites this paper as [73]
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“For multigroup analysis, scores of the two samples were equated, using methods described by [42].”
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“Could groups be analyzed separately and then aggregated as if across separate studies (e.g., Curran & Hussong, 2009)?”
BackgroundTheobald, E. J., Hill, M. J., Tran, E., Agrawal, S., Arroyo, E. N., Behling, S., Chambwe, N., Cintrón, D. L., Cooper, J. D., Dunster, G., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1916903117 · Cited by 1,387
“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).”
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Metadata
- Authors
- Curran, P. J., & Hussong, A. M.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Venue
- Psychological Methods
- Published
- 2009
- Volume
- 14
- Issue
- 2
- Pages
- 81-100
- ISSN
- 1082-989X (ISSN-L)
- 1939-1463
- PMC
- PMC2777640
- OpenAlex
- W2072327382
- Cited by
- 867
- References
- 112
- Citations by year
- 2026: 61 · 2025: 51 · 2024: 95 · 2023: 73 · 2022: 95 · 2021: 94 · 2020: 80 · 2019: 61 · 2018: 51 · 2017: 35 · 2016: 45 · 2015: 43 · 2014: 23 · 2013: 17 · 2012: 21
- FWCI
- 17.24
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Psychological Methods (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- UNC Libraries
- Funders
- National Institute on Drug Abuse (DA013148, DA15398)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Psychology Research and Bibliometrics
- Keywords
- integrative data analysis, pooled analysis, multiple data sets, meta-analysis, analytical strategies, evidence synthesis
- MeSH terms
- Data Collection, Data Interpretation, Statistical, Humans, Research, Research Design, Meta-Analysis as Topic
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