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Combining individual participant and aggregated data in a meta-analysis with correlational studies.

作者信息

Pigott Terri, Williams Ryan, Polanin Joshua

机构信息

Loyola University Chicago, School of Education, 820 North Michigan Avenue, Chicago, IL, 60611, USA.

出版信息

Res Synth Methods. 2012 Dec;3(4):257-68. doi: 10.1002/jrsm.1051. Epub 2012 Aug 26.


DOI:10.1002/jrsm.1051
PMID:26053420
Abstract

This paper presents methods for combining individual participant data (IPD) with aggregated study level data (AD) in a meta-analysis of correlational studies. Although medical researchers have employed IPD in a wide range of studies, only a single example exists in the social sciences. New policies at the National Science Foundation requiring grantees to submit data archiving plans may increase social scientists' access to individual level data that could be combined with traditional meta-analysis. The methods presented here extend prior work on IPD to meta-analyses using correlational studies. The examples presented illustrate the synthesis of publicly available national datasets in education with aggregated study data from a meta-analysis examining the correlation of socioeconomic status measures and academic achievement. The major benefit of the inclusion of the individual level is that both within-study and between-study interactions among moderators of effect size can be estimated. Given the potential growth in data archives in the social sciences, we should see a corresponding increase in the ability to synthesize IPD and AD in a single meta-analysis, leading to a more complete understanding of how within-study and between-study moderators relate to effect size. Copyright © 2012 John Wiley & Sons, Ltd.

摘要

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