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Data Aggregation: A Proposed Psychometric IPD Meta-Analysis

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Document pages: 11 pages

Abstract: Individual participant data (IPD) meta-analysis wasdeveloped to overcome several meta-analytical pitfalls of classicalmeta-analysis. One advantage of classical psychometric meta-analysis over IPDmeta-analysis is the corrections of the aggregated unit of studies, namelystudy differences, i.e., artifacts,such as measurement error. Without these corrections on a study level, meta-analystsmay assume moderator variables instead of artifacts between studies. Thepsychometric correction of the aggregation unit of individuals in IPDmeta-analysis has been neglected by IPD meta-analysts thus far. In this paper,we present the adaptation of a psychometric approach for IPD meta-analysis toaccount for the differences in the aggregation unit of individuals to overcomedifferences between individuals. We introduce the reader to this approach usingthe aggregation of lens model studies on individual data as an example, and layout different application possibilities for the future (e.g., big dataanalysis). Our suggested psychometric IPD meta-analysis supplements themeta-analysis approaches within the field and is a suitable alternative forfuture analysis.

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