Abstract
Individual participant data (IPD) meta-analysis was developed to overcome several meta-analytical pitfalls of classical meta-analysis. One advantage of classical psychometric meta-analysis over IPD meta-analysis is the corrections of the aggregated unit of studies, namely study differences, i.e., artifacts, such as measurement error. Without these corrections on a study level, meta-analysts may assume moderator variables instead of artifacts between studies. The psychometric correction of the aggregation unit of individuals in IPD meta-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 to account for the differences in the aggregation unit of individuals to overcome differences between individuals. We introduce the reader to this approach using the aggregation of lens model studies on individual data as an example, and lay out different application possibilities for the future (e.g., big data analysis). Our suggested psychometric IPD meta-analysis supplements the meta-analysis approaches within the field and is a suitable alternative for future analysis.
Highlights
We present the adaptation of a psychometric approach for individual participant data (IPD) meta-analysis to account for the differences in the aggregation unit of individuals to overcome differences between individuals
We introduced a psychometric IPD meta-analysis, adapting the psychometric Hunter-Schmidt approach instead of the aggregation-unit of studies to the aggregation-units of persons
We note that ambulatory assessment data, studies applying the so-called experience sampling approach, may be a suitable future application of psychometric IPD meta-analysis
Summary
The technology revolution owing to the introduction of computers and Internet impacts all areas, the registration and archiving of data in scientific fields. We talk of the age of “big data” because of the improvement in data gathering, registration, and archiving. Data are often seen to have the same potential as oil did in previous years; the question that is raised is, “Do we really take advantage of our current golden oil products?” In other words, do we really know how to analyze such large datasets? Prior to the technology revolution, large datasets in social science were analyzed, but the analysis effort involved was considerably larger than today. One approach to analyze big data is meta-analysis. The potential of meta-analysis approaches, the so-called individual participant data (IPD) meta-analysis approach supplemented by a psychometric correction, to analyze big data, is evident
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
