TITLE:
Data Aggregation: A Proposed Psychometric IPD Meta-Analysis
AUTHORS:
Esther Kaufmann
KEYWORDS:
Data Aggregation, Meta-Analysis, Bias, IPD Meta-Analysis, Psychometric Meta-Analysis, Big Data
JOURNAL NAME:
Open Journal of Statistics,
Vol.8 No.1,
February
1,
2018
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.