Data Aggregation: A Proposed Psychometric IPD Meta-Analysis

作     者：Esther Kaufmann 

作者机构：Institute of Education University of Zurich Zurich Switzerland 

出 版 物：《Open Journal of Statistics》 (统计学期刊（英文）)

年 卷 期：2018年第8卷第1期

页      面：38-48页

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

主　　题：Data Aggregation Meta-Analysis Bias IPD Meta-Analysis Psychometric Meta-Analysis Big Data 

摘      要：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.