Integrative data analysis through coordination of measurement and analysis protocol across independent longitudinal studies.
Replication of research findings across independent longitudinal studies is essential for a cumulative and innovative developmental science. Meta-analysis of longitudinal studies is often limited by the amount of published information on particular research questions, the complexity of longitudinal designs and the sophistication of analyses, and practical limits on full reporting of results. In many cases, cross-study differences in sample composition and measurements impede or lessen the utility of pooled data analysis. A collaborative, coordinated analysis approach can provide a broad foundation for cumulating scientific knowledge by facilitating efficient analysis of multiple studies in ways that maximize comparability of results and permit evaluation of study differences. The goal of such an approach is to maximize opportunities for replication and extension of findings across longitudinal studies through open access to analysis scripts and output for published results, permitting modification, evaluation, and extension of alternative statistical models and application to additional data sets. Drawing on the cognitive aging literature as an example, the authors articulate some of the challenges of meta-analytic and pooled-data approaches and introduce a coordinated analysis approach as an important avenue for maximizing the comparability, replication, and extension of results from longitudinal studies.
Hofer
Scott M
SM
Department of Human Development and Family Sciences, Oregon State University. Department of Psychology, University of Victoria, P.O. Box 3050 STN CSC, Victoria, British Columbia V8W 3P5, Canada. smhofer@uvic.ca
Piccinin
Andrea M
AM
D000375:Aging / Q000502:physiology; D003071:Cognition / Q000502:physiology; D003431:Cross-Cultural Comparison; D005163:Factor Analysis, Statistical; D006801:Humans; D008137:Longitudinal Studies; D015201:Meta-Analysis as Topic; D011594:Psychometrics / Q000379:methods / Q000706:statistics & numerical data; D012107:Research Design; D013223:Statistics as Topic / Q000379:methods