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A Taxonomy of Data Synthesis
Emorie D Beck1, Emily C Willroth2, Julia A M Delius3
1University of California, Davis, USA.
Personality & Social Psychology Bulletin
|June 24, 2026
Summary
Researchers explored six methods for individual participant meta-analysis to synthesize findings from multiple studies. Openness reliably predicted crystallized abilities across diverse samples, offering guidance for future research synthesis in social sciences.
Area of Science:
- Psychological Science
- Social Science Research Methods
Background:
- Improving credibility in social sciences necessitates robust multi-study and multi-sample research synthesis.
- Individual participant meta-analysis offers advanced synthesis but lacks a standardized framework and comparative analyses of its parameterizations.
Purpose of the Study:
- To provide a taxonomy and overview of six common parameterizations for individual participant meta-analysis.
- To empirically compare these parameterizations using longitudinal data on personality traits and crystallized abilities.
Main Methods:
- Developed a taxonomy categorizing six individual participant meta-analysis parameterizations based on features like sample-specific parameters and model complexity.
- Applied these parameterizations to a large dataset (N=26,205) from 11 longitudinal studies to examine personality-crystallized ability associations.
Main Results:
- Openness emerged as a consistent and robust predictor of crystallized abilities across all analyzed samples.
- Generally, parameterizations yielded consistent model estimates, though some exceptions were noted, highlighting the importance of method selection.
Conclusions:
- The study provides a framework for understanding and selecting appropriate individual participant meta-analysis parameterizations.
- Recommendations are offered for researchers based on study goals, data availability, and desired model features to enhance research synthesis and credibility.
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