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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

View abstract on PubMed

Summary
This summary is machine-generated.

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.

Keywords:
agingcoordinated data analysisdata synthesisintegrative data analysislongitudinal methodologymeta-analysispersonalityreplication

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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.