A Guide to Reanalyzing Data From Psychology Articles for Verification, Meta-Analysis, and Reuse

Abstract

Reanalysis of data from published research in personality and social psychology offers many ways to learn more from the data. Reanalysis is crucial in verifying that published results are free from error and bias and robust to alternative ways of analyzing the data, while meta-analysis and secondary analysis empowers analysts to further investigate interesting patterns in the data. In this perspective, I offer useful guidance on different types of reanalysis: Verification analysis (checks of computational or analytic reproducibility, robustness, or check analyses), meta-analysis (including systematic reviews, individual participant data meta-analysis, and meta-research), and secondary analysis (consideration of novel research questions). I discuss issues in obtaining the data, data ownership, co-authorship, involvement of data collectors, and failures of sharing data in a Findable, Accessible, Interoperable, and Reusable (FAIR) manner. I discuss how reanalysts gain a good understanding of the data via codebooks, metadata, and syntax (computer code) and data collector involvement. I discuss challenges in explorative secondary data analysis to avoid overfitting and consider issues related to ethics screening, preregistration, potential misconduct, and privacy. I conclude that while reanalyzing data responsibly can be challenging, it ultimately advances our understanding of personality and social behavior.