Bridging a gap in meta-analytical practices: A superior approach for converting standardized beta weights to...

Meta-analysis is crucial to coping with the contemporary landscape of exponential scientific output. Hindering this effort is effect size variety, with standardized beta coefficients, regression weights, elasticities, or partial correlations proven to be nonequivalent to zero-order correlations, despite that these parameters are often aggregated together. Addressing this challenge, we developed two novel approaches that convert standardized betas to correlations, uninformed and informed imputation, and compare their accuracy and bias against the traditional method of Peterson and Brown (2005). In our simulations, we tested matrices from three to 10 variables, finding that Peterson and Brown's technique is inherently biased and less accurate and misestimates error variance. Uninformed and informed imputation were, on average, more accurate and unbiased and, by merging sampling error with imputation error, correctly identify error variance. Due to imputation error, reporting beta weights alone typically destroys 95% to over 99% of the information originally held by a full correlation matrix. For fields that almost exclusively report betas, such as economics, it necessarily hampers them from becoming cumulative sciences. We recommend that all previous uses of Peterson and Brown be reevaluated, future aggregations of standardized beta weights use our provided imputation techniques, and correlation matrices be routinely reported. (PsycInfo Database Record (c) 2026 APA, all rights reserved).