The Superiority of Simple Alternatives to Regression for Social Science Predictions

Abstract

Some simple, nonoptimized coefficients (e.g., correlation weights, equal weights) were pitted against regression in extensive prediction competitions. After drawing calibration samples from large supersets of real and synthetic data, the researchers observed which set of sample-derived coefficients made the best predictions when applied back to the superset. When adjusted R from the calibration sample was < .6, correlation weights were typically superior to regression coefficients, even if the sample contained 100 observations per predictor; unit weights were likewise superior to all methods if adjusted R was < .4. Correlation weights were generally the best method. It was concluded that regression is rarely useful for prediction in most social science contexts.