Ezekiel's classic estimator of the population squared multiple correlation coefficient: Monte Carlo-based extensions...

Ezekiel's adjusted R² is widely used in linear regression analysis. The present study examined the statistical properties of Ezekiel's measure through a series of Monte Carlo simulations. Specifically, we examined the bias and root mean squared error (RMSE) of Ezekiel's adjusted R² relative to (a) the sample R² statistic, and (b) the sample R² minus the expected value of R². Simulation design factors consisted of sample sizes (N = 50, 100, 200, 400), number of predictors (2, 3, 4, 5, 6), and population squared multiple correlations (ρ² = 0, .10, .25, .40, .60). Factorially crossing these design factors resulted in 100 simulation conditions. All populations were normal/Gaussian, and for each condition, we drew 10,000 Monte Carlo samples. Regarding systematic variation (bias), results indicated that with few exceptions, Ezekiel's adjusted R² demonstrated the lowest bias. Regarding unsystematic variation (RMSE), the performance of Ezekiel's measure was comparable to the other statistics, suggesting that the bias-variance tradeoff is minimal for Ezekiel's adjusted R². Additional findings indicated that sample size-to-predictor ratios of 66.67 and greater were associated with low bias and that ratios of this magnitude were accompanied by large sample sizes (N = 200 and 400), thus suggesting that researchers using Ezekiel's adjusted R² should aim for sample sizes of 200 or greater in order to minimize bias when estimating the population squared multiple correlation coefficient. Overall, these findings indicate that Ezekiel's adjusted R² has desirable properties and, in addition, these findings bring needed clarity to the statistical literature on Ezekiel's classic estimator.