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

Ezekiel's adjusted 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 relative to (a) the sample statistic, and (b) the sample minus the expected value of . Simulation design factors consisted of sample sizes ( = 50, 100, 200, 400), number of predictors (2, 3, 4, 5, 6), and population squared multiple correlations ( = 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 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 . 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 ( = 200 and 400), thus suggesting that researchers using Ezekiel's adjusted 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 has desirable properties and, in addition, these findings bring needed clarity to the statistical literature on Ezekiel's classic estimator.