.png)
139 – Inference and Variance Estimation with Complex Survey Data
Effect Size Indices for Artificially Dichotomized Variables Measured with Error: An Empirical Investigation of Accuracy and Precision
Isaac Li
University of South Florida
Patricia Rodriguez de Gil
University of South Florida
Jeanine Romano
University of South Florida
Aarti P. Bellara
University of South Florida
George MacDonald
Harold Holmes
University of South Florida
Patrice Rasmussen
University of South Florida
Yi-Hsin Chen
University of South Florida
Jeffrey D. Kromrey
University of South Florida
Monte Carlo methods were used to investigate the accuracy and precision of effect size indices in estimating what the standardized mean difference from a 2 X 2 sample table of dichotomized variables would have been had the data not been dichotomized. Normally distributed, continuous data were generated for two groups and the continuous variable was dichotomized at specified cut points. The factors manipulated in the simulation study included overall sample size (n1 + n2 = 30, 60, 120, 240), reliability levels (.5, .7, .8, .9, 1), population effect size (0, .2, .5, .8), continuous score cut point for dichotomization (.10, .25, .40, .50, .70), and population variance ratio (1:1, 1:2, 1:4). For each sample generated (100,000 replications), each of seven proposed effect size indices was calculated. Both the statistical bias and the RMSE were computed across the set of replications. Although the sample standardized mean difference became substantially biased in the presence of measurement error, the performance of the seven indices was not notably affected. Results were interpreted in terms of recommendations for estimating effect sizes with dichotomized variables.