Testing for adverse impact when sample size is small

被引:27
作者
Collins, Michael W. [1 ]
Morris, Scott B. [1 ]
机构
[1] IIT, Inst Psychol, Chicago, IL 60616 USA
关键词
selection; adverse impact; significance testing;
D O I
10.1037/0021-9010.93.2.463
中图分类号
B849 [应用心理学];
学科分类号
040203 ;
摘要
Adverse impact evaluations often call for evidence that the disparity between groups in selection rates is statistically significant, and practitioners must choose which test statistic to apply in this situation. To identify the most effective testing procedure, the authors compared several alternate test statistics in terms of Type I error rates and power, focusing on situations with small samples. Significance testing was found to be of limited value because of low power for all tests. Among the alternate test statistics, the widely-used Z-test on the difference between two proportions performed reasonably well, except when sample size was extremely small. A test suggested by G. J. G. Upton (1982) provided slightly better control of Type I error under some conditions but generally produced results similar to the Z-test. Use of the Fisher Exact Test and Yates's continuity-corrected chi-square test are not recommended because of overly conservative Type I error rates and substantially lower power than the Z-test.
引用
收藏
页码:463 / 471
页数:9
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