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ERIC Number: ED231882
Record Type: RIE
Publication Date: 1983-Apr
Pages: 33
Abstractor: N/A
Reference Count: 0
Parametric ANCOVA vs. Rank Transform ANCOVA when Assumptions of Conditional Normality and Homoscedasticity Are Violated.
Olejnik, Stephen F.; Algina, James
Parametric analysis of covariance was compared to analysis of covariance with data transformed using ranks. Using a computer simulation approach the two strategies were compared in terms of the proportion of Type I errors made and statistical power when the conditional distribution of errors were: (1) normal and homoscedastic, (2) normal and heteroscedastic, (3) non-normal and homoscedastic, and (4) non-normal and heteroscedastic. The results indicated that parametric ANCOVA was robust to violations of either normality or homoscedasticity. However when both assumptions were violated the observed alpha levels underestimated the nominal alpha level when sample sizes were small and alpha=.05. Rank ANCOVA led to a slightly liberal test of the hypothesis when the covariate was non-normal and the errors were heteroscedastic. Practical significant power differences favoring the rank ANCOVA procedure were observed with moderate sample sizes and skewed conditional error distributions. (Author)
Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers: Computer Simulation; Heteroscedasticity (Statistics); Homoscedasticity (Statistics); Parametric Analysis; Robustness; Type I Errors
Note: Paper presented at the Annual Meeting of the American Educational Research Association (67th, Montreal, Quebec, April 11-15, 1983). Table 3 contains small print.