Parametric ANCOVA vs. Rank Transform ANCOVA when Assumptions of Conditional Normality and Homoscedasticity Are Violated.

Autor/inn/en	Olejnik, Stephen F.; Algina, James
Titel	Parametric ANCOVA vs. Rank Transform ANCOVA when Assumptions of Conditional Normality and Homoscedasticity Are Violated.
Quelle	(1983), (33 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Analysis of Covariance; Control Groups; Data Collection; Error of Measurement; Power (Statistics); Pretests Posttests; Research Design; Sample Size; Sampling + Suchen Sie Ihr Suchwort? Data capture; Datensammlung; Messfehler; Forschungsdesign
Abstract	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)
Erfasst von	ERIC (Education Resources Information Center), Washington, DC