A Review of Nonparametric Alternatives To Analysis of Covariance

Stephen F. Olejnik and James Algina
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Stephen F. Olejnik: University of Florida
James Algina: University of Florida

Evaluation Review, 1985, vol. 9, issue 1, 51-83

Abstract: Five distribution-free alternatives to parametric analysis of covariance are presented and demonstrated using a specific data example. The results of simulation studies investigating these procedures regarding their respective Type I error rate under a null condition and their statistical power are also reviewed. The results indicate that the nonparametric procedures have appropriate Type I error rates only for those situations in which para metric A NCO VA is robust to violations of data assumptions. In terms of statistical power, nonparametric alternatives to parametric ANCOVA provide a considerable power advan tage only for situations in which extreme violations of assumptions have occurred and the linear relationship between measures is weak.

Date: 1985
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Persistent link: https://EconPapers.repec.org/RePEc:sae:evarev:v:9:y:1985:i:1:p:51-83

DOI: 10.1177/0193841X8500900104

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