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A Review of Nonparametric Alternatives To Analysis of Covariance

Author

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  • Stephen F. Olejnik

    (University of Florida)

  • James Algina

    (University of Florida)

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.

Suggested Citation

  • Stephen F. Olejnik & James Algina, 1985. "A Review of Nonparametric Alternatives To Analysis of Covariance," Evaluation Review, , vol. 9(1), pages 51-83, February.
    • Handle: RePEc:sae:evarev:v:9:y:1985:i:1:p:51-83
    DOI: 10.1177/0193841X8500900104
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