A Review of Nonparametric Alternatives To Analysis of Covariance

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.