This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Olejnik, S. F. Articles by Algina, J. Search for Related Content PubMed Articles by Olejnik, S. F. Articles by Algina, J. Social Bookmarking                   What's this?

A Review of Nonparametric Alternatives To Analysis of Covariance

University of Florida

James Algina

University of Florida

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.

CiteULike    Connotea    Del.icio.us    Digg    Reddit    Technorati    What's this?