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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.

Configurations of means denying treatment effectiveness are presented for a multiple group time-series design. The configurations imply a sequence of null hypotheses. Failure to reject all of these hypotheses means the treatment effect is not supported. Rejection of any one of the hypotheses leads to tests of more detailed hypotheses. The rejection of these hypotheses can provide support for a treatment effect. Since both univariate and multivariate test criteria can be used to test the hypotheses, the procedure can be applied to data sets that have fewer subjects than occasions of measurement.

A complete introduction to discriminant analysis--extensively revised, expanded, and updated. This Second Edition of the classic book, Applied Discriminant Analysis, reflects and references current usage with its new title, Applied MANOVA and Discriminant Analysis