SAS procedures for approximate randomization tests

Approximate randomization tests are alternatives to conventional parametric statistical methods used when the normality and homoscedasticity assumptions are violated This article presents an SAS program that tests the equality of two means using an approximate randomization test This program can serve as a template for testing other hypotheses, which is illustrated by modifications to test the significance of a correlation coefficient or the equality of more than two means.     

SPSS procedures for approximate randomization tests

Randomization tests are valid alternatives to parametric tests like the t test and analysis of variance when the normality or random sampling assumptions of these tests are violated. Three SPSS programs are listed and described that will conduct approximate randomization tests for testing the null hypotheses that two or more means or distributions are the same or that two variables are independent (i.e., uncorrelated or “randomly associated”). The programs will work on both desktop and mainframe versions of SPSS. Although the SPSS programs are slower on desktop machines than software designed explicitly for randomization tests, these programs bring randomization tests into the reach of researchers who prefer the SPSS computing environment for data analysis.     

An R package for single-case randomization tests

Randomization tests are nonparametric statistical tests that obtain their validity by computationally mimicking the random assignment procedure that was used in the design phase of a study. Because randomization tests do not rely on a random sampling assumption, they can provide a better alternative than parametric statistical tests for analyzing data from single-case designs. In this article, an R package is described for use in designing single-case phase (AB, ABA, and ABAB) and alternation (completely randomized, alternating treatments, and randomized block) experiments, as well as for conducting statistical analyses on data gathered by means of such designs. The R code is presented in a step-by-step way, which at the same time clarifies the rationale behind single-case randomization tests.