Randomization tests as alternative analysis methods for behavior-analytic data

Andrew R. Craig, Wayne W. Fisher

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

Randomization statistics offer alternatives to many of the statistical methods commonly used in behavior analysis and the psychological sciences, more generally. These methods are more flexible than conventional parametric and nonparametric statistical techniques in that they make no assumptions about the underlying distribution of outcome variables, are relatively robust when applied to small-n data sets, and are generally applicable to between-groups, within-subjects, mixed, and single-case research designs. In the present article, we first will provide a historical overview of randomization methods. Next, we will discuss the properties of randomization statistics that may make them particularly well suited for analysis of behavior-analytic data. We will introduce readers to the major assumptions that undergird randomization methods, as well as some practical and computational considerations for their application. Finally, we will demonstrate how randomization statistics may be calculated for mixed and single-case research designs. Throughout, we will direct readers toward resources that they may find useful in developing randomization tests for their own data.

Original languageEnglish (US)
Pages (from-to)309-328
Number of pages20
JournalJournal of the experimental analysis of behavior
Volume111
Issue number2
DOIs
StatePublished - Mar 2019

Keywords

  • autocorrelation
  • between-groups research
  • data analysis
  • nonparametric statistics
  • randomization
  • single-case research
  • within-subjects research

ASJC Scopus subject areas

  • Experimental and Cognitive Psychology
  • Behavioral Neuroscience

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