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Computer‐intensive correlational analysis: Bootstrap and approximate randomization techniques
Author(s) -
Rasmussen Jeffrey Lee
Publication year - 1989
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1111/j.2044-8317.1989.tb01118.x
Subject(s) - type i and type ii errors , parametric statistics , sample size determination , randomization , statistics , mathematics , statistical hypothesis testing , nonparametric statistics , statistical analysis , test (biology) , type (biology) , econometrics , randomized controlled trial , medicine , ecology , surgery , biology , paleontology
Computer‐intensive statistical techniques have been suggested as alternatives to standard parametric analysis due to their freedom from normal‐theory assumptions. Two such techniques that may be used for correlational analysis are bootstrap and approximate randomization tests. These techniques were compared with the parametric Pearson's r under composite‐normal conditions in which the test of significance of Pearson's r is known to possess overly liberal Type I error rates. Results indicated that the approximate randomization test had Type I error rates that closely followed the parametric approach. The bootstrap, however, showed good control of the Type I error rates, except on small sample sizes.