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The analysis of repeated measurements: Univariate tests, multivariate tests, or both?
Author(s) -
Keselman H. J.,
Keselman Joanne C.,
Lix Lisa M.
Publication year - 1995
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.2044-8317.1995.tb01066.x
Subject(s) - univariate , multivariate statistics , type i and type ii errors , statistics , multivariate analysis , repeated measures design , covariance , statistic , mathematics , multivariate analysis of variance , econometrics , test (biology) , test statistic , degrees of freedom (physics and chemistry) , statistical hypothesis testing , paleontology , physics , quantum mechanics , biology
One strategy which has been recommended for examining effects in repeated measures designs combines a degrees of freedom (d.f.)‐adjusted univariate F test and a multivariate test. The results of a simulation study for a groups × trials repeated measures design are presented, and demonstrate that for balanced designs this combined strategy rarely provided superior Type I error control to that afforded by uniformly adopting either a univariate or multivariate test. For unbalanced repeated measures designs, all three strategies typically resulted in very conservative or very liberal Type I error rates when covariance matrices and group sizes were positively and negatively paired, respectively. For such designs, the approximate d.f. Welch‐James (James, 1951, 1954; Welch, 1947, 1951) multivariate statistic developed by Johansen (1980) is preferable to any of the other strategies investigated in this paper. Power comparisons also favour the use of the Welch‐James test.