Detecting Treatment Effects with Small Samples: The Power of Some Tests under the Randomization Model

Autor/in	Keller, Bryan
Titel	Detecting Treatment Effects with Small Samples: The Power of Some Tests under the Randomization Model
Quelle	In: Psychometrika, 77 (2012) 2, S.324-338 (15 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-3123
DOI	10.1007/s11336-012-9249-5
Schlagwörter	Statistical Analysis; Nonparametric Statistics; Simulation; Sampling; Sample Size + Suchen Sie Ihr Suchwort? Statistische Analyse; Simulation program; Simulationsprogramm
Abstract	Randomization tests are often recommended when parametric assumptions may be violated because they require no distributional or random sampling assumptions in order to be valid. In addition to being exact, a randomization test may also be more powerful than its parametric counterpart. This was demonstrated in a simulation study which examined the conditional power of three nondirectional tests: the randomization "t" test, the Wilcoxon-Mann-Whitney (WMW) test, and the parametric "t" test. When the treatment effect was skewed, with degree of skewness correlated with the size of the effect, the randomization "t" test was systematically more powerful than the parametric "t" test. The relative power of the WMW test under the skewed treatment effect condition depended on the sample size ratio. (As Provided).
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Update	2017/4/10