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Multiple Comparison Procedures for Simple One-Way ANOVA with Dependent Data

Published online by Cambridge University Press:  10 April 2014

Guillermo Vallejo Seco ,
Ignacio Menéndez de la Fuente  and
Paula Fernández García
Guillermo Vallejo Seco*
Affiliation:
University of Oviedo
Ignacio Menéndez de la Fuente
Affiliation:
University of Oviedo
Paula Fernández García
Affiliation:
University of Oviedo
*
Correspondence concerning this article should be addressed to Dr. Guillermo Vallejo, Departamento de Psicología.Universidad de Oviedo. Plaza Feijoo, s/n. 33003 OVIEDO.Spain. E-mail: [email protected]
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Abstract

The independence assumption, although reasonable when examining cross-sectional data using single-factor experimental designs, is seldom verified by investigators. A Monte Carlo type simulation experiment was designed to examine the relationship between true Types I and II error probabilities in six multiple comparison procedures. Various aspects, such as patterns of means, types of hypotheses, and degree of dependence of the observations, were taken into account. Results show that, if independence is violated, none of the procedures control a using the error rate per comparison. At the same time, as the correlation increases, so does the per-comparison power.

La asunción de independencia parece un supuesto razonable al examinar los datos de un diseño experimental de grupos al azar. Probablemente debido a ello, esta asunción raramente es verificada por los investigadores. Por todo ello, realizamos un experimento de simulación Monte Carlo por medio del cual se examinan las tasas de error tipo I y tipo II cometidas al utilizar diferentes procedimientos de comparación múltiple, haciendo uso de diferentes tipos de patrones de medias, tipos de hipótesis y grados de dependencia entre las observaciones. Si se viola la independencia, los resultados revelan que ningún procedimiento mantiene controlada la tasa de error por contraste al nivel nominal, al mismo tiempo, conforme se incrementa la correlación en una pequeña cantidad, la potencia por comparación también se incrementa.

Keywords

error rate per comparisonper-pairs powerdependent dataerror tipo Ipotenciacomparación múltipleMonte Carlo
Type
Articles
Information
The Spanish Journal of Psychology , Volume 2 , May 1999 , pp. 55 - 63
Copyright
Copyright © Cambridge University Press 1999

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