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Psicothema was founded in Asturias (northern Spain) in 1989, and is published jointly by the Psychology Faculty of the University of Oviedo and the Psychological Association of the Principality of Asturias (Colegio Oficial de Psicología del Principado de Asturias).
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Psicothema, 2009. Vol. Vol. 21 (nº 1). 141-151




Calculating the main alternatives to null-hypothesis-significance testing in between-subject experimental designs

Nekane Balluerka Lasa, Ana Isabel Vergara Iraeta and Jaume Arnau Gras*

Universidad del País Vasco and * Universidad de Barcelona

The present article deals with the controversy about null hypothesis significance testing (NHST) that currently exists in psychological research. The main criticisms and counter-criticisms posed by its detractors and supporters are presented. Alternatives have been proposed to replace or complement the NHST, as recommended by the Task Force on Statistical Inference of the APA, and these are explained. Furthermore, the procedure for calculating these alternatives using SPSS statistical software in a two-way between-factor design is demonstrated. Thus, an attempt is made to provide the applied researcher with resources that make it possible to analyse and interpret the results of any research study using a group of indicators that lends a high level of validity to the statistical inference performed.

Cálculo de las principales alternativas a la prueba de significación de la hipótesis nula en diseños experimentales intersujetos. El presente artículo se centra en la controversia existente actualmente en torno a la prueba de significación de la hipótesis nula en la investigación psicológica. Se exponen las principales críticas y contra-críticas que plantean sus detractores y defensores, así como las alternativas que, contando con el apoyo de la Task Force on Statistical Inference de la APA, han sido propuestas para sustituirla o complementarla. Además, se muestra la forma en la que pueden calcularse, mediante el programa estadístico SPSS, tales índices alternativos en un diseño factorial aleatorio. Con ello se pretende dotar al investigador aplicado de una serie de recursos que le permitan analizar e interpretar los resultados de cualquier estudio manejando un conjunto de indicadores que proporcionan un alto grado de validez a la inferencia realizada.

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