<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-05-28T13:21:29Z</responseDate><request verb="GetRecord" identifier="oai:riuma.uma.es:10630/40153" metadataPrefix="qdc">https://riuma.uma.es/rest/oai/request</request><GetRecord><record><header><identifier>oai:riuma.uma.es:10630/40153</identifier><datestamp>2026-02-03T10:49:57Z</datestamp><setSpec>com_10630_2254</setSpec><setSpec>col_10630_37953</setSpec></header><metadata><qdc:qualifieddc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:doc="http://www.lyncode.com/xoai" xmlns:qdc="http://dspace.org/qualifieddc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://purl.org/dc/elements/1.1/ http://dublincore.org/schemas/xmls/qdc/2006/01/06/dc.xsd http://purl.org/dc/terms/ http://dublincore.org/schemas/xmls/qdc/2006/01/06/dcterms.xsd http://dspace.org/qualifieddc/ http://www.ukoln.ac.uk/metadata/dcmi/xmlschema/qualifieddc.xsd">
   <dc:title>When to use Bootstrap-F in One-Way Repeated Measures ANOVA: Type I Error and Power.</dc:title>
   <dc:creator>Blanca-Mena, María José</dc:creator>
   <dc:creator>Bono Cabré, Roser</dc:creator>
   <dc:creator>Arnau, Jaume</dc:creator>
   <dc:creator>García-Castro, F. Javier</dc:creator>
   <dc:creator>Alarcón-Postigo, Rafael</dc:creator>
   <dc:creator>Vallejo, Guillermo</dc:creator>
   <dc:subject>Psicología - Metodología</dc:subject>
   <dc:subject>Análisis de varianza</dc:subject>
   <dcterms:abstract>Background: With repeated measures, the traditional ANOVA F-statistic requires fulfillment of normality and sphericity. Bootstrap-F (B-F) has been proposed as a procedure for dealing with violation of these assumptions when conducting a one-way repeated measures ANOVA. However, evidence regarding its robustness and power is limited. Our aim is to extend knowledge about the behavior of B-F with a wider range of conditions. Method: A simulation study was performed, manipulating the number of repeated measures, sample sizes, epsilon values, and distribution shape. Results: B-F may become conservative with higher values of epsilon, and liberal under extreme violation of both normality and sphericity and small sample sizes. In these cases, B-F may be used with a more stringent alpha level (.025). The results also show that power is affected by sphericity: the lower the epsilon value, the larger the sample size required to ensure adequate power. Conclusions: B-F is robust under non-normality and non-sphericity with sample sizes larger than 20-25.</dcterms:abstract>
   <dcterms:dateAccepted>2025-10-09T11:40:39Z</dcterms:dateAccepted>
   <dcterms:available>2025-10-09T11:40:39Z</dcterms:available>
   <dcterms:created>2025-10-09T11:40:39Z</dcterms:created>
   <dcterms:issued>2025</dcterms:issued>
   <dc:type>journal article</dc:type>
   <dc:identifier>Blanca, M. J., Bono, R., Arnau, J., Alarcón, R., García-Castro, F. J., &amp; Vallejo, G. (2025). When to use Bootstrap-F in One-Way Repeated Measures ANOVA: Type I Error and Power. Psicothema, 37(3), 12-22. https://doi.org/10.70478/psicothema.2025.37.20</dc:identifier>
   <dc:identifier>https://hdl.handle.net/10630/40153</dc:identifier>
   <dc:identifier>10.70478/psicothema.2025.37.20</dc:identifier>
   <dc:language>eng</dc:language>
   <dc:rights>http://creativecommons.org/licenses/by-nc-nd/4.0/</dc:rights>
   <dc:rights>open access</dc:rights>
   <dc:rights>Attribution-NonCommercial-NoDerivatives 4.0 Internacional</dc:rights>
   <dc:publisher>Grupo Editorial de Psicofundación</dc:publisher>
</qdc:qualifieddc>
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