The Scree Test and the Number of Factors: a Dynamic Graphics Approach

Autores
Ledesma, Ruben Daniel; Valero Mora, Pedro; Macbeth, Guillermo Eduardo
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Exploratory Factor Analysis and Principal Component Analysis are two data analysis methods that are commonly used in psychological research. When applying these techniques, it is important to determine how many factors to retain. This decision is sometimes based on a visual inspection of the Scree plot. However, the Scree plot may at times be ambiguous and open to interpretation. This paper aims to explore a number of graphical and computational improvements to the Scree plot in order to make it more valid and informative. These enhancements are based on dynamic and interactive data visualization tools, and range from adding Parallel Analysis results to "linking" the Scree plot with other graphics, such as factor-loadings plots. To illustrate our proposed improvements, we introduce and describe an example based on real data on which a principal component analysis is appropriate. We hope to provide better graphical tools to help researchers determine the number of factors to retain.
Fil: Ledesma, Ruben Daniel. Universidad Nacional de Mar del Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Valero Mora, Pedro. Universidad de Valencia; España
Fil: Macbeth, Guillermo Eduardo. Universidad Nacional de Entre Ríos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Factor Analysis
Scree Test
Data Visualization
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/44516
Acceder
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network_name_str CONICET Digital (CONICET)
spelling The Scree Test and the Number of Factors: a Dynamic Graphics ApproachLedesma, Ruben DanielValero Mora, PedroMacbeth, Guillermo EduardoFactor AnalysisScree TestData Visualizationhttps://purl.org/becyt/ford/5.1https://purl.org/becyt/ford/5Exploratory Factor Analysis and Principal Component Analysis are two data analysis methods that are commonly used in psychological research. When applying these techniques, it is important to determine how many factors to retain. This decision is sometimes based on a visual inspection of the Scree plot. However, the Scree plot may at times be ambiguous and open to interpretation. This paper aims to explore a number of graphical and computational improvements to the Scree plot in order to make it more valid and informative. These enhancements are based on dynamic and interactive data visualization tools, and range from adding Parallel Analysis results to "linking" the Scree plot with other graphics, such as factor-loadings plots. To illustrate our proposed improvements, we introduce and describe an example based on real data on which a principal component analysis is appropriate. We hope to provide better graphical tools to help researchers determine the number of factors to retain.Fil: Ledesma, Ruben Daniel. Universidad Nacional de Mar del Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Valero Mora, Pedro. Universidad de Valencia; EspañaFil: Macbeth, Guillermo Eduardo. Universidad Nacional de Entre Ríos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaCambridge University Press2015-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/44516Ledesma, Ruben Daniel; Valero Mora, Pedro; Macbeth, Guillermo Eduardo; The Scree Test and the Number of Factors: a Dynamic Graphics Approach; Cambridge University Press; The Spanish Journal of Psychology; 18; 3-2015; 1-101988-2904CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1017/sjp.2015.13info:eu-repo/semantics/altIdentifier/url/https://www.cambridge.org/core/journals/spanish-journal-of-psychology/article/scree-test-and-the-number-of-factors-a-dynamic-graphics-approach/FD59EBE07263C51BCD8742A1060DD7D4info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:32:04Zoai:ri.conicet.gov.ar:11336/44516instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 14:32:04.596CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv The Scree Test and the Number of Factors: a Dynamic Graphics Approach
title The Scree Test and the Number of Factors: a Dynamic Graphics Approach
spellingShingle The Scree Test and the Number of Factors: a Dynamic Graphics Approach
Ledesma, Ruben Daniel
Factor Analysis
Scree Test
Data Visualization
title_short The Scree Test and the Number of Factors: a Dynamic Graphics Approach
title_full The Scree Test and the Number of Factors: a Dynamic Graphics Approach
title_fullStr The Scree Test and the Number of Factors: a Dynamic Graphics Approach
title_full_unstemmed The Scree Test and the Number of Factors: a Dynamic Graphics Approach
title_sort The Scree Test and the Number of Factors: a Dynamic Graphics Approach
dc.creator.none.fl_str_mv Ledesma, Ruben Daniel
Valero Mora, Pedro
Macbeth, Guillermo Eduardo
author Ledesma, Ruben Daniel
author_facet Ledesma, Ruben Daniel
Valero Mora, Pedro
Macbeth, Guillermo Eduardo
author_role author
author2 Valero Mora, Pedro
Macbeth, Guillermo Eduardo
author2_role author
author
dc.subject.none.fl_str_mv Factor Analysis
Scree Test
Data Visualization
topic Factor Analysis
Scree Test
Data Visualization
purl_subject.fl_str_mv https://purl.org/becyt/ford/5.1
https://purl.org/becyt/ford/5
dc.description.none.fl_txt_mv Exploratory Factor Analysis and Principal Component Analysis are two data analysis methods that are commonly used in psychological research. When applying these techniques, it is important to determine how many factors to retain. This decision is sometimes based on a visual inspection of the Scree plot. However, the Scree plot may at times be ambiguous and open to interpretation. This paper aims to explore a number of graphical and computational improvements to the Scree plot in order to make it more valid and informative. These enhancements are based on dynamic and interactive data visualization tools, and range from adding Parallel Analysis results to "linking" the Scree plot with other graphics, such as factor-loadings plots. To illustrate our proposed improvements, we introduce and describe an example based on real data on which a principal component analysis is appropriate. We hope to provide better graphical tools to help researchers determine the number of factors to retain.
Fil: Ledesma, Ruben Daniel. Universidad Nacional de Mar del Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Valero Mora, Pedro. Universidad de Valencia; España
Fil: Macbeth, Guillermo Eduardo. Universidad Nacional de Entre Ríos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description Exploratory Factor Analysis and Principal Component Analysis are two data analysis methods that are commonly used in psychological research. When applying these techniques, it is important to determine how many factors to retain. This decision is sometimes based on a visual inspection of the Scree plot. However, the Scree plot may at times be ambiguous and open to interpretation. This paper aims to explore a number of graphical and computational improvements to the Scree plot in order to make it more valid and informative. These enhancements are based on dynamic and interactive data visualization tools, and range from adding Parallel Analysis results to "linking" the Scree plot with other graphics, such as factor-loadings plots. To illustrate our proposed improvements, we introduce and describe an example based on real data on which a principal component analysis is appropriate. We hope to provide better graphical tools to help researchers determine the number of factors to retain.
publishDate 2015
dc.date.none.fl_str_mv 2015-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/44516
Ledesma, Ruben Daniel; Valero Mora, Pedro; Macbeth, Guillermo Eduardo; The Scree Test and the Number of Factors: a Dynamic Graphics Approach; Cambridge University Press; The Spanish Journal of Psychology; 18; 3-2015; 1-10
1988-2904
CONICET Digital
CONICET
url http://hdl.handle.net/11336/44516
identifier_str_mv Ledesma, Ruben Daniel; Valero Mora, Pedro; Macbeth, Guillermo Eduardo; The Scree Test and the Number of Factors: a Dynamic Graphics Approach; Cambridge University Press; The Spanish Journal of Psychology; 18; 3-2015; 1-10
1988-2904
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1017/sjp.2015.13
info:eu-repo/semantics/altIdentifier/url/https://www.cambridge.org/core/journals/spanish-journal-of-psychology/article/scree-test-and-the-number-of-factors-a-dynamic-graphics-approach/FD59EBE07263C51BCD8742A1060DD7D4
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Cambridge University Press
publisher.none.fl_str_mv Cambridge University Press
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.221938

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