Effect size, power analysis, Bayes factor and meta-analysis in the framework of science's replication crisis. the case of proportions and contingency tables with nominal variables and independent samples: Second part

This paper is a continuation of the article on effect size for difference of independent means, focusing now on differences of proportions and contingency tables with nominal variables and independent samples. Statistical research on proportions plays a fundamental role in a wide range of scientifi...

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Autor principal: D'Angelo, Luis
Formato: Artículo publishedVersion
Lenguaje:Español
Publicado: CIMBAGE - IADCOM - Facultad de Ciencias Económicas - Universidad de Buenos Aires 2024
Materias:
TAMAÁ‘O DEL EFECTO
PROPORCIONES
POTENCIA DE LA PRUEBA
FACTOR DE BAYES
META-ANÁLISIS
EFFECT SIZE
PROPORTIONS
POWER ANALYSIS
BAYES FACTOR
META-ANALYSIS
Acceso en línea:https://ojs.economicas.uba.ar/CIMBAGE/article/view/3022
https://repositoriouba.sisbi.uba.ar/gsdl/cgi-bin/library.cgi?a=d&c=cimbage&d=3022_oai
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Repositorio Digital de la Universidad de Buenos Aires (UBA) de Universidad de Buenos Aires
id I28-R145-3022_oai
record_format dspace
spelling I28-R145-3022_oai2025-02-11 D'Angelo, Luis 2024-05-29 This paper is a continuation of the article on effect size for difference of independent means, focusing now on differences of proportions and contingency tables with nominal variables and independent samples. Statistical research on proportions plays a fundamental role in a wide range of scientific and social fields by providing valuable insights into the distribution and incidence of phenomena in a specific population. Beyond assessing the statistical significance of the evidence, it is crucial to consider the practical or clinical relevance of the results, understanding the importance of effect size. In the case of proportions, we find a wide variety of effect size coefficients available, such as the Phi coefficient, Cramer's V, Cohen's W, Ben-Shachar's Fei, Cohen's h, relative risk, odds ratio, among others. This diversity can generate challenges when selecting the most appropriate statistic to analyze the data. Therefore, this paper aims to address this issue in detail, exploring the characteristics and applications of each statistic, and offering guidance on their selection according to the context and objetives of the research. Este trabajo es la continuación del artículo sobre tamaño de efecto para diferencia de medias independientes, centrándose ahora en las diferencias de proporciones y tablas de contingencia con variables nominales y muestras independientes. La investigación estadística sobre proporciones desempeña un papel fundamental en una amplia gama de ámbitos científicos y sociales al proporcionar una valiosa comprensión sobre la distribución y la incidencia de fenómenos en una población específica. Más allá de evaluar la significación estadística de las pruebas es crucial considerar la relevancia práctica o clínica de los resultados, entendiendo la importancia del tamaño de efecto. En el caso de las proporciones, nos encontramos con una amplia variedad de estadísticos de tamaño de efecto disponibles, como el coeficiente Phi, V de Cramer, W de Cohen, Fei de Ben-Shachar, h de Cohen, riesgo relativo, odds ratio, entre otros. Esta diversidad puede generar desafíos al momento de seleccionar el estadístico más adecuado para analizar los datos. Por lo tanto, este trabajo se propone abordar esta problemática en detalle, explorando las características y aplicaciones de cada estadístico, y ofreciendo orientación sobre su selección según el contexto y los objetivos de la investigación. application/pdf text/html https://ojs.economicas.uba.ar/CIMBAGE/article/view/3022 10.56503/CIMBAGE/Vol.1/Nro.26(2024)/3022 spa CIMBAGE - IADCOM - Facultad de Ciencias Económicas - Universidad de Buenos Aires https://ojs.economicas.uba.ar/CIMBAGE/article/view/3022/3872 https://ojs.economicas.uba.ar/CIMBAGE/article/view/3022/3877 Derechos de autor 2024 Cuadernos del CIMBAGE Cuadernos del CIMBAGE; Vol. 1 No. 26 (2024): Cuadernos del CIMBAGE N°26 (June 2024); 77-107 Cuadernos del CIMBAGE; Vol. 1 Núm. 26 (2024): Cuadernos del CIMBAGE N°26 (Junio 2024); 77-107 1669-1830 1666-5112 TAMAÁ‘O DEL EFECTO PROPORCIONES POTENCIA DE LA PRUEBA FACTOR DE BAYES META-ANÁLISIS EFFECT SIZE PROPORTIONS POWER ANALYSIS BAYES FACTOR META-ANALYSIS Effect size, power analysis, Bayes factor and meta-analysis in the framework of science's replication crisis. the case of proportions and contingency tables with nominal variables and independent samples: Second part Tamaño de efecto, potencia de la prueba, factor de Bayes y meta-análisis en el marco de la crisis de reproducibilidad de la ciencia. el caso de las diferencias de proporciones y tablas de contingencia con variables nominales y muestras independientes: Segunda parte info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://repositoriouba.sisbi.uba.ar/gsdl/cgi-bin/library.cgi?a=d&c=cimbage&d=3022_oai
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-145
collection Repositorio Digital de la Universidad de Buenos Aires (UBA)
language Español
orig_language_str_mv spa
topic TAMAÁ‘O DEL EFECTO
PROPORCIONES
POTENCIA DE LA PRUEBA
FACTOR DE BAYES
META-ANÁLISIS
EFFECT SIZE
PROPORTIONS
POWER ANALYSIS
BAYES FACTOR
META-ANALYSIS
spellingShingle TAMAÁ‘O DEL EFECTO
PROPORCIONES
POTENCIA DE LA PRUEBA
FACTOR DE BAYES
META-ANÁLISIS
EFFECT SIZE
PROPORTIONS
POWER ANALYSIS
BAYES FACTOR
META-ANALYSIS
D'Angelo, Luis
Effect size, power analysis, Bayes factor and meta-analysis in the framework of science's replication crisis. the case of proportions and contingency tables with nominal variables and independent samples: Second part
topic_facet TAMAÁ‘O DEL EFECTO
PROPORCIONES
POTENCIA DE LA PRUEBA
FACTOR DE BAYES
META-ANÁLISIS
EFFECT SIZE
PROPORTIONS
POWER ANALYSIS
BAYES FACTOR
META-ANALYSIS
description This paper is a continuation of the article on effect size for difference of independent means, focusing now on differences of proportions and contingency tables with nominal variables and independent samples. Statistical research on proportions plays a fundamental role in a wide range of scientific and social fields by providing valuable insights into the distribution and incidence of phenomena in a specific population. Beyond assessing the statistical significance of the evidence, it is crucial to consider the practical or clinical relevance of the results, understanding the importance of effect size. In the case of proportions, we find a wide variety of effect size coefficients available, such as the Phi coefficient, Cramer's V, Cohen's W, Ben-Shachar's Fei, Cohen's h, relative risk, odds ratio, among others. This diversity can generate challenges when selecting the most appropriate statistic to analyze the data. Therefore, this paper aims to address this issue in detail, exploring the characteristics and applications of each statistic, and offering guidance on their selection according to the context and objetives of the research.
format Artículo
publishedVersion
author D'Angelo, Luis
author_facet D'Angelo, Luis
author_sort D'Angelo, Luis
title Effect size, power analysis, Bayes factor and meta-analysis in the framework of science's replication crisis. the case of proportions and contingency tables with nominal variables and independent samples: Second part
title_short Effect size, power analysis, Bayes factor and meta-analysis in the framework of science's replication crisis. the case of proportions and contingency tables with nominal variables and independent samples: Second part
title_full Effect size, power analysis, Bayes factor and meta-analysis in the framework of science's replication crisis. the case of proportions and contingency tables with nominal variables and independent samples: Second part
title_fullStr Effect size, power analysis, Bayes factor and meta-analysis in the framework of science's replication crisis. the case of proportions and contingency tables with nominal variables and independent samples: Second part
title_full_unstemmed Effect size, power analysis, Bayes factor and meta-analysis in the framework of science's replication crisis. the case of proportions and contingency tables with nominal variables and independent samples: Second part
title_sort effect size, power analysis, bayes factor and meta-analysis in the framework of science's replication crisis. the case of proportions and contingency tables with nominal variables and independent samples: second part
publisher CIMBAGE - IADCOM - Facultad de Ciencias Económicas - Universidad de Buenos Aires
publishDate 2024
url https://ojs.economicas.uba.ar/CIMBAGE/article/view/3022
https://repositoriouba.sisbi.uba.ar/gsdl/cgi-bin/library.cgi?a=d&c=cimbage&d=3022_oai
work_keys_str_mv AT dangeloluis effectsizepoweranalysisbayesfactorandmetaanalysisintheframeworkofsciencesreplicationcrisisthecaseofproportionsandcontingencytableswithnominalvariablesandindependentsamplessecondpart
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