Tamaño de efecto, potencia de la prueba, Factor de Bayes y meta-análisis en el marco de la crisis de reproducibilidad de las cienciasel caso de las diferencias de proporciones y tablas de contingencia con variables nominales y muestras independientes (Segunda parte)
| dc.creator | D´Angelo, Luis | |
| dc.date | 2023-06-00 | |
| dc.date.accessioned | 2026-05-18T20:34:13Z | |
| dc.description | Fil: D´Angelo, Luis. Instituto Nacional de Parasitología 'Dr. Mario Fatala Chaben. Buenos Aires, Argentina | |
| dc.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.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.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.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. | |
| dc.description | 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. | |
| dc.format | application/pdf | |
| dc.identifier | cuadcimbage_n26_v1_05 | |
| dc.identifier.uri | https://bibliotecadigital.economicas.uba.ar/handle/FCE-RI/8040 | |
| dc.language | spa | |
| dc.publisher | Universidad de Buenos Aires. Facultad de Ciencias Económicas. Instituto de Investigaciones en Estadística y Matemática. Centro de Investigación en Metodología Borrosa Aplicada a la Gestión y Economía | |
| dc.relation.contentWebUrl | https://ojs.economicas.uba.ar/CIMBAGE/article/view/3022/3872 | |
| dc.relation.ispartofseries | Cuad. CIMBAGE - Nro. 26, v. 1 | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights | http://creativecommons.org/licenses/by/2.5/ar/ | |
| dc.source | Cuad. CIMBAGE Nro. 26, v. 1 (2024), p. 77-107 | |
| dc.subject | Factor de Bayes | |
| dc.subject | Meta-análisis | |
| dc.subject | Potencia de la prueba | |
| dc.subject | Proporciones | |
| dc.subject | Tamaño del efecto | |
| dc.title | Tamaño de efecto, potencia de la prueba, Factor de Bayes y meta-análisis en el marco de la crisis de reproducibilidad de las cienciasel caso de las diferencias de proporciones y tablas de contingencia con variables nominales y muestras independientes (Segunda parte) | |
| dc.type | info:eu-repo/semantics/article | |
| dc.type | info:ar-repo/semantics/artículo | |
| dc.type | info:eu-repo/semantics/publishedVersion |
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