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• W2132631493 abstract "Pese a que la regresion logistica es una de las tecnicas estadisticas de analisis mas usadas en ciencias sociales no esta carente de ciertas limitaciones. El reducido tamano de la muestra y la presencia de casos perdidos son algunas de las situaciones que han sido identificadas como problematicas para la regresion logistica. En este trabajo hemos comparado la regresion logistica dicotomica y el clasificador simple de Bayes en su habilidad para predecir la tendencia emprendedora manipulando el numero de eventos por variable. Una muestra de estudiantes universitarios (N = 1230) respondio a cinco escalas (motivacion, actitud emprendedora, obstaculos, carencias y preparacion percibida) que fueron utilizadas como variables predictoras de la tendencia emprendedora y a un conjunto de tres preguntas relativas a la tendencia emprendedora que fueron consideradas como variables de respuesta. Nuestros resultados indican que el numero de eventos por variable afecta mas a la regresion logistica en terminos del area bajo la curva ROC comparado con las redes bayesianas. Asi pues, proponemos que las redes bayesianas podrian considerarse como otra alternativa mas, junto a las ya existentes, para superar las debilidades de la regresion logistica en determinadas condiciones de ejecucion. Although logistic regression is one of the most commonly used data analysis techniques in social sciences it is also true that it has some limitations. A reduced sample size and the presence of missing data are some of the problems logistic regression can’t cope with. In this work we compare the success of dichotomic logistic regression model and the Bayes simple classifier to predict entrepreneurship after manipulating the sample size. A sample of university undergraduate students (N = 1230) was asked to fill in five scales (motivation, attitude towards business creation, obstacles, deficiencies and training needs) whose scores were used as predictors and three questions referred to entrepreneurship tendency were considered as outcomes. Our results show that the Receiver Operating Characteristic (ROC) curve is affected by the number of events per variable in both techniques but logistic regression seems to be more vulnerable. We propose to use Bayesian networks as an additional alternative to surpass the weaknesses of logistic regression." @default.
• W2132631493 created "2016-06-24" @default.
• W2132631493 creator A5028676967 @default.
• W2132631493 creator A5043155780 @default.
• W2132631493 date "2011-01-01" @default.
• W2132631493 modified "2023-09-23" @default.
• W2132631493 title "Eventos por Variable en Regresión Logística y Redes Bayesianas para Predecir Actitudes Emprendedoras" @default.
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• W2132631493 doi "https://doi.org/10.17811/rema.16.1.2011.13-34" @default.
• W2132631493 hasPublicationYear "2011" @default.
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