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A Comparative Study on Entrepreneurial Attitudes Modeled with Logistic Regression and Bayes Nets
Published online by Cambridge University Press: 10 January 2013
Abstract
Entrepreneurship research is receiving increasing attention in our context, as entrepreneurs are key social agents involved in economic development. We compare the success of the dichotomic logistic regression model and the Bayes simple classifier to predict entrepreneurship, after manipulating the percentage of missing data and the level of categorization in predictors. A sample of undergraduate university students (N = 1230) completed five scales (motivation, attitude towards business creation, obstacles, deficiencies, and training needs) and we found that each of them predicted different aspects of the tendency to business creation. Additionally, our results show that the receiver operating characteristic (ROC) curve is affected by the rate of missing data in both techniques, but logistic regression seems to be more vulnerable when faced with missing data, whereas Bayes nets underperform slightly when categorization has been manipulated. Our study sheds light on the potential entrepreneur profile and we propose to use Bayesian networks as an additional alternative to overcome the weaknesses of logistic regression when missing data are present in applied research.
El estudio de las actitudes emprendedoras está cobrando especial interés en nuestro contexto dada la trascendencia que tienen los emprendedores como agentes sociales dinamizadores del desarrollo económico. Hemos comparado la regresión logística binaria y el clasificador simple de Bayes en su habilidad para predecir la tendencia emprendedora manipulando número de casos perdidos y el nivel de categorización de los predictores del modelo. Una muestra de estudiantes universitarios (N = 1230) respondió a cinco escalas (motivación, actitud emprendedora, obstáculos, carencias y preparación percibida) y se observó que cada una de estas escalas predecía diferentes dimensiones de la tendencia a crear empresas. Por otro lado, la categorización de los predictores beneficia ligeramente a la regresión logística mientras que la presencia de casos perdidos lo hace sobre las redes bayesianas en términos del área bajo una curva ROC. Nuestros resultados arrojan luz sobre las características del emprendedor potencial y proponemos que las redes bayesianas se consideren como otra alternativa más, junto a las ya existentes, para superar las debilidades derivadas de la presencia de casos perdidos en situaciones aplicadas.
Keywords
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- Research Article
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- Copyright © Cambridge University Press 2012
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