title: A comparative study on entrepreneurial attitudes modeled with logistic regression and Bayes nets.
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.
authors.affiliation: Facultad de Psicología, Universidad de Almería, Ctra. Sacramento S/N, La Cañada de San Urbane, 04120 - Almería, Spain. jpuga@ual.es
authors.forename: Jorge
authors.initials: J
authors.lastname: López Puga
authors.forename: Juan
authors.initials: J
authors.lastname: García García
mesh_terms: D000293:Adolescent; D000328:Adult; D001499:Bayes Theorem; D016520:Entrepreneurship / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D012372:ROC Curve; D011795:Surveys and Questionnaires; D055815:Young Adult