Teaching binary logistic regression modeling in an introductory business analytics course. (13th August 2022)

• Record Type: Journal Article
• Authors: Hoang, Viet‐Ngu; Watson, Justin
• Is Part Of: Decision sciences journal of innovative education. Volume 20:Number 4(2022)
• Page Start: 201
• Page End: 211
• Publication Date: 2022-08-13
• Subjects: business analytics -- classification modeling -- binary logistic regression -- linear regression; Business education -- Periodicals; 658.00711
• DOI: 10.1111/dsji.12274
• Languages: English
• ISSNs: 1540-4595

Abstract

Abstract: There is an increasing demand to introduce Introductory Business Analytics (IBA) courses into undergraduate business education. Many real‐world business contexts require predictive analytics to understand the determinants of a dichotomous outcome; hence, IBA courses should include binary logistic regression analysis. This article provides our reflective discussions on the design of learning activities and assessments to assist business students in learning binary logistic regression in an IBA course. Data on student engagement and learning outcomes are used to shed light on the impacts of teaching logistic regression on student learning and experience. Notably, students opt to focus their assessment work more on logistic regression than on multiple regression analysis, showing the potential attraction of students toward binary logistic regression analysis. We also observed several challenges, mainly related to the use of Excel, that require special attention from instructors.