Ipek Bozkurt
The purpose of this study is to develop and execute a framework for evaluating and selecting statistical software for a graduate engineering course. The analytical hierarchy process (AHP) is chosen as the method to objectively formalize this decision-making process using multiple criteria and multiple alternatives. The criteria selected are Cost, Comprehensiveness, Tutorials, Ease of Installation, Compatibility, and Industry Demand. Five software alternatives are analyzed: IBM SPSS, Minitab, RStudio, Stata, and SAS. Subject Matter Expert opinion is used for the pairwise comparisons of criteria, and literature-based analysis is used as a methodology to obtain pairwise comparison results for the alternatives. The results offer an evidence-based decision for software selection, reducing subjectivity and improving decision-making efficacy in academic settings. The evaluation found IBM SPSS (29.3%) and RStudio (28.4%) to be the top choices for statistical software, indicating a preference for tools that balance cost-effectiveness with comprehensive functionality. Lower scores for Minitab (19.7%), Stata (12.3%), and SAS (10.3%) suggest these alternatives may be less favored due to limited versatility or higher costs.