According to the Chronicle of Higher Education (2001), 15 percent of entering freshmen believe that there is a good chance they will change their college major and 8 percent are undecided. To gain insight into the criteria that students use to select a major, a model of the student decision making process was developed using the Analytic Hierarchy Process (AHP). This model predicted student's first choice major with 88 percent accuracy for sophomores and seniors. An analysis of the criteria revealed judgement inconsistencies, particularly for accounting, finance, and decision science majors. Not surprisingly, sophomores were more inconsistent in their decision making than were seniors. It was also determined that students clustered the majors into two separate groups, viewing accounting, finance and decision science majors differently than marketing and management majors While it is important for students to understand where their strengths and interests lie, are other criteria also involved in the…decision process students use in choosing a major?
Analytics has become a new source of competitive advantage for many corporations. Today’s work force therefore must be cognizant of its power and value to effectively perform their jobs. In this paper, we define the appropriate skill level and breadth of knowledge required for business school graduates to be successful. An undergraduate course of study in analytics is proposed for students with average to above-average analytical skills. Implementation guidelines are also addressed to ensure a successful program.
The one sample t-test is compared with the Wilcoxon Signed-Rank test for identical data sets representing various Likert scales. An empirical approach is used with simulated data. Comparisons are based on observed error rates for 27,850 data sets. Recommendations are provided.
The need for analysts with expertise in big data software is becoming more apparent in today's society. Unfortunately, the demand for these analysts far exceeds the number available. A potential way to combat this shortage is to identify the software taught in colleges or universities. This article will examine four data analysis software-Excel add-ins, SPSS, SAS, and R-and we will outline the cost, training, and statistical methods/tests/uses for each of these software. It will further explain implications for universities and future students.
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