Brooklyn College/CUNY (UNITED STATES)
About this paper:
Appears in: ICERI2018 Proceedings
Publication year: 2018
Page: 20 (abstract only)
ISBN: 978-84-09-05948-5
ISSN: 2340-1095
doi: 10.21125/iceri.2018.1004
Conference name: 11th annual International Conference of Education, Research and Innovation
Dates: 12-14 November, 2018
Location: Seville, Spain
An introductory course in statistics is one of the requirements for many undergraduate degrees. Many students are anxious about this course and failure rates are high. The current study sought to determine whether basic mathematical ability (ranging from calculating percentages to graphical interpretation) predicts performance in an undergraduate psychology statistics course after adjusting for psychological, academic, and demographic variables. To our knowledge, these variables have not been simultaneously investigated.

Student participants (n=460; 78.5% female, 48.3% white, 55.2% non-transfer students) were recruited during five semesters of an undergraduate psychology statistics class at an urban public senior college in the northeast United States. At the beginning of the semester, students answered questions about their demographics (age, gender, year in school, race/ethnicity, transfer status), and specific academic-focused psychological processes and behaviors (self-efficacy/outcome expectancy, help-seeking, procrastination). Students also completed a 30-item objective measure of basic mathematical skills (Math Assessment for College Students, MACS; Rabin et al., in press).

Course performance was evaluated based on the average scores obtained on three in-class exams. To identify differences between patterns of responses based on basic mathematical skills (i.e., MACS score), and other variables, we used discriminant correspondence analysis (DiCA) (Williams, Abdi, French, & Orange, 2010). The overall component structure was statistically significant (based on permutation tests; pperm < .0001), and the first two components explained, respectively, 80.70% (pperm < .0001) and 11.87%, (pperm = .0008) of the variance. Results from component 1 showed that knowledge of basic mathematical concepts as measured by the MACS was the main determinant of course performance (47% contribution on component 1). Students who scored higher on the MACS earned better course grades than students who scored lower on the MACS. Transfer status, year in school, and race/ethnicity together contributed 45% on component 1, with all the other variables contributing the remaining 8%. Specifically, traditional (non-transferred) white students who took the course early in their college career earned better course grades, while high procrastinators earned lower grades than low procrastinators. Component 2 results revealed that what separated students with C-range grades from those with A-range and B-range grades was MACS performance (60% contribution on component 2), with none of the other variables contributing significantly to the difference between these groups. As statistics is important to various academic disciplines and to understanding the scientific method, it is critical to identify variables that predict learning and performance in introductory statistics courses.

Our results suggest that mathematical competency may be the most important roadblock to the successful course completion for some students. We discuss the implications of these novel findings for identifying areas of mathematical deficiency and providing appropriate and timely remediation.
Introductory statistics, basic math skills, math assessment, undergraduate education, course performance, statistics education.