Journal Article

  • We examined the attitudes of about 2200 students enrolled in 101 sections of post-secondary introductory statistics service courses located across the United States. Using the Survey of Attitudes Toward Statistics-36, we assessed students’ attitudes when they entered and left their courses, as well as changes in attitudes across their courses. Results showed that, on average, students entered these courses with neutral (Affect, Difficulty), positive (Cognitive Competence, Value, Interest), and very positive (Effort) attitudes. Their attitudes either stayed about the same (Affect, Cognitive Competence, Difficulty) or decreased (Value, Interest, Effort). These results help us understand the current impact of introductory statistics instruction in U.S. institutions.

  • People forget what they do not use. But attitudes “stick.” Our article emphasizes the importance of students’ attitudes toward statistics. We examine 15 surveys that purport to assess these attitudes and then describe the Survey of Attitudes Toward Statistics, a commonly used attitude survey. We present our conceptual model of Students’ Attitudes Toward Statistics (SATS-M), which is congruent with Eccles and colleagues’ Expectancy-Value Theory (Eccles’ EVT), as well as others. The SATS-M includes three broad constructs that impact Statistics Course Outcomes: Student Characteristics, Previous Achievement-Related Experiences, and Statistics Attitudes. We briefly describe Eccles’ EVT and other theories that support our SATS-M. We relate findings from research using the SATS to our model and end with implications for statistics education.

  • Students’ attitudes toward statistics were investigated using a mixed-methods approach including a discovery-oriented qualitative methodology among 684 undergraduate students across business, criminal justice, and psychology majors where at least one course in statistics was required. Students were asked about their attitudes toward statistics and the reasons for their attitudes. Five categories resulted for those with positive and negative attitudes and were separated on the basis of discipline. Approximately 63% of students indicated a positive attitude toward statistics. Business majors were most positive and were more likely to believe statistics would be used in their future career. Multiple methodological approaches have now provided data on the various domains of attitudes toward statistics and those implications are discussed.

  • Teachers’ attitudes towards statistics can have a significant effect on their own statistical training, their teaching of statistics, and the future attitudes of their students. The influence of attitudes in teaching statistics in different contexts was previously studied in the work of Estrada et al. (2004, 2010a, 2010b) and Martins et al. (2011). This work is part of a broader study of Portuguese education teachers and statistics. In the current paper, we use a qualitative content analysis of survey responses from Portuguese first-stage in-service teachers, focusing on nine open-ended items extracted from the Escala de Actitudes hacia la Estadística de Estrada (Estrada, 2002). These responses allow us to investigate teachers’ attitudes towards statistics, and their reasons and motivations for holding these attitudes.

  • Although statistics education research has focused on students’ learning and conceptual
    understanding of statistics, researchers have only recently begun investigating students’
    perceptions of statistics. The term perception describes the overlap between cognitive and noncognitive
    factors. In this mixed-methods study, undergraduate students provided their perceptions
    of statistics and completed the Survey of Students’ Attitudes Toward Statistics-36 (SATS-36). The
    qualitative data suggest students had basic knowledge of what the word statistics meant, but with
    varying depths of understanding and conceptualization of statistics. Quantitative analysis also
    examined the relationship between students’ perceptions of statistics and attitudes toward
    statistics. We found no significant difference in mean pre- or post-SATS scores across
    conceptualization and content knowledge categories. The implications of these findings for
    education and research are discussed.

  • Previous research suggests that a randomization-based introductory statistics course may improve student learning compared to the consensus curriculum. However, it is unclear whether these gains are retained by students post-course. We compared the conceptual understanding of a cohort of students who took a randomization-based curriculum (n = 76) to a cohort of students who used the consensus curriculum (n = 79). Overall, students taking the randomization-based curriculum showed higher conceptual retention in areas emphasized in the curriculum, with no significant decrease in conceptual retention in other areas. This study provides additional support for the use of randomization-methods in teaching introductory statistics courses.

  • In this study we examined the effects of prior mathematics achievement and completion of a commercially developed, National Science Foundation-funded, or University of Chicago School Mathematics Project high school mathematics curriculum on achievement in students’ first college statistics course. Specifically, we examined the relationship between students’ high school mathematics achievement and high school mathematics curriculum on the difficulty level of students’ first college statistics course, and on the grade earned in that course. In general, students with greater prior mathematics achievement took more difficult statistics courses and earned higher grades in those courses. The high school mathematics curriculum a student completed was unrelated to statistics grades and course-taking.

  • “Simulation-based inference” (e.g., bootstrapping and randomization tests) has been advocated recently with the goal of improving student understanding of statistical inference, as well as the statistical investigative process as a whole. Preliminary assessment data have been largely positive. This article describes the analysis of the first year of data from a multi-institution assessment effort by instructors using such an approach in a college-level introductory statistics course, some for the first time. We examine several pre-/post-measures of student attitudes and conceptual understanding of several topics in the introductory course. We highlight some patterns in the data, focusing on student level and instructor level variables and the application of hierarchical modeling to these data. One observation of interest is that the newer instructors see very similar gains to more experienced instructors, but we also look to how the data collection and analysis can be improved for future years, especially the need for more data on “nonusers.”

  • In this article, we highlight the advantages of incorporating a statistical capstone experience in the undergraduate curriculum, where students perform an in-depth analysis of real-world data. Capstone experiences develop statistical thinking by allowing students to engage in a consulting-like experience that requires skills outside the scope of traditional courses: defining a complex problem, analyzing data, building a strong team, and communicating effectively. We describe the pedagogical benefits as they relate to improved student outcomes and prospective job and graduate school placement, and we classify statistical capstones into four groups: standalone capstone projects, statistical consultancies, capstone projects embedded in an advanced statistics methodology course, and instruction-focused capstone courses. This article serves as a guide for educators seeking to implement an enriching capstone experience in their undergraduate mathematics or statistics curriculum to better prepare students for industrial and academic careers in data science.

  • Monte Carlo simulations (MCSs) provide important information about statistical phenomena that would be impossible to assess otherwise. This article introduces MCS methods and their applications to research and statistical pedagogy using a novel software package for the R Project for Statistical Computing constructed to lessen the often steep learning curve when organizing simulation code. A primary goal of this article is to demonstrate how well-suited MCS designs are to classroom demonstrations, and how they provide a hands-on method for students to become acquainted with complex statistical concepts. In this article, essential programming aspects for writing MCS code in R are overviewed, multiple applied examples with relevant code are provided, and the benefits of using a generate–analyze–summarize coding structure over the typical “for-loop” strategy are discussed.