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Undergraduate research experiences (UREs), whether within the context of a mentor-mentee experience or a classroom framework, represent an excellent opportunity to expose students to the independent scholarship model. The high impact of undergraduate research has received recent attention in the context of STEM disciplines. Reflecting a 2017 survey of statistics faculty, this article examines the perceived benefits of UREs, as well as barriers to the incorporation of UREs, specifically within the field of statistics. Viewpoints of students, faculty mentors, and institutions are investigated. Further, the article offers several strategies for leveraging characteristics unique to the field of statistics to overcome barriers and thereby provide greater opportunity for undergraduate statistics students to gain research experience.

“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, es...

Prolonged, severe, unalleviated fatigue may be disabling whether it occurs on its own or in conjunction with medical or psychiatric conditions. This paper compares the prevalence and correlates of prolonged fatigue in general population samples in Ukraine versus the U.S.Population surveys were conducted in 2002 in both Ukraine (Ukraine World Mental Health [WMH] Survey) and the U.S. (National Comorbidity Survey-Replication; NCS-R). Both surveys administered the Composite International Diagnostic Interview (CIDI 3.0), which contained modules assessing: neurasthenia (prolonged fatigue); mood, anxiety, and alcohol/drug use disorders; chronic medical conditions; and demographic characteristics. Multivariable logistic regression was used to examine risk factors in each country.The lifetime prevalence of prolonged fatigue was higher in Ukraine (5.2%) than the U.S. (3.7%). In both countries, one-fifth of individuals with prolonged fatigue had no medical or DSM-IV psychiatric condition. Also in both settings, fatigue was significantly associated with sociodemographic characteristics (being female, not working, and married before) as well as early onset and adult episodes of mood/anxiety disorder. Fatigue prevalence in Ukraine increased with age, but decreased in the U.S. at age 70. Unique risk factors for fatigue in Ukraine included lower socio-economic status, Ukrainian vs Russian ethnicity, and cardiovascular disease. Unique risk factors in the U.S. were parental depression/anxiety, adult episodes of alcohol/drugs, pain conditions, and other health problems.The lifetime prevalence of prolonged fatigue in Ukraine was 40% higher than that found in U.S. data. In addition, fatigue prevalence increased sharply with age in Ukraine perhaps due to limited social and medical resources and greater comorbidity.

The algebra-based introductory statistics course is the most popular undergraduate course in statistics. While there is a general consensus for the content of the curriculum, the recent Guidelines for Assessment and Instruction in Statistics Education (GAISE) have challenged the pedagogy of this course. Additionally, some arguments have been made that the curriculum should focus on a randomization approach to statistical inference instead of using asymptotic tests. We developed a preliminary version of a randomization based curriculum which we then implemented with 240 students in eight sections of introductory statistics in fall 2009. The Comprehensive Assessment of Outcomes in Statistics (CAOS) assessment test was administered to these students and showed that students learned significantly more about statistical inference using the new curriculum, with comparable learning on most other questions. The assessment results demonstrate that refining content, improving pedagogy and rethinking the consensus c...