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Course-based undergraduate research experiences (CURE) provide valuable opportunities to large numbers of students relatively early in their academic careers and have the potential to attract and retain women and students from underrepresented minority groups both in the sciences and in other technical fields requiring quantitative research literacy. To evaluate the relative success of a multidisciplinary CURE, we compared background characteristics, course experiences and outcomes of men and women under-represented (URM) and non-underrepresented students. Though URM and non-URM students of both genders differed on many background characteristics, and self-reported course experiences, with few exceptions, positive course outcomes and predictors of those outcomes did not differ by URM/gender group. The Passion-Driven Statistics CURE aims to equip the future STEM workforce with the data analysis skills and reasoning needed across industries. Additional research is needed to determine whether this CURE may influence educational and career trajectories for women and URM students. 

Self-efficacy is associated with a range of educational outcomes, including science and math degree attainment. Project-based statistics courses have the potential to increase students’ math self-efficacy because projects may represent a mastery experience, but students enter courses with preexisting math self-efficacy. This study explored associations between pre-course math confidence and coding confidence with post course statistical intentions and perceived achievement among students in a project-based statistics course at 28 private and public colleges and universities between fall 2018 and winter 2020 (n=801) using multilevel mixed-effects multivariate linear regression within multiply imputed data with a cross-validation approach (testing n=508 at 20 colleges/universities). We found that pre-course coding confidence was associated with, respectively, 9 points greater post-course statistical intentions and 10 points greater perceived achievement on a scale 0–100 (0.09, 95% confidence interval (0.02, 0.17), p=0.02; 0.10, 95% CI (0.01, 0.19), p=0.04), and that minoritized students have greater post-course statistical intentions than non-minoritized students. These results concur with past research showing the potential effectiveness of the project-based approach for increasing the interest of minoritized students in statistics. Pre-course interventions to increase coding confidence such as pre-college coding experiences may improve students’ post-course motivations and perceived achievement in a project-based course. Supplementary materials for this article are available online. 

The purpose of this study was to inform the dissemination of a project-based statistics curriculum by identifying institutional and instructor characteristics that predict its implementation. Data were drawn from pre- and post-workshop surveys completed by 67 instructors attending a one-and-a-half-day professional development workshop. Nearly half of the instructors who intended to implement Passion-Driven Statistics following the workshop employed the project-based curriculum by the end of the first full academic year. Teaching at a private institution and endorsing a larger number of positive adjectives regarding teachers’ likely experiences with the model predicted its actual implementation. 

Abstract Deaths are frequently under-estimated during emergencies, times when accurate mortality estimates are crucial for emergency response. This study estimates excess all-cause, pneumonia and influenza mortality during the coronavirus disease 2019 (COVID-19) pandemic using the 11 September 2020 release of weekly mortality data from the United States (U.S.) Mortality Surveillance System (MSS) from 27 September 2015 to 9 May 2020, using semiparametric and conventional time-series models in 13 states with high reported COVID-19 deaths and apparently complete mortality data: California, Colorado, Connecticut, Florida, Illinois, Indiana, Louisiana, Massachusetts, Michigan, New Jersey, New York, Pennsylvania and Washington. We estimated greater excess mortality than official COVID-19 mortality in the U.S. (excess mortality 95% confidence interval (CI) 100 013–127 501 vs. 78 834 COVID-19 deaths) and 9 states: California (excess mortality 95% CI 3338–6344) vs. 2849 COVID-19 deaths); Connecticut (excess mortality 95% CI 3095–3952) vs. 2932 COVID-19 deaths); Illinois (95% CI 4646–6111) vs. 3525 COVID-19 deaths); Louisiana (excess mortality 95% CI 2341–3183 vs. 2267 COVID-19 deaths); Massachusetts (95% CI 5562–7201 vs. 5050 COVID-19 deaths); New Jersey (95% CI 13 170–16 058 vs. 10 465 COVID-19 deaths); New York (95% CI 32 538–39 960 vs. 26 584 COVID-19 deaths); and Pennsylvania (95% CI 5125–6560 vs. 3793 COVID-19 deaths). Conventional model results were consistent with semiparametric results but less precise. Significant excess pneumonia deaths were also found for all locations and we estimated hundreds of excess influenza deaths in New York. We find that official COVID-19 mortality substantially understates actual mortality, excess deaths cannot be explained entirely by official COVID-19 death counts. Mortality reporting lags appeared to worsen during the pandemic, when timeliness in surveillance systems was most crucial for improving pandemic response.