Engaging Underrepresented High School students in Data Driven Storytelling: An Examination of Learning Experiences and Outcomes for a Cohort of Rising Seniors Enrolled in the Gaining Early Awareness and Readiness for Undergraduate Program (GEAR UP)

Background: Upward trends in data-oriented careers threaten to further increase the underrepresentation of both females and individuals from racial minority groups in programs focused on data analysis and applied statistics. To begin to develop the necessary skills for a data-oriented career, project-based learning seems the most promising given its focus on real-world activities that are aimed at engaging student interest and enthusiasm. Method: Using pre and post survey data, the present study examines student background characteristics, learning experiences and course outcomes for a cohort of 33 rising high school seniors involved in a two-week, accelerated version of a project-based data analysis and applied statistics curriculum. Results: On average, students rated the experience as rewarding and the vast majority (78.1%) felt that they had accomplished more than they had expected. Based on responses to both the pre and post course surveys, roughly half of the students reported increases in confidence in applied skills (i.e. developing a research question, managing data, choosing the correct statistical test, effectively presenting research results, and conducting a statistical analysis of data), while more than 80% reported increased confidence in writing code to run statistical analyses. Fully 84.4% of students reported interest in one or more follow-up courses with interest in computer programming being endorsed by the largest number of students (53.1%). Conclusions: These findings support previous research showing that real-world, project-based experiences afford the best hope for achieving the kind of analytic and statistical literacy necessary for meaningful engagement in research, problem solving and professional development

Passion-Driven Statistics: A course-based undergraduate research experience (CURE)

This paper describes the use of scientific practices in the Passion-Driven Statistics CURE and presents the results of surveys from the implementation of this CURE at three different colleges. Overall, students experienced positive changes in thinking and working like a scientist, personal gains related to research, and gains in research skills, attitudes and behaviors. The Passion-Driven Statistics CURE aims to equip the future STEM workforce with the data analysis skills and reasoning needed across industries

A Practical Guide to Calculating Cohen’s f2, a Measure of Local Effect Size, from PROC MIXED

Reporting effect sizes in scientific articles is increasingly widespread and encouraged by journals; however, choosing an effect size for analyses such as mixed-effects regression modeling and hierarchical linear modeling can be difficult. One relatively uncommon, but very informative, standardized measure of effect size is Cohen’s f2, which allows an evaluation of local effect size, i.e., one variable’s effect size within the context of a multivariate regression model. Unfortunately, this measure is often not readily accessible from commonly used software for repeated-measures or hierarchical data analysis. In this guide, we illustrate how to extract Cohen’s f2 for two variables within a mixed-effects regression model using PROC MIXED in SAS® software. Two examples of calculating Cohen’s f2 for different research questions are shown, using data from a longitudinal cohort study of smoking development in adolescents. This tutorial is designed to facilitate the calculation and reporting of effect sizes for single variables within mixed-effects multiple regression models, and is relevant for analyses of repeated-measures or hierarchical/multilevel data that are common in experimental psychology, observational research, and clinical or intervention studies

Engaging Diverse Students in Statistical Inquiry: A Comparison of Learning Experiences and Outcomes of Under-Represented and Non-Underrepresented Students Enrolled in a Multidisciplinary Project-Based Statistics Course

Introductory statistics needs innovative, evidence-based teaching practices that support and engage diverse students. To evaluate the success of a multidisciplinary, project-based course, we compared experiences of under-represented (URM) and non-underrepresented students in 4 years of the course. While URM students considered the material more difficult than non-URM students, URM students demonstrated similar levels of increased confidence in applied skills and interest in follow up courses as non-URM students. URM students were found to be twice as likely as non-URM students to report that their interest in conducting research increased. Increasing student confidence and interest gives all students a welcoming place at the table that will afford the best hope for achieving the kind of statistical literacy necessary for interdisciplinary research

Statistical Power of Alternative Structural Models for Comparative Effectiveness Research: Advantages of Modeling Unreliability

The advantages of modeling the unreliability of outcomes when evaluating the comparative effectiveness of health interventions is illustrated. Adding an action-research intervention component to a regular summer job program for youth was expected to help in preventing risk behaviors. A series of simple two-group alternative structural equation models are compared to test the effect of the intervention on one key attitudinal outcome in terms of model fit and statistical power with Monte Carlo simulations. Some models presuming parameters equal across the intervention and comparison groups were under- powered to detect the intervention effect, yet modeling the unreliability of the outcome measure increased their statistical power and helped in the detection of the hypothesized effect. Comparative Effectiveness Research (CER) could benefit from flexible multi- group alternative structural models organized in decision trees, and modeling unreliability of measures can be of tremendous help for both the fit of statistical models to the data and their statistical power

Passion-Driven Statistics: A course-based undergraduate research experience (CURE)

This paper describes the use of scientific practices in the Passion-Driven Statistics CURE and presents the results of surveys from the implementation of this CURE at three different colleges. Overall, students experienced positive changes in thinking and working like a scientist, personal gains related to research, and gains in research skills, attitudes and behaviors. The Passion-Driven Statistics CURE aims to equip the future STEM workforce with the data analysis skills and reasoning needed across industries

Project-Based Learning in Introductory Statistics: Comparing Course Experiences and Predicting Positive Outcomes for Students from Diverse Educational Settings

In order to evaluate the acceptability and potential impact of the Passion-Driven Statistics curriculum, this article describes background characteristics, and course experiences and outcomes of students enrolled in the multidisciplinary, introductory, project-based course in liberal arts colleges, large state universities, regional college/universities, and community colleges. We found that the course could be successfully delivered across these diverse educational settings. After controlling for educational setting and pre-survey responses to individual outcome measures, consistent predictors of positive course outcomes included student’s initial interest in conducting research, their higher likelihood of enrolling in a statistics course if it were not required, finding the project-based course less challenging, and finding the research project more rewarding than other students. Regional college/university, and community college students reported working significantly harder in the course and finding the course more challenging than students taking the course at liberal arts colleges or state universities. Students from liberal arts colleges generally reported more positive course experiences than students from other educational settings. However, when compared to students from both liberal arts colleges and large state universities, those from regional colleges/universities reported being more likely to have learned more in the project-based course than in other college courses they had taken. Taken together, the project-based course was successfully delivered across diverse post-secondary educational settings and provides a promising model for getting students hooked on the power and excitement of applied statistics

Developmental and individual differences in children\u27s ability to distinguish reality from fantasy

The present study focuses on children\u27s developing ability to categorize real and pretend events, their understanding regarding the permanence of the state of pretense, and the potential effects that emotional tones have on these abilities. Children\u27s involvement in imagination was also assessed as a possible factor contributing to individual differences in reality/fantasy understanding. Sixty male and female children selected from university preschool and kindergarten classes judged happy, neutral and frightening pictures selected from children\u27s books according to whether they believed that the event could happen in real life. Mental age was statistically controlled in the analyses using scores on the Peabody Picture Vocabulary Test-Revised (1981). Imaginative involvement was measured using the Imaginative Play Predisposition Interview (Singer, 1973) and self directed pretend tasks (Overton and Jackson, 1973).^ Findings show that kindergartners perform significantly better than preschoolers in distinguishing real from pretend events. These group differences seemed to stem from preschooler\u27s tendency to overattribute pretense when considering pictured events (e.g. preschoolers often judged that a man talking on the phone was only pretend). Overall, children made significantly fewer correct distinctions between reality and fantasy for the frightening pictures than for both the happy and neutral pictures.^ Individual difference analyses between children judged to be differentially involved in fantasy did not reveal any differences in their ability to distinguish between real and pretend events. Children\u27s judgments regarding the permanence of the state of pretense revealed that children in both preschool and kindergarten often believe that pretend events can be made real by imagining. The group of high fantasizers as measured by the Imaginative Play Predisposition Interview were found to believe in the possibility of fantasy events becoming real by pretending more often than the low fantasizing group. Theoretical and practical implications of these findings are discussed.

Confidence Disparities: Pre-course Coding Confidence Predicts Greater Statistics Intentions and Perceived Achievement in a Project-Based Introductory Statistics Course

AbstractSelf-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 nonminoritized 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