Pub Date : 2023-02-07DOI: 10.1080/26939169.2023.2177214
Hairui Yu, S. Perumean-Chaney, K. Kaiser
{"title":"What is Missing in Missing Data Handling? An Evaluation of Missingness in and Potential Remedies for Doctoral Dissertations and Subsequent Publications that use NHANES Data","authors":"Hairui Yu, S. Perumean-Chaney, K. Kaiser","doi":"10.1080/26939169.2023.2177214","DOIUrl":"https://doi.org/10.1080/26939169.2023.2177214","url":null,"abstract":"","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45941538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-03DOI: 10.1080/26939169.2023.2231036
A. Flores, Lauren Cappiello, Isaac Quintanilla Salinas
As the COVID-19 pandemic took hold in early months of 2020, education at all levels was pushed to emergency fully remote, online formats. This emergency shift affected all aspects of teaching and learning with very little notice and often with limited resources. Educators were required to convert entire courses online and shift to remote instructional approaches practically overnight. Students found themselves enrolled in online courses without choice and struggling to adjust to their new learning environments. This article highlights some of the challenges and successes of teaching emergency online undergraduate statistics courses. In particular, we discuss challenges and successes related to (1) technology, (2) classroom community and feedback, and (3) student-content engagement. We also reflect on the opportunity to continue to enhance and enrich the learning experiences of our students by utilizing some of the lessons learned from emergency online teaching as new permanent online statistics courses are developed and/or moved back into the classroom.
{"title":"Challenges and Successes of Emergency Online Teaching in Statistics Courses","authors":"A. Flores, Lauren Cappiello, Isaac Quintanilla Salinas","doi":"10.1080/26939169.2023.2231036","DOIUrl":"https://doi.org/10.1080/26939169.2023.2231036","url":null,"abstract":"As the COVID-19 pandemic took hold in early months of 2020, education at all levels was pushed to emergency fully remote, online formats. This emergency shift affected all aspects of teaching and learning with very little notice and often with limited resources. Educators were required to convert entire courses online and shift to remote instructional approaches practically overnight. Students found themselves enrolled in online courses without choice and struggling to adjust to their new learning environments. This article highlights some of the challenges and successes of teaching emergency online undergraduate statistics courses. In particular, we discuss challenges and successes related to (1) technology, (2) classroom community and feedback, and (3) student-content engagement. We also reflect on the opportunity to continue to enhance and enrich the learning experiences of our students by utilizing some of the lessons learned from emergency online teaching as new permanent online statistics courses are developed and/or moved back into the classroom.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"1 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41317557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-12DOI: 10.1080/26939169.2023.2167750
Nicole M. Dalzell, Ciaran Evans
Abstract Statistical competitions like ASA DataFest and the Women in Data Science (WiDS) Datathon give students valuable experience working with real, challenging data. By participating, students practice important statistics and data science skills including data wrangling, visualization, modeling, communication, and teamwork. However, while advanced students may have already acquired these skills over the course of their undergraduate program, students with less experience often need additional preparation to participate. In this article, we discuss strategies and targeted activities for helping lower-level students feel comfortable and prepared to compete in events like DataFest. We also share how we used these tools to create a low-stakes DataFest preparation course at our institution. Supplementary materials for this article are available online.
{"title":"Increasing student access to and readiness for statistical competitions","authors":"Nicole M. Dalzell, Ciaran Evans","doi":"10.1080/26939169.2023.2167750","DOIUrl":"https://doi.org/10.1080/26939169.2023.2167750","url":null,"abstract":"Abstract Statistical competitions like ASA DataFest and the Women in Data Science (WiDS) Datathon give students valuable experience working with real, challenging data. By participating, students practice important statistics and data science skills including data wrangling, visualization, modeling, communication, and teamwork. However, while advanced students may have already acquired these skills over the course of their undergraduate program, students with less experience often need additional preparation to participate. In this article, we discuss strategies and targeted activities for helping lower-level students feel comfortable and prepared to compete in events like DataFest. We also share how we used these tools to create a low-stakes DataFest preparation course at our institution. Supplementary materials for this article are available online.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42996136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-10DOI: 10.1080/26939169.2023.2165987
Ann M. Brearley, Kollin W Rott, Laura J Le
Abstract We present a unique and innovative course, Biostatistical Literacy, developed at the University of Minnesota. The course is aimed at public health graduate students and health sciences professionals. Its goal is to develop students’ ability to read and interpret statistical results in the medical and public health literature. The content spans the typical first-semester introductory material, including data summaries, hypothesis tests and interval estimation, and simple linear regression, as well as material typically presented in a second introductory course, including multiple linear regression, logistic regression, and time-to-event methods. The focus is on when to use a method and how to interpret the results; no statistical software computing is taught. A flipped classroom approach is used, where students are first exposed to the material outside of class, and class time is devoted to actively exploring and applying the concepts in greater depth. The course structure, the class activities, and feedback from students will be shared. Supplementary materials for this article are available online.
{"title":"A Biostatistical Literacy Course: Teaching Medical and Public Health Professionals to Read and Interpret Statistics in the Published Literature","authors":"Ann M. Brearley, Kollin W Rott, Laura J Le","doi":"10.1080/26939169.2023.2165987","DOIUrl":"https://doi.org/10.1080/26939169.2023.2165987","url":null,"abstract":"Abstract We present a unique and innovative course, Biostatistical Literacy, developed at the University of Minnesota. The course is aimed at public health graduate students and health sciences professionals. Its goal is to develop students’ ability to read and interpret statistical results in the medical and public health literature. The content spans the typical first-semester introductory material, including data summaries, hypothesis tests and interval estimation, and simple linear regression, as well as material typically presented in a second introductory course, including multiple linear regression, logistic regression, and time-to-event methods. The focus is on when to use a method and how to interpret the results; no statistical software computing is taught. A flipped classroom approach is used, where students are first exposed to the material outside of class, and class time is devoted to actively exploring and applying the concepts in greater depth. The course structure, the class activities, and feedback from students will be shared. Supplementary materials for this article are available online.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46647570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-02DOI: 10.1080/26939169.2023.2178778
N. Horton
{"title":"Teaching causal inference: moving beyond ‘correlation does not imply causation’","authors":"N. Horton","doi":"10.1080/26939169.2023.2178778","DOIUrl":"https://doi.org/10.1080/26939169.2023.2178778","url":null,"abstract":"","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"31 1","pages":"1 - 3"},"PeriodicalIF":1.7,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43787646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-02DOI: 10.1080/26939169.2022.2040402
D. Follmer
Abstract Learners’ efficacy beliefs are an important determinant of their performance and future study in an academic domain, and recent work has highlighted the complexities associated with promoting learners’ statistics efficacy. The current study tested the effectiveness of a course-embedded, activity-driven intervention, grounded in principles of self-regulated learning, in promoting graduate learners’ statistics efficacy and concept knowledge. The intervention was designed to elicit and scaffold students’ analysis-specific efficacy beliefs and facilitate their monitoring of and critical reflection on their statistics understanding across engagement in an intermediate, graduate-level statistics course. Students’ pre- and post-course statistics efficacy was assessed; students’ statistics concept knowledge was assessed at the completion of the course. Engagement in the intervention explained a moderate amount of variance in learners’ post-course statistics efficacy and concept knowledge; efficacy and concept knowledge scores were higher for students in the intervention condition compared with students in the comparison condition. The findings suggest the use of these activities as a method of prompting students to engage more strategically with learning material in statistics. Implications for future research and pedagogy are discussed.
{"title":"Implementing a Simple, Scalable Self-Regulated Learning Intervention to Promote Graduate Learners’ Statistics Self-Efficacy and Concept Knowledge","authors":"D. Follmer","doi":"10.1080/26939169.2022.2040402","DOIUrl":"https://doi.org/10.1080/26939169.2022.2040402","url":null,"abstract":"Abstract Learners’ efficacy beliefs are an important determinant of their performance and future study in an academic domain, and recent work has highlighted the complexities associated with promoting learners’ statistics efficacy. The current study tested the effectiveness of a course-embedded, activity-driven intervention, grounded in principles of self-regulated learning, in promoting graduate learners’ statistics efficacy and concept knowledge. The intervention was designed to elicit and scaffold students’ analysis-specific efficacy beliefs and facilitate their monitoring of and critical reflection on their statistics understanding across engagement in an intermediate, graduate-level statistics course. Students’ pre- and post-course statistics efficacy was assessed; students’ statistics concept knowledge was assessed at the completion of the course. Engagement in the intervention explained a moderate amount of variance in learners’ post-course statistics efficacy and concept knowledge; efficacy and concept knowledge scores were higher for students in the intervention condition compared with students in the comparison condition. The findings suggest the use of these activities as a method of prompting students to engage more strategically with learning material in statistics. Implications for future research and pedagogy are discussed.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"31 1","pages":"80 - 90"},"PeriodicalIF":1.7,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47738076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-02DOI: 10.1080/26939169.2022.2093805
K. MacArthur, J. Santo
Abstract This study explores three understudied facets—quadratic effects, change over time, and gender as a moderator—of the otherwise well-documented relationships between statistics anxiety and academic performance. Using pre- and post- course survey data among a sample of 111 undergraduate students in Social Statistics courses at a U.S. Midwestern university, we employ hierarchical linear modeling (HLM) to test for relationships between change in the six dimensions of the Statistics Anxiety Rating Scale (STARS) and exam grades over the course of the semester. We find that exam grades decreased over time, but at different rates across gender and the six STARS dimensions. We also identify a quadratic relationship between self-concept and final exam grades, as well as several gender interactions. This study highlights the multifaceted and dynamic nature of statistics anxiety/attitudes, as its relationship with academic performance is not always negative, linear, stable over time, or uniform across gender. Supplementary materials for this article are available online.
{"title":"A Multi-Level Analysis of the Effects of Statistics Anxiety/Attitudes on Trajectories of Exam Scores","authors":"K. MacArthur, J. Santo","doi":"10.1080/26939169.2022.2093805","DOIUrl":"https://doi.org/10.1080/26939169.2022.2093805","url":null,"abstract":"Abstract This study explores three understudied facets—quadratic effects, change over time, and gender as a moderator—of the otherwise well-documented relationships between statistics anxiety and academic performance. Using pre- and post- course survey data among a sample of 111 undergraduate students in Social Statistics courses at a U.S. Midwestern university, we employ hierarchical linear modeling (HLM) to test for relationships between change in the six dimensions of the Statistics Anxiety Rating Scale (STARS) and exam grades over the course of the semester. We find that exam grades decreased over time, but at different rates across gender and the six STARS dimensions. We also identify a quadratic relationship between self-concept and final exam grades, as well as several gender interactions. This study highlights the multifaceted and dynamic nature of statistics anxiety/attitudes, as its relationship with academic performance is not always negative, linear, stable over time, or uniform across gender. Supplementary materials for this article are available online.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"31 1","pages":"102 - 112"},"PeriodicalIF":1.7,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49108384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-14DOI: 10.1080/26939169.2022.2146614
Yuan-Wei Lu
Abstract Interactive web-based applets have proven effective in teaching statistics. This article presents new implementations of web-based applets primarily targeting a traditional introductory statistics course in two particular areas: (a) using real-time response data to engage students in simulations and (b) generating randomized datasets for assignments. It also provides an extended use case in courses beyond the traditional introductory statistics course. All applets given in the examples are made using the open-source package Shiny in R. The source code with detailed comments for all applets in this article is available in the supplementary materials section for other instructors to adopt and tailor to their needs. Supplementary materials for this article are available online.
{"title":"Web-Based Applets for Facilitating Simulations and Generating Randomized Data Sets for Teaching Statistics","authors":"Yuan-Wei Lu","doi":"10.1080/26939169.2022.2146614","DOIUrl":"https://doi.org/10.1080/26939169.2022.2146614","url":null,"abstract":"Abstract Interactive web-based applets have proven effective in teaching statistics. This article presents new implementations of web-based applets primarily targeting a traditional introductory statistics course in two particular areas: (a) using real-time response data to engage students in simulations and (b) generating randomized datasets for assignments. It also provides an extended use case in courses beyond the traditional introductory statistics course. All applets given in the examples are made using the open-source package Shiny in R. The source code with detailed comments for all applets in this article is available in the supplementary materials section for other instructors to adopt and tailor to their needs. Supplementary materials for this article are available online.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48593476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-10DOI: 10.1080/26939169.2022.2146613
M. Arendarczyk, T. Kozubowski, A. Panorska
Abstract We provide tools for identification and exploration of data with very large variability having power law tails. Such data describe extreme features of processes such as fire losses, flood, drought, financial gain/loss, hurricanes, population of cities, among others. Prediction and quantification of extreme events are at the forefront of the current research needs, as these events have the strongest impact on our lives, safety, economics, and the environment. We concentrate on the intuitive, rather than rigorous mathematical treatment of models with heavy tails. Our goal is to introduce instructors to these important models and provide some tools for their identification and exploration. The methods we provide may be incorporated into courses such as probability, mathematical statistics, statistical modeling or regression methods. Our examples come from ecology and census fields. Supplementary materials for this article are available online.
{"title":"Preparing students for the future: extreme events and power tails","authors":"M. Arendarczyk, T. Kozubowski, A. Panorska","doi":"10.1080/26939169.2022.2146613","DOIUrl":"https://doi.org/10.1080/26939169.2022.2146613","url":null,"abstract":"Abstract We provide tools for identification and exploration of data with very large variability having power law tails. Such data describe extreme features of processes such as fire losses, flood, drought, financial gain/loss, hurricanes, population of cities, among others. Prediction and quantification of extreme events are at the forefront of the current research needs, as these events have the strongest impact on our lives, safety, economics, and the environment. We concentrate on the intuitive, rather than rigorous mathematical treatment of models with heavy tails. Our goal is to introduce instructors to these important models and provide some tools for their identification and exploration. The methods we provide may be incorporated into courses such as probability, mathematical statistics, statistical modeling or regression methods. Our examples come from ecology and census fields. Supplementary materials for this article are available online.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49301390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-28DOI: 10.1080/26939169.2022.2138799
B. Arinze
Abstract Data Analytics has grown dramatically in importance and in the level of business deployments in recent years. It is used across most functional areas and applications, some of the latter including market campaigns, detecting fraud, determining credit, identifying assembly line defects, health services and many others. Indeed, the realm of analytics has famously grown to include major league sports and even U.S. election campaigns. Universities have raced to include data analytics in their curricula as the need for data scientists has become more acute. Unfortunately, many data science courses in college curricula suffer from various deficiencies: some lack a hands-on component, others are insufficiently experiential, and yet others leave students with too few transferable skills. This article describes an experiential approach to teaching data analytics at the college level that uses an election simulation, MISSimulation.com—to communicate key data science concepts in a competitive setting. Many universities actively use the simulation, combined with analytic tools such as Tableau and Excel, to implement team competition. We explore key techniques used and knowledge learned during the typical teaching of a data analytics course using the simulation. We end with pedagogical review of data analytics skills transferred during the course and student feedback.
{"title":"Teaching Experiential Data Analytics Using an Election Simulation","authors":"B. Arinze","doi":"10.1080/26939169.2022.2138799","DOIUrl":"https://doi.org/10.1080/26939169.2022.2138799","url":null,"abstract":"Abstract Data Analytics has grown dramatically in importance and in the level of business deployments in recent years. It is used across most functional areas and applications, some of the latter including market campaigns, detecting fraud, determining credit, identifying assembly line defects, health services and many others. Indeed, the realm of analytics has famously grown to include major league sports and even U.S. election campaigns. Universities have raced to include data analytics in their curricula as the need for data scientists has become more acute. Unfortunately, many data science courses in college curricula suffer from various deficiencies: some lack a hands-on component, others are insufficiently experiential, and yet others leave students with too few transferable skills. This article describes an experiential approach to teaching data analytics at the college level that uses an election simulation, MISSimulation.com—to communicate key data science concepts in a competitive setting. Many universities actively use the simulation, combined with analytic tools such as Tableau and Excel, to implement team competition. We explore key techniques used and knowledge learned during the typical teaching of a data analytics course using the simulation. We end with pedagogical review of data analytics skills transferred during the course and student feedback.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"1 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46462958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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