Pub Date : 2022-05-13DOI: 10.1080/26939169.2022.2094300
Dewi Amaliah, D. Cook, Emi Tanaka, Kate Hyde, Nicholas J. Tierney
Abstract Textbook data is essential for teaching statistics and data science methods because it is clean, allowing the instructor to focus on methodology. Ideally textbook datasets are refreshed regularly, especially when they are subsets taken from an ongoing data collection. It is also important to use contemporary data for teaching, to imbue the sense that the methodology is relevant today. This article describes the trials and tribulations of refreshing a textbook dataset on wages, extracted from the National Longitudinal Survey of Youth (NLSY79) in the early 1990s. The data is useful for teaching modeling and exploratory analysis of longitudinal data. Subsets of NLSY79, including the wages data, can be found in supplementary materials from numerous textbooks and research articles. The NLSY79 database has been continually updated through to 2018, so new records are available. Here we describe our journey to refresh the wages data, and document the process so that the data can be regularly updated into the future. Our journey was difficult because the steps and decisions taken to get from the raw data to the wages textbook subset have not been clearly articulated. We have been diligent to provide a reproducible workflow for others to follow, which also hopefully inspires more attempts at refreshing data for teaching. Three new datasets and the code to produce them are provided in the open source R package called yowie. Supplementary materials for this article are available online.
{"title":"A Journey from Wild to Textbook Data to Reproducibly Refresh the Wages Data from the National Longitudinal Survey of Youth Database","authors":"Dewi Amaliah, D. Cook, Emi Tanaka, Kate Hyde, Nicholas J. Tierney","doi":"10.1080/26939169.2022.2094300","DOIUrl":"https://doi.org/10.1080/26939169.2022.2094300","url":null,"abstract":"Abstract Textbook data is essential for teaching statistics and data science methods because it is clean, allowing the instructor to focus on methodology. Ideally textbook datasets are refreshed regularly, especially when they are subsets taken from an ongoing data collection. It is also important to use contemporary data for teaching, to imbue the sense that the methodology is relevant today. This article describes the trials and tribulations of refreshing a textbook dataset on wages, extracted from the National Longitudinal Survey of Youth (NLSY79) in the early 1990s. The data is useful for teaching modeling and exploratory analysis of longitudinal data. Subsets of NLSY79, including the wages data, can be found in supplementary materials from numerous textbooks and research articles. The NLSY79 database has been continually updated through to 2018, so new records are available. Here we describe our journey to refresh the wages data, and document the process so that the data can be regularly updated into the future. Our journey was difficult because the steps and decisions taken to get from the raw data to the wages textbook subset have not been clearly articulated. We have been diligent to provide a reproducible workflow for others to follow, which also hopefully inspires more attempts at refreshing data for teaching. Three new datasets and the code to produce them are provided in the open source R package called yowie. Supplementary materials for this article are available online.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 1","pages":"289 - 303"},"PeriodicalIF":1.7,"publicationDate":"2022-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43117295","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 : 2022-05-04DOI: 10.1080/26939169.2022.2074584
Ashley Petersen
Abstract While correlated data methods (like random effect models and generalized estimating equations) are commonly applied in practice, students may struggle with understanding the reasons that standard regression techniques fail if applied to correlated outcomes. To this end, this article presents an in-class activity using results from Monte Carlo simulations to introduce the impact of ignoring the correlation between outcomes by applying standard regression techniques. This activity is used at the beginning of a graduate course on statistical methods for analyzing correlated data taken by students with limited mathematical backgrounds. Students gain the intuition that analyzing correlated outcomes using methods for independent data produces invalid inference (i.e., confidence intervals and p-values) due to underestimated or overestimated standard errors of the effect estimates, even though the effect estimates themselves are still valid. While this standalone 90-minute in-class activity can be added at the beginning of an existing course on statistical methods for correlated data without any further changes, techniques for reinforcing students’ intuition throughout the course and applying this intuition to teach sample size and power calculations for correlated outcomes are also discussed. Supplementary materials for this article are available online.
{"title":"Developing Students’ Intuition on the Impact of Correlated Outcomes","authors":"Ashley Petersen","doi":"10.1080/26939169.2022.2074584","DOIUrl":"https://doi.org/10.1080/26939169.2022.2074584","url":null,"abstract":"Abstract While correlated data methods (like random effect models and generalized estimating equations) are commonly applied in practice, students may struggle with understanding the reasons that standard regression techniques fail if applied to correlated outcomes. To this end, this article presents an in-class activity using results from Monte Carlo simulations to introduce the impact of ignoring the correlation between outcomes by applying standard regression techniques. This activity is used at the beginning of a graduate course on statistical methods for analyzing correlated data taken by students with limited mathematical backgrounds. Students gain the intuition that analyzing correlated outcomes using methods for independent data produces invalid inference (i.e., confidence intervals and p-values) due to underestimated or overestimated standard errors of the effect estimates, even though the effect estimates themselves are still valid. While this standalone 90-minute in-class activity can be added at the beginning of an existing course on statistical methods for correlated data without any further changes, techniques for reinforcing students’ intuition throughout the course and applying this intuition to teach sample size and power calculations for correlated outcomes are also discussed. Supplementary materials for this article are available online.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 1","pages":"154 - 164"},"PeriodicalIF":1.7,"publicationDate":"2022-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41349885","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 : 2022-05-04DOI: 10.1080/26939169.2022.2044943
Sierra Paloian, Kirsten Doehler, Alexandra Lahetta
Abstract A Statistics Practicum course is offered as another option besides an internship or research experience for students to fulfill a required statistics major capstone experience. This article discusses the first and fourth offering of this practicum course, which provides a unique perspective on the initial implementation of the course and its development over time. The course offers students opportunities to carry out statistical consulting projects with external clients. Students were given multiple reflection assignments throughout the course. Challenges of the projects were discussed in the reflections, which included issues of data cleaning and analysis. Students also responded to both Likert-scale and open-ended questions on an end of semester survey. These responses provided information on sentiment regarding the consulting projects and perceived usefulness of various components of the Statistics Practicum course. Both student reflection assignments and survey responses were analyzed in this study. Explanations of the thought processes that went into setting up and running the course are included. Advice and suggestions for course improvements and successful administration are also presented.
{"title":"Implementing a Senior Statistics Practicum: Lessons and Feedback from Multiple Offerings","authors":"Sierra Paloian, Kirsten Doehler, Alexandra Lahetta","doi":"10.1080/26939169.2022.2044943","DOIUrl":"https://doi.org/10.1080/26939169.2022.2044943","url":null,"abstract":"Abstract A Statistics Practicum course is offered as another option besides an internship or research experience for students to fulfill a required statistics major capstone experience. This article discusses the first and fourth offering of this practicum course, which provides a unique perspective on the initial implementation of the course and its development over time. The course offers students opportunities to carry out statistical consulting projects with external clients. Students were given multiple reflection assignments throughout the course. Challenges of the projects were discussed in the reflections, which included issues of data cleaning and analysis. Students also responded to both Likert-scale and open-ended questions on an end of semester survey. These responses provided information on sentiment regarding the consulting projects and perceived usefulness of various components of the Statistics Practicum course. Both student reflection assignments and survey responses were analyzed in this study. Explanations of the thought processes that went into setting up and running the course are included. Advice and suggestions for course improvements and successful administration are also presented.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 1","pages":"114 - 126"},"PeriodicalIF":1.7,"publicationDate":"2022-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42142110","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 : 2022-05-04DOI: 10.1080/26939169.2022.2058656
J. Mackay
Abstract Students need to know how to discern patterns and make decisions using visual information in our modern economy. However, there are few sources of real-world information available to instructors that give students access to visualizations to help develop their skills in interpreting complex situations using diverse data sources. This article outlines a teaching exercise that uses the New Zealand government’s data portal. This website contains detailed time series data and visualizations that span economic, social and health data derived from multiple government ministries and New Zealand businesses. The portal continues to be used by government decision-makers to make real-time decisions about the nation’s economy and citizen well-being. Typically, statistical agencies carefully vet the data they supply. The data portal prioritizes the timeliness of the information for decision-makers working in a crisis. This brief communication outlines an exercise for students to explore and interpret data through visualizations.
{"title":"Data Discovery Challenge Using the COVID-19 Data Portal from New Zealand","authors":"J. Mackay","doi":"10.1080/26939169.2022.2058656","DOIUrl":"https://doi.org/10.1080/26939169.2022.2058656","url":null,"abstract":"Abstract Students need to know how to discern patterns and make decisions using visual information in our modern economy. However, there are few sources of real-world information available to instructors that give students access to visualizations to help develop their skills in interpreting complex situations using diverse data sources. This article outlines a teaching exercise that uses the New Zealand government’s data portal. This website contains detailed time series data and visualizations that span economic, social and health data derived from multiple government ministries and New Zealand businesses. The portal continues to be used by government decision-makers to make real-time decisions about the nation’s economy and citizen well-being. Typically, statistical agencies carefully vet the data they supply. The data portal prioritizes the timeliness of the information for decision-makers working in a crisis. This brief communication outlines an exercise for students to explore and interpret data through visualizations.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 1","pages":"187 - 190"},"PeriodicalIF":1.7,"publicationDate":"2022-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42529468","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 : 2022-05-04DOI: 10.1080/26939169.2022.2074923
Ciaran Evans
Abstract This article demonstrates how data from a biology paper, which analyzes the relationship between mass and metabolic rate for two species of marine bryozoan, can be used to teach a variety of regression topics to both introductory and advanced students. A thorough analysis requires intelligent data wrangling, variable transformations, and accounting for correlation between observations. The bryozoan data can be used as a valuable class example throughout the semester, or as a dataset for extended homework assignments and class projects. Supplementary materials for this article are available online.
{"title":"Regression, Transformations, and Mixed-Effects with Marine Bryozoans","authors":"Ciaran Evans","doi":"10.1080/26939169.2022.2074923","DOIUrl":"https://doi.org/10.1080/26939169.2022.2074923","url":null,"abstract":"Abstract This article demonstrates how data from a biology paper, which analyzes the relationship between mass and metabolic rate for two species of marine bryozoan, can be used to teach a variety of regression topics to both introductory and advanced students. A thorough analysis requires intelligent data wrangling, variable transformations, and accounting for correlation between observations. The bryozoan data can be used as a valuable class example throughout the semester, or as a dataset for extended homework assignments and class projects. Supplementary materials for this article are available online.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 1","pages":"198 - 206"},"PeriodicalIF":1.7,"publicationDate":"2022-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41630073","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 : 2022-05-04DOI: 10.1080/26939169.2022.2075814
Travis Weiland, Anita Sundrani
Abstract Statistical literacy is key in this heavily polarized information age for an informed and critical citizenry to make sense of arguments in the media and society. The responsibility of developing statistical literacy is often left to the K-12 mathematics curriculum. In this article, we discuss our investigation of K-8 students’ current opportunities to learn statistics created by state mathematics standards. We analyze the standards for alignment to the Guidelines for the Assessment and Instruction in Statistics Education (GAISE II) PreK-12 report and summarize the conceptual themes that emerged. We found that while states provide K-8 students opportunities to analyze and interpret data, they do not offer many opportunities for students to engage in formulating questions and collecting/considering data. We discuss the implications of the findings for policy makers and researchers and provide recommendations for policy makers and standards writers.
{"title":"Opportunities for K-8 Students to Learn Statistics Created by States’ Standards in the United States","authors":"Travis Weiland, Anita Sundrani","doi":"10.1080/26939169.2022.2075814","DOIUrl":"https://doi.org/10.1080/26939169.2022.2075814","url":null,"abstract":"Abstract Statistical literacy is key in this heavily polarized information age for an informed and critical citizenry to make sense of arguments in the media and society. The responsibility of developing statistical literacy is often left to the K-12 mathematics curriculum. In this article, we discuss our investigation of K-8 students’ current opportunities to learn statistics created by state mathematics standards. We analyze the standards for alignment to the Guidelines for the Assessment and Instruction in Statistics Education (GAISE II) PreK-12 report and summarize the conceptual themes that emerged. We found that while states provide K-8 students opportunities to analyze and interpret data, they do not offer many opportunities for students to engage in formulating questions and collecting/considering data. We discuss the implications of the findings for policy makers and researchers and provide recommendations for policy makers and standards writers.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 1","pages":"165 - 178"},"PeriodicalIF":1.7,"publicationDate":"2022-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44817784","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 : 2022-05-04DOI: 10.1080/26939169.2022.2097563
Nicholas J. Horton
{"title":"What to Teach, How to Teach, and When to Teach: Musings on Data Science Education","authors":"Nicholas J. Horton","doi":"10.1080/26939169.2022.2097563","DOIUrl":"https://doi.org/10.1080/26939169.2022.2097563","url":null,"abstract":"","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 1","pages":"99 - 99"},"PeriodicalIF":1.7,"publicationDate":"2022-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49621352","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 : 2022-05-04DOI: 10.1080/26939169.2022.2082601
Ibrahim Dahlstrom‐Hakki, Michelle L. Wallace
Abstract There have been significant developments in the field of statistics education over the past decade that have improved outcomes for all students. However, there remains relatively little research on the best practices for teaching statistics to students with disabilities. This article describes a conceptual visual approach to teaching a college level general education statistics course aimed at addressing the needs of students with disabilities and other struggling students. The conceptual visual components were employed using the technology tool TinkerPlots. The approach is informed by the recommendations of the GAISE report as well as research on Universal Design and Cognitive Load Theory. With support from the NSF (HRD-1128948), the approach was pilot tested at a college that exclusively serves students with LD, ADHD, and autism to gather preliminary evidence of its effectiveness in teaching statistics concepts to that population. The results of this research and the emergent recommendations to help students with disabilities gain access to statistics are described in this article. Supplementary materials for this article are available online.
{"title":"Teaching Statistics to Struggling Students: Lessons Learned from Students with LD, ADHD, and Autism","authors":"Ibrahim Dahlstrom‐Hakki, Michelle L. Wallace","doi":"10.1080/26939169.2022.2082601","DOIUrl":"https://doi.org/10.1080/26939169.2022.2082601","url":null,"abstract":"Abstract There have been significant developments in the field of statistics education over the past decade that have improved outcomes for all students. However, there remains relatively little research on the best practices for teaching statistics to students with disabilities. This article describes a conceptual visual approach to teaching a college level general education statistics course aimed at addressing the needs of students with disabilities and other struggling students. The conceptual visual components were employed using the technology tool TinkerPlots. The approach is informed by the recommendations of the GAISE report as well as research on Universal Design and Cognitive Load Theory. With support from the NSF (HRD-1128948), the approach was pilot tested at a college that exclusively serves students with LD, ADHD, and autism to gather preliminary evidence of its effectiveness in teaching statistics concepts to that population. The results of this research and the emergent recommendations to help students with disabilities gain access to statistics are described in this article. Supplementary materials for this article are available online.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 1","pages":"127 - 137"},"PeriodicalIF":1.7,"publicationDate":"2022-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45593991","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 : 2022-05-04DOI: 10.1080/26939169.2022.2075161
Allan Rossman, Bob delMas
RD: At age 18, I was in my first and second years of college as an undergraduate at the University of Minnesota. I always had a strong interest in science and an aptitude for mathematics. I was someone that my fellow classmates in grade school and high school sought out for help in these areas, so I also demonstrated an ability to explain and guide in understandable ways. When I considered going to college, I initially intended to major in a science area that combined my academic interests, such as biochemistry. However, I came to realize that I was very much interested in a type of epistemological question: Why do I and others seem able to learn science and mathematics, whereas others find these disciplines challenging? This led me to declare Psychology as a major as a first-year student, and then switch to Child Development in my sophomore year, a decision that set the stage for my academic future.
{"title":"Interview with Bob delMas","authors":"Allan Rossman, Bob delMas","doi":"10.1080/26939169.2022.2075161","DOIUrl":"https://doi.org/10.1080/26939169.2022.2075161","url":null,"abstract":"RD: At age 18, I was in my first and second years of college as an undergraduate at the University of Minnesota. I always had a strong interest in science and an aptitude for mathematics. I was someone that my fellow classmates in grade school and high school sought out for help in these areas, so I also demonstrated an ability to explain and guide in understandable ways. When I considered going to college, I initially intended to major in a science area that combined my academic interests, such as biochemistry. However, I came to realize that I was very much interested in a type of epistemological question: Why do I and others seem able to learn science and mathematics, whereas others find these disciplines challenging? This led me to declare Psychology as a major as a first-year student, and then switch to Child Development in my sophomore year, a decision that set the stage for my academic future.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 1","pages":"191 - 197"},"PeriodicalIF":1.7,"publicationDate":"2022-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47757520","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 : 2022-04-19DOI: 10.1080/26939169.2022.2058655
Gita Taasoobshirazi, M. Wagner, A. Brown, Colene Copeland
Abstract The widely used Draw a Scientist Test was revised to focus on statistics and 110 Elementary Statistics students were asked to draw a statistician. In addition, to better understand students’ drawings and have some relative comparison, 173 College Algebra students were asked to draw a mathematician. A detailed analysis of students’ images and students’ demographic information was conducted using descriptive statistics, categorical data analysis, logistic regressions, and hierarchical cluster analysis. Results showed that students tend to perceive statisticians and mathematicians as primarily White and male. However, female students were more likely than male students to draw a female statistician and mathematician. Two themed clusters emerged from the hierarchical cluster analysis for both the math and statistics students. We discuss the implications of the results for teaching and future research.
{"title":"An Evaluation of College Students’ Perceptions of Statisticians","authors":"Gita Taasoobshirazi, M. Wagner, A. Brown, Colene Copeland","doi":"10.1080/26939169.2022.2058655","DOIUrl":"https://doi.org/10.1080/26939169.2022.2058655","url":null,"abstract":"Abstract The widely used Draw a Scientist Test was revised to focus on statistics and 110 Elementary Statistics students were asked to draw a statistician. In addition, to better understand students’ drawings and have some relative comparison, 173 College Algebra students were asked to draw a mathematician. A detailed analysis of students’ images and students’ demographic information was conducted using descriptive statistics, categorical data analysis, logistic regressions, and hierarchical cluster analysis. Results showed that students tend to perceive statisticians and mathematicians as primarily White and male. However, female students were more likely than male students to draw a female statistician and mathematician. Two themed clusters emerged from the hierarchical cluster analysis for both the math and statistics students. We discuss the implications of the results for teaching and future research.","PeriodicalId":34851,"journal":{"name":"Journal of Statistics and Data Science Education","volume":"30 1","pages":"138 - 153"},"PeriodicalIF":1.7,"publicationDate":"2022-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44145770","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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