Pub Date : 2021-03-01DOI: 10.5038/1936-4660.14.2.1392
W. Briggs
A teacher looks back on three decades of teaching, pondering, and writing about quantitative reasoning (QR) and shares a few lessons learned. The skills that we teach in QR courses are more important than ever in providing students with a sense of civic virtue: the ability to be engaged and informed citizens in an increasingly complex and quantitative world.
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Pub Date : 2021-01-01DOI: 10.5038/1936-4660.14.1.1356
Luc Arrondel
This article looks back over the different dimensions of financial literacy: theoretical, methodological, empirical and political. The theoretical foundations of the notion of financial literacy are presented with reference to recent contributions by psychological or behavioural economics: “household finance” refers to the concept of financial literacy based on the empirical dead-ends of standard saver theory. This raises the fundamental question as to how to measure and evaluate financial literacy. Here, we are especially interested in the empirical robustness of a standard measure of financial literacy based on three straightforward questions (interest calculations, notion of inflation and risk diversification). Is this measure adequate or do other definitions need to be developed? We use original data from a survey conducted in 2017 that proposes alternative measures. Our results show that the measure in most studies seems a good proxy for a more global measure based on a larger battery of similar questions. Nevertheless, the global measure improves the statistical quality of the measure even though this more sophisticated measure does not statistically significantly improve behavioural regressions.
{"title":"Financial Literacy and French Behaviour on the Stock Market","authors":"Luc Arrondel","doi":"10.5038/1936-4660.14.1.1356","DOIUrl":"https://doi.org/10.5038/1936-4660.14.1.1356","url":null,"abstract":"This article looks back over the different dimensions of financial literacy: theoretical, methodological, empirical and political. The theoretical foundations of the notion of financial literacy are presented with reference to recent contributions by psychological or behavioural economics: “household finance” refers to the concept of financial literacy based on the empirical dead-ends of standard saver theory. This raises the fundamental question as to how to measure and evaluate financial literacy. Here, we are especially interested in the empirical robustness of a standard measure of financial literacy based on three straightforward questions (interest calculations, notion of inflation and risk diversification). Is this measure adequate or do other definitions need to be developed? We use original data from a survey conducted in 2017 that proposes alternative measures. Our results show that the measure in most studies seems a good proxy for a more global measure based on a larger battery of similar questions. Nevertheless, the global measure improves the statistical quality of the measure even though this more sophisticated measure does not statistically significantly improve behavioural regressions.","PeriodicalId":36166,"journal":{"name":"Numeracy","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70470847","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 : 2021-01-01DOI: 10.5038/1936-4660.14.1.1389
M. Schield
National Numeracy Network Officers and Board of Directors in the year 2020.
国家计算网络官员和董事会成员。
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Pub Date : 2021-01-01DOI: 10.5038/1936-4660.14.1.1357
P. Walter, E. Nuhfer, Crisel Suárez
We introduce an approach for making a quantitative comparison of the item response curves (IRCs) of any two populations on a multiple-choice test instrument. In this study, we employ simulated and actual data. We apply our approach to a dataset of 12,187 participants on the 25-item Science Literacy Concept Inventory (SLCI), which includes ample demographic data of the participants. Prior comparisons of the IRCs of different populations addressed only two populations and were made by visual inspection. Our approach allows for quickly comparing the IRCs for many pairs of populations to identify those items where substantial differences exist. For each item, we compute the IRC dot product, a number between 0 and 1 for which a value of 1 occurs when the IRCs of the two populations are identical. We then determine whether the value of the IRC dot product is indicative of significant differences in populations of real students. Through this process, we can quickly discover bias across demographic groups. As a case example, we apply our metric to illuminate four SLCI items that exhibit gender bias. We further found that gender bias was present for non-science majors on those items but not for science majors.
{"title":"Probing for Bias: Comparing Populations Using Item Response Curves","authors":"P. Walter, E. Nuhfer, Crisel Suárez","doi":"10.5038/1936-4660.14.1.1357","DOIUrl":"https://doi.org/10.5038/1936-4660.14.1.1357","url":null,"abstract":"We introduce an approach for making a quantitative comparison of the item response curves (IRCs) of any two populations on a multiple-choice test instrument. In this study, we employ simulated and actual data. We apply our approach to a dataset of 12,187 participants on the 25-item Science Literacy Concept Inventory (SLCI), which includes ample demographic data of the participants. Prior comparisons of the IRCs of different populations addressed only two populations and were made by visual inspection. Our approach allows for quickly comparing the IRCs for many pairs of populations to identify those items where substantial differences exist. For each item, we compute the IRC dot product, a number between 0 and 1 for which a value of 1 occurs when the IRCs of the two populations are identical. We then determine whether the value of the IRC dot product is indicative of significant differences in populations of real students. Through this process, we can quickly discover bias across demographic groups. As a case example, we apply our metric to illuminate four SLCI items that exhibit gender bias. We further found that gender bias was present for non-science majors on those items but not for science majors.","PeriodicalId":36166,"journal":{"name":"Numeracy","volume":"14 1","pages":"2"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70470930","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 : 2021-01-01DOI: 10.5038/1936-4660.14.1.1376
M. Lewis
Lewis, Michael Anthony. 2019. Social Workers Count: Numbers and Social Issues (New York: Oxford University Press) 224 pp. ISBN 978-0190467135. This essay introduces Social Workers Count: Numbers and Social Issues by Michael Anthony Lewis. Inspired by the seminal work of Bennett and Briggs, Lewis shares how he came to write a math book for social workers to meet new demands as the field has developed to include more quantitative concepts. The result is a book that may be of interest to many in the quantitative reasoning movement in the social sciences and beyond.
{"title":"How Social Workers Count: Numbers and Social Issues Came to Be","authors":"M. Lewis","doi":"10.5038/1936-4660.14.1.1376","DOIUrl":"https://doi.org/10.5038/1936-4660.14.1.1376","url":null,"abstract":"Lewis, Michael Anthony. 2019. Social Workers Count: Numbers and Social Issues (New York: Oxford University Press) 224 pp. ISBN 978-0190467135. This essay introduces Social Workers Count: Numbers and Social Issues by Michael Anthony Lewis. Inspired by the seminal work of Bennett and Briggs, Lewis shares how he came to write a math book for social workers to meet new demands as the field has developed to include more quantitative concepts. The result is a book that may be of interest to many in the quantitative reasoning movement in the social sciences and beyond.","PeriodicalId":36166,"journal":{"name":"Numeracy","volume":"14 1","pages":"9"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70471585","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 : 2021-01-01DOI: 10.5038/1936-4660.14.1.1379
Ellen Peters
Peters, E. (2020). Innumeracy in the Wild: Misunderstanding and Misusing Numbers. (New York, NY: Oxford University Press) 315 pp. ISBN 978-0190861094 This piece briefly introduces and excerpts Innumeracy in the Wild: Misunderstanding and Misusing Numbers, written by Ellen Peters and published by Oxford University Press. Through a state-of-art review of the literature, the book explains how numeric ability supports the quality of the decisions people make and the life outcomes they experience. It presents three ways that people can be good or bad with numbers and how each of these numeric competencies matter to decision making.
{"title":"Reflections on Innumeracy in the Wild","authors":"Ellen Peters","doi":"10.5038/1936-4660.14.1.1379","DOIUrl":"https://doi.org/10.5038/1936-4660.14.1.1379","url":null,"abstract":"Peters, E. (2020). Innumeracy in the Wild: Misunderstanding and Misusing Numbers. (New York, NY: Oxford University Press) 315 pp. ISBN 978-0190861094\u0000This piece briefly introduces and excerpts Innumeracy in the Wild: Misunderstanding and Misusing Numbers, written by Ellen Peters and published by Oxford University Press. Through a state-of-art review of the literature, the book explains how numeric ability supports the quality of the decisions people make and the life outcomes they experience. It presents three ways that people can be good or bad with numbers and how each of these numeric competencies matter to decision making.","PeriodicalId":36166,"journal":{"name":"Numeracy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47018727","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 : 2021-01-01DOI: 10.5038/1936-4660.14.1.1382
Charles B. Connor
The COVID-19 pandemic has led many people to form social bubbles. These social bubbles are small groups of people who interact with one another but restrict interactions with the outside world. The assumption in forming social bubbles is that risk of infection and severe outcomes, like hospitalization, are reduced. How effective are social bubbles? A Bayesian event tree is developed to calculate the probabilities of specific outcomes, like hospitalization, using example rates of infection in the greater community and example prior functions describing the effectiveness of isolation by members of the social bubble. The probabilities are solved for two contrasting examples: members of an assisted living facility and members of a classroom, including their teacher. A web-based calculator is provided so readers can experiment with the Bayesian event tree and learn more about these probabilities by modeling their own social bubble.
{"title":"Computing for Numeracy: How Safe is Your COVID-19 Social Bubble?","authors":"Charles B. Connor","doi":"10.5038/1936-4660.14.1.1382","DOIUrl":"https://doi.org/10.5038/1936-4660.14.1.1382","url":null,"abstract":"The COVID-19 pandemic has led many people to form social bubbles. These social bubbles are small groups of people who interact with one another but restrict interactions with the outside world. The assumption in forming social bubbles is that risk of infection and severe outcomes, like hospitalization, are reduced. How effective are social bubbles? A Bayesian event tree is developed to calculate the probabilities of specific outcomes, like hospitalization, using example rates of infection in the greater community and example prior functions describing the effectiveness of isolation by members of the social bubble. The probabilities are solved for two contrasting examples: members of an assisted living facility and members of a classroom, including their teacher. A web-based calculator is provided so readers can experiment with the Bayesian event tree and learn more about these probabilities by modeling their own social bubble.","PeriodicalId":36166,"journal":{"name":"Numeracy","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47869506","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 : 2021-01-01DOI: 10.5038/1936-4660.14.1.1373
Jennifer Clinkenbeard
In Fall 2018, remedial mathematics courses were eliminated from the 23-campus California State University system under Executive Order 1110. Incoming first-year students were placed into college credit-bearing mathematics courses with options for corequisite support. This study examines the academic outcomes for students at California State University Monterey Bay in a college credit level quantitative literacy (QL) mathematics course with optional corequisite support during the 2018-2019 academic year. Taken together, the results of this study suggest that required remediation is not necessary for success in college-level QL. The corequisite support model also has potential to support more equitable outcomes for all students. However, further study is needed to identify institutional, departmental, and pedagogical best practices for effective corequisite support in QL.
{"title":"Course Design and Academic Outcomes in Quantitative Literacy After Eliminating Required Remediation","authors":"Jennifer Clinkenbeard","doi":"10.5038/1936-4660.14.1.1373","DOIUrl":"https://doi.org/10.5038/1936-4660.14.1.1373","url":null,"abstract":"In Fall 2018, remedial mathematics courses were eliminated from the 23-campus California State University system under Executive Order 1110. Incoming first-year students were placed into college credit-bearing mathematics courses with options for corequisite support. This study examines the academic outcomes for students at California State University Monterey Bay in a college credit level quantitative literacy (QL) mathematics course with optional corequisite support during the 2018-2019 academic year. Taken together, the results of this study suggest that required remediation is not necessary for success in college-level QL. The corequisite support model also has potential to support more equitable outcomes for all students. However, further study is needed to identify institutional, departmental, and pedagogical best practices for effective corequisite support in QL.","PeriodicalId":36166,"journal":{"name":"Numeracy","volume":"90 1","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70471480","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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