In this paper, we detail a convergent mixed-methods survey of faculty teaching applied social statistics courses, analyzed through a framework of learning theory. The survey explored faculty perceptions and use of recommended strategies for statistics education, perceived barriers to student success, and general recommendations for instruction in applied statistics courses. Results indicate some consistency in practice with the recommendations in the literature, but a lack of consistency was noted for strategies such as student reflections, allowing multiple attempts on assignments, and others. Particular attention is paid to connecting the results to learning theory with the discussion framed around a Statistics Self-Actualization model based on Maslow’s 1943 theory of Hierarchy of Needs. First published June 2020 at Statistics Education Research Journal Archives
{"title":"ACHIEVING STATISTICS SELF-ACTUALIZATION: FACULTY SURVEY ON TEACHING APPLIED SOCIAL STATISTICS","authors":"S. Ferguson, MaryBeth Walpole, Madjiguene Fall","doi":"10.52041/SERJ.V19I2.110","DOIUrl":"https://doi.org/10.52041/SERJ.V19I2.110","url":null,"abstract":"In this paper, we detail a convergent mixed-methods survey of faculty teaching applied social statistics courses, analyzed through a framework of learning theory. The survey explored faculty perceptions and use of recommended strategies for statistics education, perceived barriers to student success, and general recommendations for instruction in applied statistics courses. Results indicate some consistency in practice with the recommendations in the literature, but a lack of consistency was noted for strategies such as student reflections, allowing multiple attempts on assignments, and others. Particular attention is paid to connecting the results to learning theory with the discussion framed around a Statistics Self-Actualization model based on Maslow’s 1943 theory of Hierarchy of Needs. \u0000First published June 2020 at Statistics Education Research Journal Archives","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48875309","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}
F. Ruz, B. Chance, Elsa Medina, José Miguel Contreras
Recognizing that mathematics teachers are facing increasing demands in the teaching of probability and statistics (stochastics) at school level, we are interested in analyzing current pre-service teachers’ dispositions about content knowledge and attitudes towards the teaching and learning of stochastics. We implemented a quantitative study for a sample of 269 pre-service Chilean mathematics teachers to determine their understanding of stochastics content, their attitudes towards stochastics and its teaching, and whether these are related. We found weak associations overall, but stronger for some components. We conclude with recommendations based on these results to improve the Chilean teachers’ preparation process (pre-service) and advice that could guide the professional development of teachers (in-service).
{"title":"CONTENT KNOWLEDGE AND ATTITUDES TOWARDS STOCHASTICS AND ITS TEACHING IN PRE-SERVICE CHILEAN MATHEMATICS TEACHERS","authors":"F. Ruz, B. Chance, Elsa Medina, José Miguel Contreras","doi":"10.52041/serj.v20i1.100","DOIUrl":"https://doi.org/10.52041/serj.v20i1.100","url":null,"abstract":"Recognizing that mathematics teachers are facing increasing demands in the teaching of probability and statistics (stochastics) at school level, we are interested in analyzing current pre-service teachers’ dispositions about content knowledge and attitudes towards the teaching and learning of stochastics. We implemented a quantitative study for a sample of 269 pre-service Chilean mathematics teachers to determine their understanding of stochastics content, their attitudes towards stochastics and its teaching, and whether these are related. We found weak associations overall, but stronger for some components. We conclude with recommendations based on these results to improve the Chilean teachers’ preparation process (pre-service) and advice that could guide the professional development of teachers (in-service).","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44990450","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}
With the purpose of developing an instrument for measuring statistics anxiety in the online or hybrid setting, this study tested the newly developed instrument in two stages. Results on item selection and exploratory factor analysis based on pilot testing (n = 115) are presented. Results on classical item analysis, the confirmatory factor analysis, the measurement invariance test results, and the predictive and discriminant validity of the final model based on formal testing (n = 709) are presented. The resulting Statistics Anxiety Scale in the Online or Hybrid setting instrument (SASOH) has 27 items and four dimensions. The four dimensions are Class and Interpretation Anxiety (CI), Fear of Asking for Help Anxiety (FA), Online System Anxiety (OS), and Pre-Conception Anxiety (PC). The results of the confirmatory factor analysis revealed that the four-factor SASOH model represents an adequate description of statistics anxiety in an online or hybrid setting. Moreover, multiple-groups confirmatory factor analysis affirmed that the resulting model achieved at least partial measurement and structural invariance across gender and program. In addition, attitudes toward statistics significantly predicts the four factors of statistics anxiety, and the discriminant validity from mathematics anxiety was confirmed. Recommendations for future studies are also provided.
{"title":"ASSESSING STATISTICS ANXIETY IN AN ONLINE OR HYBRID SETTING: THE ADAPTION AND DEVELOPMENT OF A NEW INSTRUMENT SASOH","authors":"Lu Liu","doi":"10.52041/serj.v20i1.96","DOIUrl":"https://doi.org/10.52041/serj.v20i1.96","url":null,"abstract":"With the purpose of developing an instrument for measuring statistics anxiety in the online or hybrid setting, this study tested the newly developed instrument in two stages. Results on item selection and exploratory factor analysis based on pilot testing (n = 115) are presented. Results on classical item analysis, the confirmatory factor analysis, the measurement invariance test results, and the predictive and discriminant validity of the final model based on formal testing (n = 709) are presented. The resulting Statistics Anxiety Scale in the Online or Hybrid setting instrument (SASOH) has 27 items and four dimensions. The four dimensions are Class and Interpretation Anxiety (CI), Fear of Asking for Help Anxiety (FA), Online System Anxiety (OS), and Pre-Conception Anxiety (PC). The results of the confirmatory factor analysis revealed that the four-factor SASOH model represents an adequate description of statistics anxiety in an online or hybrid setting. Moreover, multiple-groups confirmatory factor analysis affirmed that the resulting model achieved at least partial measurement and structural invariance across gender and program. In addition, attitudes toward statistics significantly predicts the four factors of statistics anxiety, and the discriminant validity from mathematics anxiety was confirmed. Recommendations for future studies are also provided.","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70657528","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}
This article develops an analytical framework for analysing college (tertiary) statistics textbooks in terms of text accessibility by integrating the text, the reader, and the content into the framework. Five accessibility attributes of science texts were adapted to conceptualize the accessibility of statistics texts. For each accessibility attribute, two components were proposed by referring to the literature on the readability of mathematics texts as well as the characteristics of statistics. The feasibility of the framework is demonstrated by analysing sample statistics texts. The contributions and potential of the framework are discussed.
{"title":"CONCEPTUALIZING A FRAMEWORK FOR ANALYSING COLLEGE STATISTICS TEXTBOOKS IN TERMS OF TEXT ACCESSIBILITY","authors":"Kai-Lin Yang, Khairiani Idris","doi":"10.52041/serj.v20i1.102","DOIUrl":"https://doi.org/10.52041/serj.v20i1.102","url":null,"abstract":"This article develops an analytical framework for analysing college (tertiary) statistics textbooks in terms of text accessibility by integrating the text, the reader, and the content into the framework. Five accessibility attributes of science texts were adapted to conceptualize the accessibility of statistics texts. For each accessibility attribute, two components were proposed by referring to the literature on the readability of mathematics texts as well as the characteristics of statistics. The feasibility of the framework is demonstrated by analysing sample statistics texts. The contributions and potential of the framework are discussed.","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41886564","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}
This study investigated whether basic mathematics skills are associated with undergraduate psychology statistics course performance while simultaneously considering self-reported psychological/behavioral and demographic variables. Participants (n = 460) completed a Math Assessment for College Students (MACS), which included questions ranging from calculating percentages to graphical interpretation. The researchers used a discriminant correspondence analysis to reveal differences in course performance evaluated as the average of three exam grades. For the variation in the average exam scores accounted for by our model, the MACS scores provided the largest contribution. Other variables associated with better exam grades included white ethnicity, non-transfer status, lower year in school, and low procrastination. The researchers discuss the implications for helping instructors identify areas of basic mathematical deficiency and strength.
{"title":"CORRELATES OF EXAM PERFORMANCE IN AN INTRODUCTORY STATISTICS COURSE: BASIC MATH SKILLS ALONG WITH SELF-REPORTED PSYCHOLOGICAL/BEHAVIORAL AND DEMOGRAPHIC VARIABLES","authors":"L. Rabin, Anjali Krishnan, R. Bergdoll, J. Fogel","doi":"10.52041/serj.v20i1.97","DOIUrl":"https://doi.org/10.52041/serj.v20i1.97","url":null,"abstract":"This study investigated whether basic mathematics skills are associated with undergraduate psychology statistics course performance while simultaneously considering self-reported psychological/behavioral and demographic variables. Participants (n = 460) completed a Math Assessment for College Students (MACS), which included questions ranging from calculating percentages to graphical interpretation. The researchers used a discriminant correspondence analysis to reveal differences in course performance evaluated as the average of three exam grades. For the variation in the average exam scores accounted for by our model, the MACS scores provided the largest contribution. Other variables associated with better exam grades included white ethnicity, non-transfer status, lower year in school, and low procrastination. The researchers discuss the implications for helping instructors identify areas of basic mathematical deficiency and strength.","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46571182","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}
T. Esnard, Fareena M. Alladin, Keisha Chandra Samlal
The objectives of this study were to examine (i) the expectation for performance (EFP) in statistics of first year university students, (ii) the relative effect of previous mathematics performance (PMP), perceived statistical self-efficacy (SSE), and statistics anxiety (SA) for understanding these expectations, and (iii) whether students’ EFP scores differ based on sex and academic discipline. Findings point to average to high EFP in statistics, with no differences in these levels based on sex or academic discipline. PMP had little effect on students’ EFP, but, moderate effects on their levels of SA. While SSE positively affected both students’ SA, this measure produced a negative effect on their EFP in statistics. Both SSE and SA negatively affected students’ EFP in statistics, but with minimally higher levels for the latter.
{"title":"PRIOR MATHEMATICS PERFORMANCE, STATISTICS ANXIETY, SELF-EFFICACY AND EXPECTATIONS FOR PERFORMANCE IN STATISTICS: A SURVEY OF SOCIAL SCIENCES STUDENTS IN A CARIBBEAN INSTITUTION OF HIGHER EDUCATION","authors":"T. Esnard, Fareena M. Alladin, Keisha Chandra Samlal","doi":"10.52041/serj.v20i1.98","DOIUrl":"https://doi.org/10.52041/serj.v20i1.98","url":null,"abstract":"The objectives of this study were to examine (i) the expectation for performance (EFP) in statistics of first year university students, (ii) the relative effect of previous mathematics performance (PMP), perceived statistical self-efficacy (SSE), and statistics anxiety (SA) for understanding these expectations, and (iii) whether students’ EFP scores differ based on sex and academic discipline. Findings point to average to high EFP in statistics, with no differences in these levels based on sex or academic discipline. PMP had little effect on students’ EFP, but, moderate effects on their levels of SA. While SSE positively affected both students’ SA, this measure produced a negative effect on their EFP in statistics. Both SSE and SA negatively affected students’ EFP in statistics, but with minimally higher levels for the latter.","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42893908","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}
We sought to evaluate the SASTSc in two samples of students taking a statistics course that incorporates statistical software. The SASTSc was given at two time points, once at the beginning of the semester and then again at the end of the semester. Our evaluation included examining competing factor analytic models, examining convergent validity, test-retest reliability, and assessing internal consistency. Our results in both samples replicate the scale’s proposed factor structure; however, not all items were useful and we propose some changes to the wording of items to improve the scale.
{"title":"Psychometric Evaluation of the Students’ Attitudes Toward Statistics and Technology Scale (SASTSc)","authors":"A. Counsell, J. Rovetti, E. Buchanan","doi":"10.31234/OSF.IO/469SE","DOIUrl":"https://doi.org/10.31234/OSF.IO/469SE","url":null,"abstract":"We sought to evaluate the SASTSc in two samples of students taking a statistics course that incorporates statistical software. The SASTSc was given at two time points, once at the beginning of the semester and then again at the end of the semester. Our evaluation included examining competing factor analytic models, examining convergent validity, test-retest reliability, and assessing internal consistency. Our results in both samples replicate the scale’s proposed factor structure; however, not all items were useful and we propose some changes to the wording of items to improve the scale.","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42316282","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}
This study examines informal hypothesis testing in the context of drawing inferences of underlying probability distributions. Through a small-scale teaching experiment of three lessons, the study explores how fifth-grade students distinguish a non-uniform probability distribution from uniform probability distributions in a data-rich learning environment, and what role processes of data production play in their investigations. The study outlines aspects of students’ informal understanding of hypothesis testing. It shows how students with no formal education can follow the logic that a small difference in samples can be the effect of randomness, while a large difference implies a real difference in the underlying process. The students distinguish the mode and the size of differences in frequencies as signals in data and used these signals to give data-based reasons in processes of informal hypothesis testing. The study also highlights the role of data production and points to a need for further research on the role of data production in an informal approach to the teaching and learning of statistical inference. First published December 2020 at Statistics Education Research Journal: Archives
{"title":"STUDENTS’ INFORMAL HYPOTHESIS TESTING IN A PROBABILITY CONTEXT WITH CONCRETE RANDOM GENERATORS","authors":"Per Nilsson","doi":"10.52041/SERJ.V19I3.56","DOIUrl":"https://doi.org/10.52041/SERJ.V19I3.56","url":null,"abstract":"This study examines informal hypothesis testing in the context of drawing inferences of underlying probability distributions. Through a small-scale teaching experiment of three lessons, the study explores how fifth-grade students distinguish a non-uniform probability distribution from uniform probability distributions in a data-rich learning environment, and what role processes of data production play in their investigations. The study outlines aspects of students’ informal understanding of hypothesis testing. It shows how students with no formal education can follow the logic that a small difference in samples can be the effect of randomness, while a large difference implies a real difference in the underlying process. The students distinguish the mode and the size of differences in frequencies as signals in data and used these signals to give data-based reasons in processes of informal hypothesis testing. The study also highlights the role of data production and points to a need for further research on the role of data production in an informal approach to the teaching and learning of statistical inference.\u0000First published December 2020 at Statistics Education Research Journal: Archives","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46878482","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}
In Spain, curricular guidelines as well as the university-entrance tests for social-science high-school students (17–18 years old) include sampling distributions. To analyse the understanding of this concept we investigated a sample of 234 students. We administered a questionnaire to them and ask half for justifications of their answers. The questionnaire consisted of four sampling tasks with two sample sizes (n = 100 and 10) and population proportions (equal or different to 0.5)systematically varied. The experiment gathered twofold data from the students simultaneously, namely about their perception of the mean and about their understanding of variation of the sampling distribution. The analysis of students’ responses indicates a good understanding of the relationship between the theoretical proportion in the population and the sample proportion. Sampling variability, however, was overestimated in bigger samples. We also observed various types of biased thinking in the students: the equiprobability and recency biases, as well as deterministic pre-conceptions. The effect of the task variables on the students’ responses is also discussed here. First published December 2020 at Statistics Education Research Journal: Archives
{"title":"WAYS IN WHICH HIGH-SCHOOL STUDENTS UNDERSTAND THE SAMPLING DISTRIBUTION FOR PROPORTIONS","authors":"C. Batanero, Nuria Begué, M. Borovcnik, M. Gea","doi":"10.52041/SERJ.V19I3.55","DOIUrl":"https://doi.org/10.52041/SERJ.V19I3.55","url":null,"abstract":"In Spain, curricular guidelines as well as the university-entrance tests for social-science high-school students (17–18 years old) include sampling distributions. To analyse the understanding of this concept we investigated a sample of 234 students. We administered a questionnaire to them and ask half for justifications of their answers. The questionnaire consisted of four sampling tasks with two sample sizes (n = 100 and 10) and population proportions (equal or different to 0.5)systematically varied. The experiment gathered twofold data from the students simultaneously, namely about their perception of the mean and about their understanding of variation of the sampling distribution. The analysis of students’ responses indicates a good understanding of the relationship between the theoretical proportion in the population and the sample proportion. Sampling variability, however, was overestimated in bigger samples. We also observed various types of biased thinking in the students: the equiprobability and recency biases, as well as deterministic pre-conceptions. The effect of the task variables on the students’ responses is also discussed here.\u0000First published December 2020 at Statistics Education Research Journal: Archives","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41991624","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}
A central challenge of introductory statistics is the development of curricula that not only serve diverse students, but also leave them wanting more. To evaluate the potential impact of a multidisciplinary, project-based introductory statistics course, students’ future course decisions were compared against traditional statistics courses using administrative data from the fall 2009 through spring 2018 semesters. Results indicated that the project-based course helped promote continued interest in the field of statistics and data analysis based on subsequent selection of courses in the field. First published December 2020 at Statistics Education Research Journal: Archives
{"title":"COMPARISON OF FUTURE COURSE ENROLLMENT AMONG STUDENTS COMPLETING ONE OF FOUR DIFFERENT INTRODUCTORY STATISTICS COURSES","authors":"Valerie L. Nazzaro, J. Rose, L. Dierker","doi":"10.52041/SERJ.V19I3.53","DOIUrl":"https://doi.org/10.52041/SERJ.V19I3.53","url":null,"abstract":"A central challenge of introductory statistics is the development of curricula that not only serve diverse students, but also leave them wanting more. To evaluate the potential impact of a multidisciplinary, project-based introductory statistics course, students’ future course decisions were compared against traditional statistics courses using administrative data from the fall 2009 through spring 2018 semesters. Results indicated that the project-based course helped promote continued interest in the field of statistics and data analysis based on subsequent selection of courses in the field.\u0000First published December 2020 at Statistics Education Research Journal: Archives","PeriodicalId":38581,"journal":{"name":"Statistics Education Research Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47415998","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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