A Survey on How College Students in a Statistical Literacy Course Apply Statistics Terms to People

References

• American Psychological Association (APA). (2020), Publication Manual of the American Psychological Association (7th ed.), Washington, DC: APA. DOI: 10.1037/0000165-000.
   Google Scholar
• American Statistical Association. (2005), “Guidelines for Assessment and Instruction in Statistics Education: GAISE College Report,” available at https://www.amstat.org/docs/default-source/amstat-documents/2005gaisecollege_full.pdf
   Google Scholar
• Artigli, G. G., Gualano, B., Franchini, E., Scagliusa, F. B., Takesian, M., Fuchs, M., et al. (2010), “Prevalence, Magnitude, and Methods of Rapid Weight Loss Among Judo Competitors,” Medicine and Science in Sports and Exercise, 42, 436–442. DOI: 10.1249/MSS.0b013e3181ba8055.
   PubMed Web of Science ®Google Scholar
• Bargagliotti, A., Franklin, C., Arnold, P., Gould, R., Johnson, S., Perez, L., and Spangler, D. A. (2020), Pre-K-12 Guidelines for Assessment and Instruction in Statistics Education II (GAISE II): a Framework for Statistics and Data Science Education, Alexandria, VA: American Statistical Association. https://www.amstat.org/asa/files/pdfs/GAISE/GAISEIIPreK-12_Full.pdf
   Google Scholar
• Bartlett, B. (2020), “The Right’s Farcical Denial of Systemic Racism,” available at https://newrepublic.com/article/159589/conservatives-systemic-racism-denial
   Google Scholar
• Bauer, L., and Calude, A. S. (2020), Questions about Language: What Everyone Should Know about Language in the 21st Century, New York: Routledge.
   Google Scholar
• Begley, S. (2009), “Pink Brain, Blue Brain: Claims of Sex Differences Fall Apart,” Newsweek, 154, 28.
   Google Scholar
• Behara, D. N., and Das, B. (2012), “Structural Anthropometric Measurements of the Canadian Adult Population: The Fallacy of the ‘Average Person’ Concept,” Theoretical Issues in Ergonomics Science, 13, 380–392. DOI: 10.1080/1463922X.2010.505271.
   Google Scholar
• Ben-Zvi, D., Aridor, K., Makar, K., and Bakker, A. (2012), “Students’ Emergent Articulations of Uncertainty while Making Informal Statistical Inferences,” ZDM Mathematics Education, 44, 913–925. DOI: 10.1007/s11858-012-0420-3.
   Google Scholar
• Best, J. (2002), “People Count: The Social Construction of Statistics,” in Proceedings of the Joint Statistical Meetings, Section on Statistical Education, Alexandria, VA: American Statistical Association. http://statlit.org/pdf/2002BestASA.pdf
   Google Scholar
• Beyth-Marom, R., Fidler, F., and Cumming, G. (2008), “Statistical Cognition: Towards Evidence-based Practice in Statistics and Statistics Education,” Statistics Education Research Journal, 7, 20–39. https://iase-web.org/documents/SERJ/SERJ7(2)_Beyth-Maron.pdf DOI: 10.52041/serj.v7i2.468.
   Google Scholar
• Biehler, R., Frischemeier, D., Reading, C., and Shaughnessy, J. M. (2018), “Reasoning about Data,” in International Handbook of Research in Statistics Education, eds. D. Ben-Zvi, J. Garfield, and K. Makar, pp. 139–193, Cham, Switzerland: Springer International.
   Google Scholar
• Bowen, C. M. K. (2022), “The Art of Data Privacy,” Significance, 19, 14–19. DOI: 10.1111/1740-9713.01608.
   Google Scholar
• Campos, M. M. (2006), “Finding the Most Typical Record in a Group,” available at https://oracledmt.blogspot.com/2006/07/finding-most-typical-record-in-group.html
   Google Scholar
• Carpenter, T. P., Dossey, J. A., and Koehler, J. L., eds. (2004), Classics in Mathematics Education Research, Reston, VA: National Council of Teachers of Mathematics.
   Google Scholar
• Choudhury, D. R., and Das, M. N. (1992), “Use of Combinatorics for Unique Detection of Unknown Numbers of Outliers Using Group Tests,” Sankhyā: The Indian Journal of Statistics, Series B, 54, 92–99.
   Google Scholar
• Cleveland, W. S. (1994), The Elements of Graphing Data, Murray Hill, NJ: AT&T Bell Laboratories.
   Google Scholar
• Dashley, T. K. (2010), “Using Prediction And Classroom Voting Via Clickers To Address Secondary School Students’ Overreliance On The Representativeness Heuristic,” unpublished master’s thesis, The University of Texas at El Paso. Open Access Theses & Dissertations. 2667.
   Google Scholar
• Durkin, K., and Shire, B. (1991), “Lexical Ambiguity in Mathematical Contexts,” in Language in Mathematical Education: Research and Practice, eds. K. Durkin and B. Shire, pp. 71–84, Philadelphia, PA: Open University Press.
   Google Scholar
• Fernandes, P. T., De Barros, N. F., and Li, L. M. (2009), “Stop Saying Epileptic,” Epilepsia, 50, 1280–1283. DOI: 10.1111/j.1528-1167.2008.01899.x.
   PubMed Web of Science ®Google Scholar
• Fischer, J., and Perez, R. (2008), “Understanding English through Mathematics: A Research-based ELL Approach to Teaching All Students,” Research Monograph of TODOS, 1, 43–58. https://www.todos-math.org/assets/documents/Monographs/1.monograph1new.pdf
   Google Scholar
• GAISE College Report ASA Revision Committee. (2016), “Guidelines for Assessment and Instruction in Statistics Education: College Report 2016,” available at https://www.amstat.org/education/gaise/
   Google Scholar
• Gephart, R. P. Jr., (1988), Ethnostatistics: Qualitative Foundations for Quantitative Research, Newbury Park, CA: Sage Publications.
   Google Scholar
• Gladwell, M. (2008), Outliers: The Story of Success, New York: Little, Brown and Company.
   Google Scholar
• Good, T. L., and Grouws, D. A. (1979), “The Missouri Mathematics Effectiveness Project: An Experimental Study in Fourth-Grade Classrooms,” Journal of Educational Psychology, 71, 355–362. DOI: 10.1037/0022-0663.71.3.355.
   Web of Science ®Google Scholar
• Hanel, P. H. P., and Vione, K. C. (2016), “Do Student Samples Provide an Accurate Estimate of the General Public?,” PLoS One, 11, e0168354-10. DOI: 10.1371/journal.pone.0168354.
   Web of Science ®Google Scholar
• Hemenway, D. (1982), “Why Your Classes are Larger than Average,” Mathematics Magazine, 55, 162–164. DOI: 10.2307/2690083.
   Google Scholar
• Henrich, J., Heine, S. J., and Norenzayan, A. (2010), “The Weirdest People in the World?,” Behavioral and Brain Sciences, 33, 61–83. DOI: 10.1017/S0140525X0999152X.
   PubMed Web of Science ®Google Scholar
• Heyard, R., and Held, L. (2022), “When Should Data and Code Be Made Available?,” Significance, 19, 4–5. DOI: 10.1111/1740-9713.01623.
   Google Scholar
• Hirsch, L. S., and O’Donnell, A. M. (2001), “Representativeness in Statistical Reasoning: Identifying and Assessing Misconceptions,” Journal of Statistics Education, 9, 1–22. DOI: 10.1080/10691898.2001.11910655.
   Google Scholar
• Holmes, P. (2006), “iPod Shuffle not Random,” Teaching Statistics, 28, 71–74. DOI: 10.1111/j.1467-9639.2006.00253.x.
   Google Scholar
• Huang, J., and Normandia, B. (2008), “Comprehending and Solving Word Problems in Mathematics: Beyond Key Words,” in Reading in the Secondary Content Areas, eds. Z. Fang and M. Schleppegrell, pp. 64–83, Ann Arbor, MI: The University of Michigan Press.
   Google Scholar
• Huberty, C. J. (2000), “Judgment in Quantitative Research,” The Mathematics Educator, 10, 5–10.
   Google Scholar
• Huck, S. W. (2012), Reading Statistics and Research (6th ed.), Boston: Pearson.
   Google Scholar
• Hulsizer, M. R., and Woolf, L. M. (2009), A Guide to Teaching Statistics: Innovations and Best Practices, Chichester, UK: Wiley-Blackwell.
   Google Scholar
• Jourdain, L., and Sharma, S. (2016), “Language Challenges in Mathematics Education: A Literature Review,” Waikato Journal of Education, 21, 43–56. DOI: 10.15663/wje.v21i2.269.
   Google Scholar
• Kader, G., Franklin, C., Jacobbe, T., and Maddox, K. (2020), “How Long Are the Words in the Gettysburg Address?” Statistics Teacher, available at https://www.statisticsteacher.org/2020/11/12/gettysburg-address/
   Google Scholar
• Kaplan, J. J., Fisher, D. G., and Rogness, N. T. (2010), “Lexical Ambiguity in Statistics: How Students Use and Define the Words: Association, Average, Confidence, Random and Spread,” Journal of Statistics Education, 18, 1–22. DOI: 10.1080/10691898.2010.11889491.
   Google Scholar
• Kaplan, J. J., Fisher, D. G., and Rogness, N. T. (2009), “Lexical Ambiguity in Statistics: What Do Students Know about the Words Association, Average, Confidence, Random, and Spread?,” Journal of Statistics Education, 17, 1–19. DOI: 10.1080/10691898.2009.11889535.
   PubMedGoogle Scholar
• Kaplan, J. J., Gabrosek, J. G., Curtiss, P., and Malone, C. (2014), “Investigating Student Understanding of Histograms,” Journal of Statistics Education, 22, 1–30. DOI: 10.1080/10691898.2014.11889701.
   Google Scholar
• Kaplan, J. J., Rogness, N. T., and Fisher, D. G. (2012), “Lexical Ambiguity: Making a Case Against Spread,” Teaching Statistics, 34, 56–60. DOI: 10.1111/j.1467-9639.2011.00477.x.
   Google Scholar
• Kaplan, J. J., Rogness, N. T., and Fisher, D. G. (2014), “Exploiting Lexical Ambiguity to Help Students Understand the Meaning of Random,” Statistics Education Research Journal, 13, 9–24. https://iase-web.org/ojs/SERJ/article/view/296/196. Also see https://qubeshub.org/community/groups/hilt_statistics/activity_page/zebra_hat
   Google Scholar
• Kelly, I. W., and Zwiers, F. W. (1986), “Mutually Exclusive and Independence: Unravelling Basic Misconceptions in Probability Theory,” in Proceedings of the Second International Conference on Teaching Statistics, eds. R. Davidson and J. Swift, pp. 96–100. Available at https://iase-web.org/documents/papers/icots2/Kelly.Zwiers.pdf
   Google Scholar
• Konold, C., Higgins, T., Russell, S. J., and Khalil, K. (2015), “Data Seen Through Different Lenses,” Educational Studies in Mathematics, 88, 305–325. DOI: 10.1007/s10649-013-9529-8.
   Web of Science ®Google Scholar
• Kouritzin, M. A., Newton, F., Orsten, S., and Wilson, D. C. (2008), “On Detecting Fake Coin Flip Sequences,” IMS Colleges Markov Processes and Related Topics: A Festschrift for Thomas G. Kurtz, 4, 107–122.
   Google Scholar
• Kramer, S. (2020), “The ‘Class Size Paradox’: How Individual- and Group-Level Perspectives Differ, and Why It Matters in Research,” available at https://medium.com/pew-research-center-decoded/the-class-size-paradox-how-individual-and-group-level-perspectives-differ-and-why-it-matters-b62071f4e909
   Google Scholar
• Lavy, I., and Mashiach-Eizenberg, M. (2009), “The Interplay between Spoken Language and Informal Definitions of Statistical Concepts,” Journal of Statistics Education, 17, 1–9. DOI: 10.1080/10691898.2009.11889502.
   Google Scholar
• Lee, C. (2019), “Welcome, Singular ‘They’, “American Psychological Association blogpost. Available at https://apastyle.apa.org/blog/singular-they
   Google Scholar
• Lesser, L. (2013), “Using Context to Classify Variables,” Teaching Statistics, 35, 29–31. DOI: 10.1111/j.1467-9639.2012.00527.x.
   Google Scholar
• Lesser, L., Wagler, A., Esquinca, A., and Valenzuela, M. G. (2013), “Survey of Native English Speakers and Spanish-Speaking English Language Learners in Tertiary Introductory Statistics,” Statistics Education Research Journal, 12, 6–31. https://iase-web.org/ojs/SERJ/article/view/302 DOI: 10.52041/serj.v12i2.302.
   Google Scholar
• Lesser, L., Wagler, A., and Salazar, B. (2016), “Flipping Between Languages? An Exploratory Analysis of the Usage by Spanish-Speaking English Language Learner Tertiary Students of a Bilingual Probability Applet,” Statistics Education Research Journal, 15, 145–168. DOI: 10.52041/serj.v15i2.245.
   Google Scholar
• Lesser, L., and Winsor, M. (2009), “English Language Learners in Introductory Statistics: Lessons Learned from an Exploratory Case Study of Two Pre-service Teachers,” Statistics Education Research Journal, 8, 5–32. https://iase-web.org/ojs/SERJ/article/view/393 DOI: 10.52041/serj.v8i2.393.
   Google Scholar
• Levitin, D. (2018), A Field Guide to Lies: Critical Thinking in the Information Age, London: Penguin.
   Google Scholar
• Liu, K. A., and DiPietro Mager, N. A. (2016), “Women’s Involvement in Clinical Trials: Historical Perspective and Future Implications,” Pharmacy Practice, 14, 1–9. DOI: 10.18549/PharmPract.2016.01.708.
   Google Scholar
• Martin, D. B. (2009), “Does Race Matter?,” Teaching Children Mathematics, 16, 134–139. DOI: 10.5951/TCM.16.3.0134.
   Google Scholar
• Martynenko, G. (2003), “Semiotics of Statistics,” Journal of Quantitative Linguistics, 10, 105–115. DOI: 10.1076/jqul.10.2.105.16712.
   Google Scholar
• Newman, M. L., Groom, C. J., Handelman, L. D., and Pennebaker, J. W. (2008), “Gender Differences in Language Use: An Analysis of 14,000 Text Samples,” Discourse Processes, 45, 211–236. DOI: 10.1080/01638530802073712.
   Web of Science ®Google Scholar
• Nolan, V. (2002), “Influence of Attitude towards Statistics, English Language Ability and Mathematical Ability in the Subject Quantitative Techniques at the Vaal Triangle Technikon, South Africa,” in Proceedings of the Sixth International Conference on Teaching Statistics: Developing a Statistically Literate Society, Cape Town, South Africa, ed. B. Phillips, Voorburg, The Netherlands: International Statistical Institute. https://iase-web.org/documents/papers/icots6/8a5_nola.pdf
   Google Scholar
• Nulty, D. D. (2008), “The Adequacy of Response Rates to Online and Paper Surveys: What Can Be Done?”, Assessment & Evaluation in Higher Education, 33, 301–314. DOI: 10.1080/02602930701293231.
   Web of Science ®Google Scholar
• Owora, A. H., Dawson, J., Gadbury, G., Mestre, L. M., Pavela, G., Mehta, T., Vorland, C. J., Xun, P., and Allison, D. B. (2022), “Randomisation Can Do Many Things – But It Cannot ‘Fail’,” Significance, 19, 20–23. DOI: 10.1111/1740-9713.01609.
   Google Scholar
• Parker, M. (2020), “Does the Average Person Exist?”, available at https://www.youtube.com/watch?v=NbiveCNBOxk
   Google Scholar
• Perkins, K. K., and Wieman, C. E. (2005), “The Surprising Impact of Seat Location on Student Performance,” The Physics Teacher, 43, 30–33. DOI: 10.1119/1.1845987.
   Google Scholar
• Peterson, R. A. (2001), “On the Use of College Students in Social Science Research: Insights from a Second-Order Meta-Analysis,” Journal of Consumer Research, 28, 450–461. DOI: 10.1086/323732.
   Web of Science ®Google Scholar
• Pfannkuch, M. (2011), “The Role of Context in Developing Informal Statistical Inferential Reasoning: A Classroom Study,” Mathematical Thinking and Learning, 13, 27–46. DOI: 10.1080/10986065.2011.538302.
   Web of Science ®Google Scholar
• Rampell, C. (2011), “Discovered: The Happiest Man in America” The New York Times, March 5. Available at https://economix.blogs.nytimes.com/2011/03/07/the-happiest-man-in-america-annotated/ or https://www.nytimes.com/2011/03/06/weekinreview/06happy.html
   Google Scholar
• Rangecroft, M. (2002), “The Language of Statistics,” Teaching Statistics, 24, 34–37. DOI: 10.1111/1467-9639.00080.
   Google Scholar
• Raper, S. (2017), “The Shock of the Mean,” Significance, 14, 12–17. DOI: 10.1111/j.1740-9713.2017.01087.x.
   Google Scholar
• Reid, D. K., Robinson, S. J., and Bunsen, T. D. (1995), “Empiricism and Beyond: Expanding the Boundaries of Special Education,” Remedial and Special Education, 16, 131–141. DOI: 10.1177/074193259501600302.
   Web of Science ®Google Scholar
• Roe, M., Shin, H., Ukkusuri, S., Blatt, A., Majka, K., et al. (2010), “The New York City Pedestrian Safety Study and Action Plan Technical Supplement,” New York City Transportation Dept. Available at https://www.nyc.gov/html/dot/downloads/pdf/nyc_ped_safety_study_action_plan_technical_supplement.pdf
   Google Scholar
• Rose, T. (2016), The End of Average: How We Succeed in a World That Values Sameness, New York: HarperOne.
   Google Scholar
• Scheaffer, R. L., Watkins, A., Gnanadesikan, M., and Witmer, J. A. (1996), “Random Rectangles,” in Activity-Based Statistics, New York: Springer. DOI: 10.1007/978-1-4757-3843-8_21.
   Google Scholar
• Schield, M. (2010), “Assessing Statistical Literacy: Take CARE,” in Assessment Methods in Statistical Education: An International Perspective, eds. P. Bidgood, N. Hunt, and F. Joliffe, pp. 133–152, Chichester, UK: Wiley.
   Google Scholar
• Schield, M. (2021), Statistical Literacy 2021B: Seeing the Story behind the Statistics, Instant Publisher. http://www.statlit.org/Schield.htm
   Google Scholar
• Sfard, A. (2008), Thinking as Communicating: Human Development, the Growth of Discourses, and Mathematizing, New York: Cambridge University Press.
   Google Scholar
• Shaughnessy, J. M. (1992), “Research in Probability and Statistics: Reflections and Directions,” in Handbook of Research on Mathematics Teaching and Learning, ed. D. A. Grouws, New York: Macmillan, pp. 465–494.
   Google Scholar
• Smucker, K., and Manouchehri, A. (2020), “The Impact of Self-Collected Data on Students’ Statistical Analysis,” in Mathematics Education across Cultures: Proceedings of the 42nd Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education, eds. A. I. Sacristán, J. C. Cortés-Zavala, and P. M. Ruiz-Arias, pp. 1336–1340, Mexico. Cinvestav/AMIUTEM/PME-NA. https://pmena2020.cinvestav.mx/Portals/pmena2020/Proceedings/PMENA42-BRR-1655452-Smucker-et-al.pdf DOI: 10.51272/pmena.42.2020-205.
   Google Scholar
• Sternin, J., Sternin, M., and Pascale, R. (2010), The Power of Positive Deviance: How Unlikely Innovators Solve the World’s Toughest Problems, Cambridge, MA: Harvard Business Press.
   Google Scholar
• Stigler, S. M. (1990), The History of Statistics: The Measurement of Uncertainty before 1900, Cambridge, MA: Harvard University Press.
   Google Scholar
• Stigler, S. M. (1999), Statistics on the Table: The History of Statistical Concepts and Methods, Cambridge, MA: Harvard University Press.
   Google Scholar
• Strauss, S., and Bichler, E. (1988), “The Development of Children’s Concepts of the Arithmetic Average,” Journal for Research in Mathematics Education, 19, 64–80. DOI: 10.5951/jresematheduc.19.1.0064.
   Web of Science ®Google Scholar
• Triola, M. F. (2010), Elementary Statistics (11th ed.), Boston, MA: Addison-Wesley.
   Google Scholar
• Ulbrich, C. (2018), “Totally Average Woman,” available at https://CAUSEweb.org/cause/resources/fun/songs/totally-average-woman
   Google Scholar
• Utts, J. M. (2015), Seeing through Statistics (4th ed.), Stamford, CT: Cengage.
   Google Scholar
• van de Walle, J. (2007), Elementary and Middle School Mathematics: Teaching Developmentally (6th ed.), Boston, MA: Pearson.
   Google Scholar
• van Staden, P. J. (2009), “Improbable versus Unexpected Outcomes,” Teaching Statistics, 31, 90–90. DOI: 10.1111/j.1467-9639.2009.00393.x.
   Google Scholar
• Vogt, W. P. (2007), Quantitative Research Methods for Professionals, Boston, MA: Pearson.
   Google Scholar
• Waller, P. P., and Marzocchi, A. S. (2020), “From Rules that Expire to Language that Inspires,” Mathematics Teacher: Learning and Teaching PK-12, 113, 544–550. DOI: 10.5951/MTLT.2019.0178.
   Google Scholar
• Wang, T. V., Potter van Loon, R. J. D., van den Assem, M. J., and van Dolder, D. (2016), “Number Preferences in Lotteries,” Judgment and Decision Making, 11, 243–259. DOI: 10.1017/S1930297500003089.
   Web of Science ®Google Scholar
• Watson, J. M. (2007), Statistical Literacy at School: Growth and Goals, New York: Routledge.
   Google Scholar
• Wilensky, U. (1995), “Paradox, Programming, and Learning Probability: A Case Study in a Connected Mathematics Framework,” The Journal of Mathematical Behavior, 14, 253–280. DOI: 10.1016/0732-3123(95)90010-1.
   Google Scholar
• Winsor, M. S. (2007), “Bridging the Language Barrier in Mathematics,” The Mathematics Teacher, 101, 372–378. DOI: 10.5951/MT.101.5.0372.
   Google Scholar
• Zazkis, R. (2010), “Using Code-Switching as a Tool for Learning Mathematical Language,” For the Learning of Mathematics, 20, 38–43.
   Google Scholar
• Zimmer, B. (2010, October 29), “Creeper! Rando! Sketchball!”, The New York Times. Available at https://www.nytimes.com/2010/10/31/magazine/31FOB-onlanguage-t.html. Retrieved March 7, 2022.
   Google Scholar