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Research in K-12 Statistics Education

Development of an Informal Test for the Fit of a Probability Distribution Model for Teaching

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References

  • Arnold, P. (2013), “Statistical Investigative Questions. An Enquiry Into Posing and Answering Investigative Questions From Existing Data,” Doctoral thesis, University of Auckland, New Zealand, ResearchSpace@Auckland, available at http://hdl.handle.net/2292/21305
  • Braun, V., and Clarke, V. (2006), “Using Thematic Analysis in Psychology,” Qualitative Research in Psychology, 3, 77–101. DOI: 10.1191/1478088706qp063oa.
  • Casey, S. A., and Wasserman, N. H. (2015), “Teachers’ Knowledge About Informal Line of Best Fit,” Statistics Education Research Journal, 14, 8–35.
  • Creswell, J. W. (2015), A Concise Introduction to Mixed Methods Research, Thousand Oaks, CA: SAGE.
  • Dolor, J., and Noll, J. (2015), “Using Guided Reinvention to Develop Teachers’ Understanding of Hypothesis Testing Concepts,” Statistics Education Research Journal, 14, 60–89.
  • Fergusson, A.-M. (2017), “Informally Testing the Fit of a Probability Distribution Model,” Masters dissertation, University of Auckland, New Zealand, ResearchSpace@Auckland, available at http://hdl.handle.net/2292/36909.
  • Fielding-Wells, J., and Makar, K. (2015), “Inferring to a Model: Using Inquiry-Based Argumentation to Challenge Young Children’s Expectations of Equally Likely Outcomes,” in Reasoning About Uncertainty: Learning and Teaching Informal Inferential Reasoning, eds. A. Zieffler and E. Fry, Minneapolis, MN: Catalyst Press, pp. 1–28.
  • Gould, R., Davis, G., Patel, R., and Esfandiari, M. (2010), “Enhancing Conceptual Understanding With Data Driven Labs,” in Data and Context in Statistics Education: Towards an Evidence-Based Society. Proceedings of the Eighth International Conference on Teaching Statistics, Ljubljana, Slovenia, eds. C. Reading, Voorburg, The Netherlands: International Statistical Institute.
  • Hofmann, H., Follett, L., Majumder, M., and Cook, D. (2012), “Graphical Tests for Power Comparison of Competing Designs,” IEEE Transactions on Visualization and Computer Graphics, 18, 2441–2448. DOI: 10.1109/TVCG.2012.230.
  • Kazak, S., Pratt, D., and Gökce, R. (2018), “Sixth Grade Students’ Emerging Practices of Data Modelling,” ZDM, 50, 1151–1163. DOI: 10.1007/s11858-018-0988-3.
  • Konold, C., and Kazak, S. (2008), “Reconnecting Data and Chance,” Technology Innovations in Statistics Education, 2.
  • Konold, C., Madden, S., Pollatsek, A., Pfannkuch, M., Wild, C., Ziedins, I., Finzer, W., Horton, N., and Kazak, S. (2011), “Conceptual Challenges in Coordinating Theoretical and Data-Centered Estimates of Probability,” Mathematical Thinking and Learning, 13, 68–86. DOI: 10.1080/10986065.2011.538299.
  • Lehrer, R., Jones, R., and Kim, M. (2014), “Model-Based Informal Inference,” in Sustainability in Statistics Education. Proceedings of the Ninth International Conference on Teaching Statistics (ICOTS9, July, 2014), Flagstaff, Arizona, USA, eds. K. Makar, B. de Sousa, and R. Gould, Voorburg, The Netherlands: International Statistical Institute.
  • Ministry of Education (2007). The New Zealand Curriculum, Wellington, New Zealand: Learning Media Limited.
  • Pfannkuch, M., Forbes, S., Harraway, J., Budgett, S., and Wild, C. (2013), “Bootstrapping Students’ Understanding of Statistical Inference,” Summary Research Report for the Teaching and Learning Research Initiative, available at http://www.tlri.org.nz/sites/default/files/projects/9295_summary/%20report.pdf.
  • Pfannkuch, M., Wild, C. J., and Regan, M. (2014), “Students’ Difficulties in Practicing Computer-Supported Statistical Inference: Some Hypothetical Generalizations From a Study,” in Mit Werkzeugen Mathematik Und Stochastik Lernen [Using Tools for Learning Mathematics and Statistics], eds. T. Wassong, D. Frischemeier, P. Fischer, R. Hochmuth, and P. Bender, Wiesbaden, Germany: Springer Spektrum, pp. 393–403.
  • Pfannkuch, M., and Ziedins, I. (2014), “A Modeling Perspective on Probability,” in Probabilistic Thinking: Presenting Plural Perspectives, eds. E. Chernoff and B. Sriraman, New York, NY: Springer, pp. 101–116.
  • Reaburn, R. (2014), “Introductory Statistics Course Tertiary Students’ Understanding of p-Values,” Statistics Education Research Journal, 13, 53–65.
  • Roback, P., Chance, B., Legler, J., and Moore, T. (2006), “Applying Japanese Lesson Study Principles to an Upper-Level Undergraduate Statistics Course,” Journal of Statistics Education, 14. DOI: 10.1080/10691898.2006.11910580.
  • Wild, C. J., and Pfannkuch, M. (1999), “Statistical Thinking in Empirical Enquiry,” International Statistical Review, 67, 223–248. DOI: 10.1111/j.1751-5823.1999.tb00442.x.
  • Wild, C. J., Pfannkuch, M., Regan, M., and Horton, N. J. (2011), “Towards More Accessible Conceptions of Statistical Inference,” Journal of the Royal Statistical Society, Series A, 174, 247–295. DOI: 10.1111/j.1467-985X.2010.00678.x.
  • Zieffler, A., Garfield, J., delMas, R., and Reading, C. (2008), “A Framework to Support Research on Informal Inferential Reasoning,” Statistics Education Research Journal, 7, 40–58.