Preliminary Findings from Research on Teaching a Randomization-based Introductory Statistics Course
Robert
delMas

University of Minnesota

Thursday, August 28, 2014 - 3:30pm

Preliminary results are presented from an ongoing study of the development of tertiary students’ reasoning in a one-semester college-level statistics course. The modeling and simulation-based course relies on randomization and bootstrap methods for inference. Students in the statistics course learn to use TinkerPlots® to create "just by chance" models that form the basis of simulated distributions of sample statistics in order to draw an inference about an observed effect or difference. Comparisons of performance between students enrolled in the modeling and simulation-based course and students enrolled in statistics courses based on conventional parametric methods of inference suggest that students taking the modeling and simulation-based course have a better understanding of the principles of study design and statistical inference. To provide a richer view, summaries of qualitative data from nine students who participated in think-aloud, problem-solving interviews are reported. Preliminary analyses of the qualitative data indicate that these nine students had begun to develop three of the four dimensions of statistical thinking described by Wild and Pfannkuch (1999): the logic of inference, engagement in the interrogative cycle, and integration of statistical and contextual information.


More information about Robert delMas may be found at http://www.cehd.umn.edu/edpsych/people/Faculty/delMas.html