Date Approved
2018
Degree Type
Open Access Thesis
Degree Name
Master of Arts (MA)
Department or School
Mathematics
Committee Member
Stephanie Casey, Ph.D.
Committee Member
Andrew Ross, Ph.D.
Committee Member
Carla Tayeh, Ph.D.
Abstract
Statistical association is an important concept in statistics. An exploratory study examined how students reason about statistical association utilizing graphical representations constructed with CODAP, a dynamic statistical graphing software. Task-based interviews were conducted with three 6th grade students prior to formal instruction. Students’ conceptions of a statistical relationship, proportional reasoning skill level, ability to interpret bivariate categorical graphs (particularly segmented bar graphs and two-way binned plots), and ability to identify association of two categorical variables were all investigated through interview tasks and responses to inquiry. Students were found to have developing proportional reasoning skills and struggled to correctly define and identify association. These results were compared to a previous study which asked students to analyze pre-constructed graphs. Students were more successful interpreting graphs that they constructed than pre-constructed graphs. These results have curricular and future research implications.
Recommended Citation
Eide, Adam, "Students’ interpretations of categorical data using dynamic graphical representations" (2018). Master's Theses and Doctoral Dissertations. 955.
https://commons.emich.edu/theses/955
Included in
Mathematics Commons, Science and Mathematics Education Commons, Statistics and Probability Commons