The Effects of Data and Graph Type on Concepts and Visualizations of Variability

Abstract

Recognizing and interpreting variability in data lies at the heart of statistical reasoning. Since graphical displays should facilitate communication about data, statistical literacy should include an understanding of how variability in data can be gleaned from a graph. This paper identifies several types of graphs that students typically encounter-histograms, distribution bar graphs, and value bar charts. These graphs all share the superficial similarity of employing bars, and yet the methods to perceive variability in the data differ dramatically. We provide comparisons within each graph type for the purpose of developing insight into what variability means and how it is evident within the data's associated graph. We introduce graphical aids to visualize variability for histograms and value bar charts, which could easily be tied to numerical estimates of variability.

Development of the Statistical Reasoning in Biology Concept Inventory (SRBCI)

“…Many of the SRBCI items incorporate figures, and while we are aware that some students find visual interpretation difficult for a variety of reasons ( Chinn and Brewer, 2001 ; Friel et al. , 2001 ; Cooper and Shore, 2010 ), interpreting figures is a fundamental requirement of scientific literacy (and statistical reasoning; Coil et al. , 2010 ; Glazer, 2011 ; Watson, 2011 ).…”

Developing Consistency in the Terminology and Display of Bar Graphs and Histograms

“…Other studies have shown that students have trouble with distributions and graphical representations (Bakker and Gravemeijer 2004;delMas et al 2007;Hammerman and Rubin 2004;Konold and Higgins 2003;McClain et al 2000). Cooper and Shore (2010) wrote, 'it simply makes good sense to include rich discussions connecting an assortment of graphical displays to their corresponding data sets and methods to judge center and spread' (p. 13).…”

Ortaokul Matematik Öğretmen Adaylarının Dağılım Kavramına İlişkin Anlamalarının İncelenmesi

“…Alan bilgisi ile öğretim bilgisi arasındaki bu ilişki öğretmen adaylarının istatistiksel kavramlara ilişkin anlamaları ve öğretecekleri konunun daha fazlasını bilmelerini kritik hale getirmektedir (Borko ve diğerleri, 1992;Leavy, 2006). Ancak yapılan çalışmalar öğretmenlerin ve öğretmen adaylarının dağılım kavramı ve bu kavramın ilişkili olduğu diğer kavramların anlaşılması konusunda çeşitli zorluklarının olduğunu göstermektedir (Bakker & Gravemeijer, 2004;Cooper & Shore 2010;Garfield & Ben-Zvi, 2008a;Shaughnessy, 2007).…”