Abstract
The ability to analyse qualitative information from quantitative information, and/or to create new information from qualitative and quantitative information is the key task of statistical literacy in the 21st century. Although several studies have focussed on critical evaluation of statistical information, this aspect of research has not been clearly conceptualised as yet. This paper presents a hierarchy of the graphical interpretation component of statistical literacy. 175 participants from different educational levels (junior high school to graduate students) responded to a questionnaire and some of them were also interviewed. The SOLO Taxonomy was used for coding the students’ responses and the Rasch model was used to clarify the construction of the hierarchy. Five different levels of interpretations of graphs were identified: Idiosyncratic, Basic graph reading, Rational/Literal, Critical, and Hypothesising and Modelling. These results will provide guidelines for teaching statistical literacy.
Highlights
The ability to analyse qualitative information from quantitative information, and/or to create new information from qualitative and quantitative information is the key task of statistical literacy in the 21st century
Assessment tasks which exemplify the type of statistical literacy needed by informed citizens have been included in the Programme for International Student Assessment (PISA) conducted by the Organisation for Economic Cooperation and Development (OECD, 1999, 2003)
Interpretation of graphs hardly ever extends to making qualitative interpretations of statistical information
Summary
The ability to analyse qualitative information from quantitative information, and/or to create new information from qualitative and quantitative information is the key task of statistical literacy in the 21st century. Five different levels of interpretations of graphs were identified: Idiosyncratic, Basic graph reading, Rational/Literal, Critical, and Hypothesising and Modelling These results will provide guidelines for teaching statistical literacy. Many researchers have focused on students’ ability to extract statistical information from graphs and their using of graphs to make predictions or discover a trend (Curcio, 1987; Watson & Moritz, 1999; Ben-Zvi & Arcavi, 2001; Friel, Curcio, & Bright, 2001; Monteiro, & Ainley, 2006). They defined “Graph Sense” which covers all tasks related to graphs including graph making and reading graphs. Monteiro and Ainley (2006) investigated whether individual differences between participants who have different academic background correspond to differences in the emphasis placed on different kinds of knowledge in their interpretations of media graphs
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