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Physical Sciences and Mathematics Commons

Open Access. Powered by Scholars. Published by Universities.®

Social and Behavioral Sciences

University of South Florida

2016

Graphs

Articles 1 - 2 of 2

Full-Text Articles in Physical Sciences and Mathematics

Infographics As Eye Candy: Review Of World War Ii In Numbers: An Infographic Guide To The Conflict, Its Conduct, And Its Casualties By Peter Doyle (2013), Joel Best Jan 2016

Infographics As Eye Candy: Review Of World War Ii In Numbers: An Infographic Guide To The Conflict, Its Conduct, And Its Casualties By Peter Doyle (2013), Joel Best

Numeracy

Peter Doyle. World War II in Numbers: An Infographic Guide to the Conflict, Its Conduct, and Its Casualties, illustrated by Lindsey Johns (Buffalo NY: Firefly Books, 2013). 224 pp. ISBN: 177085195X.

Doyle’s book contains dozens of graphs of statistical data dealing with World War II. Many of these graphs are visually striking. However, they often violate fundamental graphing principles, in that they distort quantitative relationships, use unidentified scales, and often make it difficult to compare quantities. Graphic software makes it easy to create imaginative images, but these can fail to communicate the very information that is the graph’s purpose.


Random Number Simulations Reveal How Random Noise Affects The Measurements And Graphical Portrayals Of Self-Assessed Competency, Edward Nuhfer, Christopher Cogan, Steven Fleisher, Eric Gaze, Karl Wirth Jan 2016

Random Number Simulations Reveal How Random Noise Affects The Measurements And Graphical Portrayals Of Self-Assessed Competency, Edward Nuhfer, Christopher Cogan, Steven Fleisher, Eric Gaze, Karl Wirth

Numeracy

Self-assessment measures of competency are blends of an authentic self-assessment signal that researchers seek to measure and random disorder or "noise" that accompanies that signal. In this study, we use random number simulations to explore how random noise affects critical aspects of self-assessment investigations: reliability, correlation, critical sample size, and the graphical representations of self-assessment data. We show that graphical conventions common in the self-assessment literature introduce artifacts that invite misinterpretation. Troublesome conventions include: (y minus x) vs. (x) scatterplots; (y minus x) vs. (x) column graphs aggregated as quantiles; line …