Graphic Representation in Managerial Decision Making: The Effect of Scale Break on the Dependent Axis

Abstract

This thesis investigated whether a scale break on the dependent axis a affects a decision maker's interpretation of the data presented in the graph. A literature search revealed criteria for constructing high integrity graphs and formatting scale breaks. An experiment was conducted to determine the effect of a scale break on the dependent axis. Graphs following the criteria for high integrity graphs were presented to the control group, while graphs following the criteria for high integrity graphs, with the exception of the scale break, were presented to the experimental group. Using a parametric two-sample t test and a non-parametric Rank Sum test, it was shown that data presented in a graph with a scale break was interpreted differently from data presented in a graph without a scale break. Analysis of variance was conducted on the demographic factors for each subject. In the experimental group, sex and professional experience were factors that led to different interpretations of graphs with a scale break. The level of experience using graphs in decision making was also a factor that led to different interpretations of graphs in both groups.... Decision making, Misleading, Graphics, Graph.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1992
Accession Number
ADA258989

Entities

People

  • Clark R. Carvalho
  • Michael D. Mcmillan

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • C4I
  • Counter WMD
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Analysis Of Variance
  • Application Software
  • Business Administration
  • Computers
  • Demography
  • Employment
  • Experimental Design
  • Information Science
  • Knowledge Management
  • Literature Surveys
  • Reliability
  • Spreadsheet Software
  • Statistical Analysis
  • Statistical Tests
  • Surveys
  • Task Performance And Analysis

Readers

  • Graph Algorithms and Convex Optimization.
  • Organizational Psychology.
  • Regression Analysis.