Accession Number : AD0651372

Title :   EVALUATION OF LARGE SCALE VISUAL DISPLAYS.

Descriptive Note : Final rept., 16 Aug 65-23 Jan 67,

Corporate Author : FRANKLIN INST RESEARCH LABS PHILADELPHIA PA

Personal Author(s) : Landis,Daniel ; Slivka,Robert M. ; Jones,James M. ; Harrison,Sylvia ; Silver,Carl A.

Report Date : APR 1967

Pagination or Media Count : 141

Abstract : The development of a usable, valid metric of information transfer from large-scale visual displays is reported. This metric is related to multidimensional analytically derived rating scales in a set of regression equations. These rating scales account for approximately 50 percent of the explained variance of the metric, and pertinent information about the display observer accounts for the remaining 50 percent. It was also found that three traditional measures of display effectiveness, based only on information assimilation, do not accurately measure the effectiveness of displays because they fail to consider the element of decision quality. The research produced several findings about the characteristics of effective displays: small displays are approximately 8 to 10 percent more effective than larger equivalent displays; the use of nonredundant color coding increases effectiveness from 8 to 13 percent; each fact added to a display produces a change in effectiveness of approximately 2 percent; and the compression of symbols produces a 2-percent increase in effectiveness for each compression step, which can be raised to as much as 3 percent under conditions of high incentive. The results indicate that, if the critical parameters of display effectiveness (determined by multidimensional analysis of each rating scale) are included in a regression model, it would be possible to predict display effectiveness from engineering data, without the necessity of using display observers in an effectiveness-testing situation. (Author)

Descriptors :   (*DISPLAY SYSTEMS, EFFECTIVENESS), MAN MACHINE SYSTEMS, DECISION MAKING, COMMAND AND CONTROL SYSTEMS, PLOTTING BOARDS

Subject Categories : Human Factors Engineering & Man Machine System

Distribution Statement : APPROVED FOR PUBLIC RELEASE