Tool for accurately predicting website navigation problems, non-problems, problem severity, and effectiveness of repairs

  • Authors:

  • Marilyn Hughes Blackmon;Muneo Kitajima;Peter G. Polson

  • Affiliations:

  • University of Colorado at Boulder, Boulder, Colorado;National Institute of Advanced Industrial Science and Technology (AIST), Ibaraki, Japan;University of Colorado at Boulder, Boulder, Colorado

  • Venue:
  • Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
  • Year:
  • 2005

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Abstract

The Cognitive Walkthrough for the Web (CWW) is a partially automated usability evaluation method for identifying and repairing website navigation problems. Building on five earlier experiments [3,4], we first conducted two new experiments to create a sufficiently large dataset for multiple regression analysis. Then we devised automatable problem-identification rules and used multiple regression analysis on that large dataset to develop a new CWW formula for accurately predicting problem severity. We then conducted a third experiment to test the prediction formula and refined CWW against an independent dataset, resulting in full cross-validation of the formula. We conclude that CWW has high psychological validity, because CWW gives us (a) accurate measures of problem severity, (b) high success rates for repairs of identified problems (c) high hit rates and low false alarms for identifying problems, and (d) high rates of correct rejections and low rates of misses for identifying non-problems.