Meta-Analysis of Correlations Among Usability Measures
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Meta-Analysis of Correlations Among Usability Measures. / Hornbæk, Kasper Anders Søren; Effie Lai Chong, Law.
Conference on Human Factors in Computing Systems: CHI 2007, Reach beyond. Conference Proceedings. ed. / Bo Begole; Stephen Payne; Elizabeth Churchill; Rob St. Amant; David Gilmore; Mary Beth Rosson. Association for Computing Machinery, 2007. p. 617-626.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Meta-Analysis of Correlations Among Usability Measures
AU - Hornbæk, Kasper Anders Søren
AU - Effie Lai Chong, Law
PY - 2007
Y1 - 2007
N2 - Understanding the relation between usability measures seems crucial to deepen our conception of usability and to select the right measures for usability studies. We present a meta-analysis of correlations among usability measures calculated from the raw data of 73 studies. Correlations are generally low: effectiveness measures (e.g., errors) and efficiency measures (e.g., time) has a correlation of .247 ± .059 (Pearson's product-moment correlation with 95% confidence interval), efficiency and satisfaction (e.g., preference) one of .196 ± .064, and effectiveness and satisfaction one of .164 ± .062. Changes in task complexity do not influence these correlations, but use of more complex measures attenuates them. Standard questionnaires for measuring satisfaction appear more reliable than homegrown ones. Measures of users' perceptions of phenomena are generally not correlated with objective measures of the phenomena. Implications for how to measure usability are drawn and common models of usability are criticized.
AB - Understanding the relation between usability measures seems crucial to deepen our conception of usability and to select the right measures for usability studies. We present a meta-analysis of correlations among usability measures calculated from the raw data of 73 studies. Correlations are generally low: effectiveness measures (e.g., errors) and efficiency measures (e.g., time) has a correlation of .247 ± .059 (Pearson's product-moment correlation with 95% confidence interval), efficiency and satisfaction (e.g., preference) one of .196 ± .064, and effectiveness and satisfaction one of .164 ± .062. Changes in task complexity do not influence these correlations, but use of more complex measures attenuates them. Standard questionnaires for measuring satisfaction appear more reliable than homegrown ones. Measures of users' perceptions of phenomena are generally not correlated with objective measures of the phenomena. Implications for how to measure usability are drawn and common models of usability are criticized.
U2 - http://doi.acm.org/10.1145/1240624.1240722
DO - http://doi.acm.org/10.1145/1240624.1240722
M3 - Article in proceedings
SN - 9781595935939
SP - 617
EP - 626
BT - Conference on Human Factors in Computing Systems
A2 - Begole, Bo
A2 - Payne, Stephen
A2 - Churchill, Elizabeth
A2 - St. Amant, Rob
A2 - Gilmore, David
A2 - Rosson, Mary Beth
PB - Association for Computing Machinery
Y2 - 28 April 2007 through 3 May 2007
ER -
ID: 934900