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On the dimensionality of the System Usability Scale: a test of alternative measurement models

Overview of attention for article published in Cognitive Processing, June 2009
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Title
On the dimensionality of the System Usability Scale: a test of alternative measurement models
Published in
Cognitive Processing, June 2009
DOI 10.1007/s10339-009-0268-9
Pubmed ID
Authors

Simone Borsci, Stefano Federici, Marco Lauriola

Abstract

The System Usability Scale (SUS), developed by Brooke (Usability evaluation in industry, Taylor & Francis, London, pp 189-194, 1996), had a great success among usability practitioners since it is a quick and easy to use measure for collecting users' usability evaluation of a system. Recently, Lewis and Sauro (Proceedings of the human computer interaction international conference (HCII 2009), San Diego CA, USA, 2009) have proposed a two-factor structure-Usability (8 items) and Learnability (2 items)-suggesting that practitioners might take advantage of these new factors to extract additional information from SUS data. In order to verify the dimensionality in the SUS' two-component structure, we estimated the parameters and tested with a structural equation model the SUS structure on a sample of 196 university users. Our data indicated that both the unidimensional model and the two-factor model with uncorrelated factors proposed by Lewis and Sauro (Proceedings of the human computer interaction international conference (HCII 2009), San Diego CA, USA, 2009) had a not satisfactory fit to the data. We thus released the hypothesis that Usability and Learnability are independent components of SUS ratings and tested a less restrictive model with correlated factors. This model not only yielded a good fit to the data, but it was also significantly more appropriate to represent the structure of SUS ratings.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 311 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 1%
Germany 3 <1%
Spain 3 <1%
United Kingdom 2 <1%
France 2 <1%
Austria 2 <1%
Australia 1 <1%
United Arab Emirates 1 <1%
Colombia 1 <1%
Other 3 <1%
Unknown 289 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 60 19%
Student > Ph. D. Student 59 19%
Student > Master 55 18%
Student > Bachelor 20 6%
Student > Postgraduate 17 5%
Other 49 16%
Unknown 51 16%
Readers by discipline Count As %
Computer Science 63 20%
Psychology 50 16%
Engineering 38 12%
Medicine and Dentistry 23 7%
Social Sciences 17 5%
Other 57 18%
Unknown 63 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 October 2021.
All research outputs
#7,453,350
of 22,786,087 outputs
Outputs from Cognitive Processing
#105
of 338 outputs
Outputs of similar age
#36,963
of 109,270 outputs
Outputs of similar age from Cognitive Processing
#2
of 3 outputs
Altmetric has tracked 22,786,087 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 338 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.2. This one has gotten more attention than average, scoring higher than 62% of its peers.
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