On the dimensionality of the System Usability Scale: a test of alternative measurement models
On the dimensionality of the System Usability Scale: a test of alternative measurement models
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DOI：
10.1007/s10339-009-0268-9
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发表时间：
2009-08-01
影响因子：
1.7
通讯作者：
Lauriola, Marco
中科院分区：
文献类型：
--
作者：
Borsci, Simone;Federici, Stefano;Lauriola, Marco
系统可用性量表(SUS)是一种收集用户对系统的可用性评价的快速、易用的测量方法，由布鲁克(伦敦，Taylor&Francis，P189-194,1996)开发，在可用性实践者中取得了巨大的成功。最近，Lewis和Sauro(2009年美国加州圣地亚哥人机交互国际会议论文集)提出了一个双因素结构--可用性(8项)和可学习性(2项)--这表明从业者可以利用这些新因素从SUS数据中提取额外的信息。为了验证SUS的两组分结构的维度，我们估计了SUS的参数，并用结构方程模型对196名大学用户的SUS结构进行了检验。我们的数据表明，Lewis和Sauro提出的一维模型和不相关因素的两因素模型(2009年加州圣地亚哥人机交互国际会议论文集，美国圣地亚哥，2009)对数据的拟合都不令人满意。因此，我们发布了可用性和可学习性是SUS评级的独立组成部分的假设，并测试了一个带有相关因素的限制性较低的模型。该模型不仅很好地拟合了数据，而且明显更适合代表SUS评级的结构。
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.