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This paper describes recent research in subjective usability measurement at IBM. The focus of the research was the application of psychometric methods to the development and evaluation of questionnaires that measure user satisfaction with system usability. The primary goals of this paper are to (1) discuss the psychometric characteristics of four IBM… (More)

- James R. Lewis
- 2000

usability studies, sample size, usability measurement Efficiency is an important consideration in the design of industrial usability studies. One way to reduce the cost of a usability study is to reduce its sample size. Small samples are not always appropriate, but in this paper I will describe a way to use binomial confidence intervals to determine rapidly… (More)

Since its introduction in 1986, the 10-item System Usability Scale (SUS) has been assumed to be unidimensional. Factor analysis of two independent SUS data sets reveals that the SUS actually has two factors – Usability (8 items) and Learnability (2 items). These new scales have reasonable reliability (coefficient alpha of .91 and .70, respectively). They… (More)

Factor analysis of Post Study System Usability Questionnaire (PSSUQ) data from 5 years of usability studies (with a heavy emphasis on speech dictation systems) indicated a 3-factor structure consistent with that initially described 10 years ago: factors for System Usefulness, Information Quality, and Interface Quality. Estimated reliabilities (ranging from… (More)

A three-item after-scenario questionnaire was used in three related usability tests in different areas of the United States. The studies had eight scenarios in common. After participants finished a scenario, they completed the After-Scenario Questionnaire (the ASQ). A factor analysis of the responses to the ASQ items revealed that an eight-factor solution… (More)

Correlations between prototypical usability metrics from 90 distinct usability tests were strong when measured at the task-level (r between .44 and .60). Using test-level satisfaction ratings instead of task-level ratings attenuated the correlations (r between .16 and .24). The method of aggregating data from a usability test had a significant effect on the… (More)

<i>"Really, how many users do you need to test? Three answers, all different."</i>---User Experience, Vol. 4, Issue 4, 2005

The distribution of task time data in usability studies is positively skewed. Practitioners who are aware of this positive skew tend to report the sample median. Monte Carlo simulations using data from 61 large-sample usability tasks showed that the sample median is a biased estimate of the population median. Using the geometric mean to estimate the center… (More)