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There is a strong business case for automotive companies to improve by understanding what consumers want, like and dislike. Various aspects of ergonomics such as reach, visibility, usability, feel are dependent on measuring consumer’s ability, opinions and satisfaction. Rating scales (such as adjective, continuous, logarithmic, etc.) are used to measure these complex attitudes. It is essential the correct rating scale and appropriate analysis methods are used to capture these attitudes. Previous psychology research has been conducted on the performance of different rating scales. This ratings scale research focused on scales and their reliability and validity for various applications. This paper will summarize past research, discuss the use of rating scales specific to vehicle ergonomics, and analyze the results of an automotive interface study that correlates the seven-point adjective rating scale to the system usability score (SUS).

Measurement of usability with the System Usability Scale (SUS) is successfully applied to products in many industries. The benefit of any measurement scale, however, is limited by the repeatability of the associated testing process. For SUS, these factors can include sample size, study protocol, previous experience, and pre study exposure to the system being tested. Differences in user exposure can influence the usability assessment of interfaces which could affect the validity of SUS scores.

The System Usability Scale (SUS) is used across industries, to evaluate a product’s ease of use. As the automotive industry increases its digital footprint, the SUS has found its application as a simple and reliable assessment of various in-vehicle human machine interfaces. These evaluations cover a broad scope and it is important to design studies with participant fatigue, study time, and study cost in mind. Reducing the number of items in the SUS questionnaire could save researchers time and resources. The SUS is a ten-item questionnaire that can measure usability and learnability, depending on the system. These ten questions are highly correlated to each other suggesting the SUS score can be determined with fewer items. Thus, the focus of this paper is two-fold: using principal component analysis (PCA) to determine the dimensionality of SUS and using this finding to reduce variables and build a regression equation for SUS scores for in-vehicle human machine interfaces.