Calibrating scales for affective responses to physical features of products using Rasch measurement theory

Affective engineering applies mathematical models to convert the information obtained from persons’ feelings to product features into an ergonomic design. However, the
methods commonly used to elicit persons’ responses can present inaccuracies if measurement principles are violated. Consequently, empirical studies cannot be easily
replicated and results cannot reliably be compared.
This research aimed to overcome the problem by establishing a novel approach in affective engineering using probabilistic models underpinned by Rasch measurement theory. The Rasch model verifies whether the observations meet the assumptions necessary for quantifying the numerical validity of the data employing the tools of standard statistics.
Initially, the research examined how well the data from affective responses would fit the expectations of the Rasch model to create a scale of specialness for four pieces of
wrapped confectionery. Anomalies in the data were investigated to determine their potential impact on measure interpretation. A second empirical study investigated the
stability of a measurement structure. Affective responses were compared with the physical properties related to compliance of a collection of product containers. A cross-validation strategy contrasted calibrations of the scale using different groups of respondents.
The results indicated that the differences between person locations on the measurement continuum from different calibrations were statistically non-significant. This
provided evidence that the use of a Rasch-calibrated scale can systematically refine and generalise its frame of reference without loss of measurement properties.
The contribution of the research for the advancement of knowledge is established by transforming affective responses to physical elements into objective measures. A
rationale was developed to achieve measurement properties in scales used in affective engineering, adapting the Rasch model’s taxonomy used in other domains. Furthermore,
the stability of a scale using different samples for calibration is demonstrated to be a property of Rasch-based scales. As a consequence of the stability, the association between affective responses and sensory information was realised and further variables were incorporated in the calibrated metric to refine the understanding of users’ experience. Consequently, reliable results can be obtained from small samples, which will reduce time and costs of quantitative consumer research.