Eliciting User Experience through Rasch-Calibrated Metrics for Latent Variables

Abstract

Measurement of affective user experience is not straightforward as it is typically to measure the physical properties of the elements that aggregate a product For this purpose, it is necessary to develop metrics associating observed user experience in the real world with a relevant latent (i.e., unobserved) attribute of the product. However, metrics for latent variables can be undermined by misinterpretation, ambiguity, unfamiliarity, bias, redundancy and multidimensionality. For this reason, anomalies in data ought to be investigated through a robust measurement theory to determine to what extent they corrupt quantitative properties. This paper shows that Rasch measurement theory, which embraces a family of probabilistic models, provides procedures referred to as calibration to test the hypothesis that the metric fulfils measurement principles. As a result, linear scales of affective user experience can be aligned to physical properties of products, allowing generalization of comparisons beyond the particular sample under which a particular product experience was observed.

Keywords: User experience, affective engineering, Rasch model, measurement.

DOI: 10.54941/ahfe100564