Are users more diverse than designs? Testing and extending a 25 years old claim

Abstract

Twenty-five years ago, Dennis Egan published a review on the impact of individual differences in human-computer interaction, where he claimed that users are more diverse than designs are [5]. While being cited frequently, this claim has not been tested since then. An efficient research design for separating and comparing variance components is presented, together with a statistical model to test Egan's claim. The results of a pilot study indicate that Egan's claim does not universally hold. An extension to the claim is suggested, capturing the trade-offs when prioritizing user tasks. An alternative strategy towards universal design is proposed.

Individual Variation in Cognitive Processing Style Predicts Differences in Phonetic Imitation of Device and Human Voices

“…For one, it can inform our scientific understanding of human-computer interaction and developing more inclusive models of human behavior toward devices. Furthermore, it is relevant for those interested in designing systems that can accommodate as many users as possible (cf., [3], [7]).…”

An extended protocol for usability validation of medical devices: Research design and reference model

“…For example, a crucial question in validation testing could be how uniformly users benefit from a novel design, which is different to just regarding the average benefit. Furthermore, tasks can be considered samples, too, and be modelled as random effects [28]. Incorporating tasks-level random effects allows assessing whether any observed advantage of a novel design is uniform across tasks.…”

“…Egan calls designs that reduce the probability of extreme low performance level robust designs [47]. Random effects analysis is a suitable method to assess inter-individual variation and can be used to assess the robustness of design [28]. This is even possible without the necessity of theoretically inferred predictors to explain variation, as the individual trajectories speak for themselves.…”

“…Hierarchical random effects apply when levels are nested, like ''employees of a hospital and hospitals of a country". Cross-classified models are increasingly used in domains, where stimuli or conditions are considered populations, rather than designed treatments, for example, phonemes in psycholinguistics [60], test items in psychometrics [61] and designs in Human-Computer Interaction [28].…”