Understanding and improving subjective measures in human-computer interaction

In Human-Computer Interaction (HCI), research has shifted from a focus on usability and performance towards the holistic notion of User Experience (UX). Research into UX places special emphasis on concepts from psychology, such as emotion, trust, and motivation. Under this paradigm, elaborate methods to capture the richness and diversity of subjective experiences are needed. Although psychology offers a long-standing tradition of developing self-reported scales, it is currently undergoing radical changes in research and reporting practice. Hence, UX research is facing several challenges, such as the widespread use of ad-hoc questionnaires with unknown or unsatisfactory psychometric properties, or a lack of replication and transparency. Therefore, this thesis contributes to several gaps in the research by developing and validating self-reported scales in the domain of user motivation (manuscript 1), perceived user interface language quality (manuscript 2), and user trust (manuscript 3). Furthermore, issues of online research and practical considerations to ensure data quality are empirically examined (manuscript 4). Overall, this thesis provides well-documented templates for scale development, and may help improve scientific rigor in HCI.