Despite its popularity, knowledge of the System Usability Scale’s (SUS) psychometric quality in German is limited. This article developed a positively worded German SUS version and investigated two existing versions. In a preregistered first study (N = 250), positive alternatives for negative items were evaluated using item analyses, exploratory factor analyses, and language expert suggestions. The two existing versions were also compared. A preregistered second study (N = 877) involved participants interacting with 12 websites and completing the different SUS versions to validate the new positive-only and the original versions. Analyses included item analyses, confirmatory factor analyses, correlations with related scales, and error analyses. Findings indicated that the SUS-DE-Pos, a positive version based on Rummel, performed best. Researchers should use this version or Rummel’s version if negative items are needed. Overall, this work provides validated German SUS versions, with and without negative items, addressing a critical gap for German-speaking HCI researchers and practitioners.

Measuring the subjective experience of digital game players is essential to player experience research. Recently, the Player Experience Inventory (PXI) was developed, which assesses both functional and psychosocial consequences of digital gameplay. We present a pre-registered independent online study with a large sample to provide additional evidence of psychometric quality for the PXI. Responses from 1518 participants were collected, rating a recent or memorable experience playing a digital game using the PXI and related measures. While our results from standard psychometric reliability and validity analyses generally favored the PXI, we also identified challenges with the immersion construct. Further, we find a ten-factor model, or alternatively, an 11-factor should enjoyment be measured, to fit our collected data best. In sum, the PXI is a valuable tool to measure a variety of constructs central to player experience.

Despite its importance, few validated scales exist to measure aesthetics in HCI. One notable exception, the Visual Aesthetics of Websites Inventory (VisAWI), has never been validated in English. Furthermore, the VisAWI contains negatively formulated items, which adversely impact the psychometric quality of survey scales. Consequently, this paper’s aim was the development of a positive-item version of the VisAWI, the VisAWI-pos, as a viable alternative to the original scale. Positive alternatives for the negative items of the VisAWI scale were developed and evaluated in a first study (N = 41). Afterward, a pre-registered second study (N = 966) was conducted to validate the VisAWI-pos. In addition, the VisAWI’s English version was formally validated for the first time. Results showed that the English VisAWI has comparable psychometric qualities to validated versions in other languages. Furthermore, the VisAWI-pos provided matching results to the original VisAWI while exhibiting equal or improved psychometric quality.

Measuring theoretical concepts, so-called constructs, is a central challenge of Player Experience research. Building on recent work in HCI and psychology, we conducted a systematic literature review to study the transparency of measurement reporting. We accessed the ACM Digital Library to analyze all 48 full papers published at CHI PLAY 2020, of those, 24 papers used self-report measurements and were included in the full review. We assessed specifically, whether researchers reported What, How and Why they measured. We found that researchers matched their measures to the construct under study and that administrative details, such as number of points on a Likert-type scale, were frequently reported. However, definitions of the constructs to be measured and justifications for selecting a particular scale were sparse. Lack of transparency in these areas threaten the validity of singular studies, but further compromise the building of theories and accumulation of research knowledge in meta-analytic work. This work is limited to only assessing the current transparency of measurement reporting at CHI PLAY 2020, however we argue this constitutes a fair foundation to assess potential pitfalls. To address these pitfalls, we propose a prescriptive model of a measurement selection process, which aids researchers to systematically define their constructs, specify operationalizations, and justify why these measures were chosen. Future research employing this model should contribute to more transparency in measurement reporting. The research was funded through internal resources. All materials are available on https://osf.io/4xz2v/.