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.

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/.