Development and validation of the player experience inventory: A scale to measure player experiences at the level of functional and psychosocial consequences
Bibliographic record
Abstract
Games User Research (GUR) focuses on measuring, analysing and understanding player experiences to optimise game designs. Hence, GUR experts aim to understand how specific game design choices are experienced by players, and how these lead to specific emotional responses. An instrument, providing such actionable insight into player experience, specifically designed by and for GUR was thus far lacking. To address this gap, the Player Experience Inventory (PXI) was developed, drawing on Means-End theory and measuring player experience both at the level of Functional Consequences, (i.e., the immediate experiences as a direct result of game design choices, such as audiovisual appeal or ease-of-control) and at the level of Psychosocial Consequences, (i.e., the second-order emotional experiences, such as immersion or mastery). Initial construct and item development was conducted in two iterations with 64 GUR experts. Next, the scale was validated and evaluated over five studies and populations, totalling 529 participants. Results support the theorized structure of the scale and provide evidence for both discriminant and convergent validity. Results also show that the scale performs well over different sample sizes and studies, supporting configural invariance. Hence, the PXI provides a reliable and theoretically sound tool for researchers to measure player experience and investigate how game design choices are linked to emotional responses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".