miniPXI: Development and Validation of an Eleven-Item Measure of the Player Experience Inventory
Bibliographic record
Abstract
Questionnaires are vital in games user research (GUR) to assess player experience (PX). However, having too many questions in surveys prevents wider uptake among GUR professionals because of games' rapid production cycles. To address this issue, we present the miniPXI---an eleven-item measure of the popular Player Experience Inventory (PXI)---providing single items for each of its eleven constructs. To develop the scale and examine its reliability and validity, we present three studies, conducted with 15 experts and 628 digital game players across continents. In the first survey study (n=366, 15 experts), single items were selected. In a second survey study (n=232), we explored reliability and validity of the single-item scale. Participants completed both full and single-item (SI) variants in three days. In the last study (n=30), we established the validity and sensitivity via an experimental evaluation of two games. The results are nuanced; SI reliability estimates for PXI constructs range from .51 to .83 with an average of .68, we could confirm the validity for nine constructs. We conclude that the miniPXI can be a valuable tool for PX evaluations where a longer measure is not feasible, and provide practical considerations for its use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".