Measure invariance of the Political Skill Inventory (PSI) across five cultures
Bibliographic record
Abstract
This research expands the study of political skill, a construct developed in North America, to other cultures. We examine the psychometric properties of the Political Skill Inventory (PSI) and test the measurement equivalence of the scale in a non-American context. Respondents were 1511 employees from China, Germany, Russia, Turkey, and the United States. The cross-cultural generalizability of the construct is established through consistent evidence of multi-group invariance in an increasingly stringent series of analyses of mean and covariance structures. Overall, the study provides systematic evidence that political skill can be treated as a stable construct among diverse cultural groups. Furthermore, our findings demonstrate that translated PSI measures operationalize the construct similarly. With some exceptions, the item loadings and intercepts are invariant for the US and non-US responses, suggesting partial measurement equivalence. After verifying the accuracy of item translation, we conclude that any differences can be explained by variation in the cultural value of uncertainly avoidance and cultural differences on a low-to-high context continuum. Detected dissimilarities are addressed, and some suggestions regarding the correct use across borders of the instrument by managers and researchers are provided.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".