Identifying the structure of within‐team variance in ratings of team constructs
Bibliographic record
Abstract
Abstract Researchers frequently assume some degree of consensus among team members' perceptions when aggregating individual survey responses to the team level. Literature reviews have routinely found, however, substantial within‐team variance in referent‐shift consensus measures of team constructs and that team member agreement is often lower than desired. To enhance our understanding of the structure of within‐team variance, using a sample of 20,183 individuals in 4,313 teams, we explored the proportion of variance attributable to different sources (response acquiescence, positivity bias, and rater variance specific to each construct) for five team constructs. We also examined the extent to which within‐team variance changed over time with a subsample of 3,720 individuals in 731 teams. The results indicated that constructs thought to be more observable, including role clarity and monitoring goal progress, appeared to be less prone to idiosyncratic perceiver effects and that the processes were widely experienced by all team members. Conversely, relationship conflict showed higher levels of within‐team variance and team consensus actually decreased over time. These findings indicate that perceptions of relationship conflict are influenced by the individual differences of team members and that conflict may be restricted to dyads or subgroups within teams. Overall, the findings indicate that stable perceiver effects and processes that are limited to a small proportion of team members may have a stronger influence on ratings of some team constructs than previously thought. We conclude with suggestions for theory development, team construct measurement, and advice to understand processes that lead to consensus in perceptions of team constructs.
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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 | 0.026 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".