Are meta-analytic estimates of team performance relationships robust? Exploring the impact of publication bias and outliers
Bibliographic record
Abstract
Purpose This study aims to explore whether meta-analytic estimates of relations between team performance and several team-level variables may be distorted by the influence of publication bias and outliers. The concern is that such distortion may negatively impact primary research and practitioner perceptions of associated relations. Design/methodology/approach Seven existing meta-analyses, comprising nine meta-analytic estimates, were examined using current techniques to assess the degree to which publication bias and outliers may have impacted these estimates. The adjusted estimates were used to index the robustness of the original meta-analytic estimates. Findings Results indicated that eight of nine meta-analytic estimates may not be robust, requiring that they be adjusted by at least 20% of their original value. Research limitations/implications For researchers, the authors describe how these adjusted estimates, and the general pattern of their results, might be considered in the design of future primary studies on team performance and inform practices for conducting meta-analyses in team research. Practical implications For practitioners, this paper provides advice on how to evaluate whether publication bias has been adequately examined in a meta-analysis and suggests how an estimate might be adjusted if this is not the case. Originality/value This study contributes to an emerging movement analyzing the robustness of meta-analytic estimates in the organizational sciences by focusing on the robustness of meta-analytic estimates of the relations between team performance and several team-level predictors.
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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 | 0.461 | 0.795 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.024 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".