Are We Drawing the Correct Conclusions? Regression Analysis in the Nonprofit Literature
Bibliographic record
Abstract
Linear models are the most commonly used analytical tools in the nonprofit literature. Academics and practitioners utilize these models to test different hypotheses in support of their research efforts, seeking to find significant results that substantiate their theories. And yet the authors of this article have discovered a surprisingly large number of insignificant results in articles from established nonprofit journals. Insignificant hypotheses and Type II errors surely account for a number of these results, but the authors believe the majority of these results are due to a different cause, one that is detectable and preventable: multicollinearity.Dans les articles sur les organismes sans but lucratif, les modèles linéaires sont les outils analytiques les plus communément utilisés. En effet, académiques et praticiens utilisent tous les deux ces modèles pour évaluer diverses hypothèses relatives à leurs recherches, espérant trouver des résultats significatifs pouvant confirmer leurs théories. Pourtant, les auteurs de cet article ont découvert un nombre surprenant de résultats non significatifs dans des articles de revues établies sur les organismes sans but lucratif. Des hypothèses non significatives et des erreurs du type II expliquent sûrement certains de ces résultats, mais les auteurs croient que la majorité des résultats ont une cause différente qui est détectable et évitable : la multicolinéarité.
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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 | 0.118 | 0.423 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".