A Cautionary Note on the Use of the Vale and Maurelli Method to Generate Multivariate, Nonnormal Data for Simulation Purposes
Bibliographic record
Abstract
To further understand the properties of data-generation algorithms for multivariate, nonnormal data, two Monte Carlo simulation studies comparing the Vale and Maurelli method and the Headrick fifth-order polynomial method were implemented. Combinations of skewness and kurtosis found in four published articles were run and attention was specifically paid to the quality of the sample estimates of univariate skewness and kurtosis. In the first study, it was found that the Vale and Maurelli algorithm yielded downward-biased estimates of skewness and kurtosis (particularly at small samples) that were also highly variable. This method was also prone to generate extreme sample kurtosis values if the population kurtosis was high. The estimates obtained from Headrick's algorithm were also biased downward, but much less so than the estimates obtained through Vale and Maurelli and much less variable. The second study reproduced the first simulation in the Curran, West, and Finch article using both the Vale and Maurelli method and the Heardick method. It was found that the chi-square values and empirical rejection rates changed depending on which data-generation method was used, sometimes sufficiently so that some of the original conclusions of the authors would no longer hold. In closing, recommendations are presented regarding the relative merits of each algorithm.
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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 | 0.149 | 0.535 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.010 | 0.004 |
| Research integrity | 0.007 | 0.028 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".