Considering Approaches to Pervasiveness in the Context of Personality Psychology
Bibliographic record
Abstract
In experimental psychology, the term “pervasiveness” has been introduced to refer to the proportion of participants who exhibit an effect. This paper is a review of existing methods that could be used to extend the idea of pervasiveness to cross-sectional correlational data. More specifically, I consider how bivariate or multivariate effect sizes can be expressed or contextualized through proportions or percentages of individuals via the following methods: Kendall’s Tau, Common Language Effect Size indicators (Dunlap’s CLr, Li and Waisman’s B P , Rosenthal and Rubin’s BESD and Mõttus’ TACT), rule-based methods (mostly Association Rule Mining, but Comparative Configural Models such as QCA and CORA are briefly discussed) and Observation Oriented Modeling. I illustrate the use of these methods in personality psychology using data from Soto's LOOPR project and highlight strengths and limitations for the different methods. I show that an Observed Percentage of Concordant Pairs (OPCP), easily obtained from Kendall’s Tau, can be used as a general-purpose common language effect size that also provides information on pervasiveness. The ‘pervasive’ R package, that provides the OPCP and information on a set of rules for use in a simple or multivariate regression setting, is introduced.
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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 | 0.093 | 0.202 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.003 | 0.032 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".