Using many-facet rasch measurement and generalizability theory to explore rater effects for direct behavior rating–multi-item scales.
Bibliographic record
Abstract
Although originally conceived of as a marriage of direct behavioral observation and indirect behavior rating scales, recent research has indicated that Direct Behavior Ratings (DBRs) are affected by rater idiosyncrasies (rater effects) similar to other indirect forms of behavioral assessment. Most of this research has been conducted using generalizability theory (GT), yet another approach, many-facet Rasch measurement (MFRM), has recently been utilized to illuminate the previously opaque nature of these rater idiosyncrasies. The purpose of this study was to utilize both approaches (GT and MFRM) to consider rater effects with 126 second- through fifth-grade students who were rated on two DBR-Multi-Item Scales by four raters (22 of these ratings were fully crossed). Results indicated the presence of rater effects and revealed nuances about their nature, including showing differences across construct domains, identifying items that are potentially more susceptible to rater effects than others, and isolating specific raters who appear to have been more susceptible to rater effects than other raters. These findings further indicate the indirect nature of DBRs and offer potential avenues for addressing and ameliorating rater effects in research and practice. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.102 | 0.241 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".