Examining the Psychometric Properties and Factor Structure of EASE
Bibliographic record
Abstract
Abstract Initial information about psychometric properties of EASE subscales as well as for the overall instrument will be discussed. Development of the tool has included initial determination of face-validity, followed by establishing construct validity (r = 0.88 with EAT tool), and interrater agreement (r = 0.97). With the administration of the EASE tool in 228 living areas (e.g., units), parallel analysis was used to investigate the structure of the 130 item EASE instrument. 12 components were initially identified, and several additional component analyses were examined to determine the most efficient structure. Results describe the structure of the EASE instrument with regard to living area characteristics, particularly focusing on items that discriminate between different models (traditional, household and hybrid). Recognition of the dual purposes of the EASE tool as both a design planning resource and as a tool for comparative research across multiple settings, different potential structures will be discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".