Is My Test Valid? Guidelines for the Practicing Psychologist for Evaluating the Psychometric Properties of Measures
Bibliographic record
Abstract
A general logic for data-based test evaluation based on Slaney and Maraun's (2008) framework is described. On the basis of this framework and other well-known test theoretic results, a set of guidelines is proposed to aid researchers in the assessment of the psychometric properties of the measures they use in their research. The guidelines are organized into eight areas and range from general recommendations, pertaining to understanding different psychometric properties of quantitative measures and at what point in a test evaluation their respective assessments should occur, to clarifications of core psychometric concepts such as factor structure, reliability, coefficient alpha, and dimensionality. Finally, an illustrative example is provided with a data-based test evaluation of the Hare Psychopathy Checklist-Revised (Hare, 1991) as a measure of psychopathic personality disorder in a sample of 384 male offenders serving sentences in a Canadian correctional facility.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".