A Review of Psychometric Assessment and Reporting Practices: An Examination of Measurement-Oriented Versus Non-Measurement-Oriented Domains
Bibliographic record
Abstract
The primary aim of the present study is to determine whether the psychometric evaluation practices and test-analytic rationales of researchers publishing in journals with a measurement focus differ from those of researchers publishing in journals with varying substantive research foci. Several components of two different samples of articles were examined and compared; one contained articles from a set of measurement-oriented journals ( n = 402) and the other contained articles published in journals representing a cross-section of research domains ( n = 289). Findings indicate that, contrary to expectations, articles published in measurement-oriented journals, as compared with general journals, generally may not reflect better psychometric analysis and reporting practices or sounder test-analytic rationales on the part of the researchers. It was also found that although researchers are generally evaluating either score precision/reliability or validity, they seldom evaluate both, indicating that there may be a general lack of appreciation for the importance of conducting a full and coherent data-based test analysis whenever a measure is employed. A number of limitations of the study and recommendations for future research are also addressed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.211 | 0.515 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.030 | 0.040 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".