WORKSHOP: Keeping up with The Standards: How to Design and Evaluate Reliability and Validity Studies
Bibliographic record
Abstract
The quality of the measures we use in research and clinical practice is of critical importance. The inferences we make from scores on psychological and health measures have impact – on theory, knowledge, policy, and individuals’ lives. This workshop reviews basic measurement principles of reliability and validity through the lens of modern validity and The Standards for Educational and Psychological Testing (American Educational Research Association [AERA], American Psychological Association [APA], and National Council on Measurement in Education [NCME], 2014). This workshop is relevant to test users, who are responsible for ensuring they have an adequate understanding of current psychometric theory and principles and use this knowledge when conducting reliability and validity studies or when using such studies to decide whether use of a measure is appropriate for their purpose, target audience, and context. This workshop will define key terms, contrast Trinitarian and modern perspectives on validity, and, based on The Standards and other recent literature, describe reliability evidence and each of the five sources of validity evidence. The workshop will address common questions about how much evidence is needed, whether all sources of validity evidence are needed for all measures, and the applicability of bodies of evidence for original and adapted/translated tests. Finally, based on reliability and validity syntheses, a summary of what evidence tends to be reported well and what does not will be presented. The workshop presentation will include examples and question/answer sessions. The facilitator is a Full Professor in Measurement, Evaluation, and Research Methodology at the University of British Columbia and former ITC Council member who has published over 85 refereed articles and book chapters and over 100 conference presentations related to psychological and health measurement, assessment, validation, and test development.
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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 | 0.393 | 0.551 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.010 | 0.015 |
| Research integrity | 0.013 | 0.025 |
| Insufficient payload (model declined to judge) | 0.020 | 0.031 |
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".