Developing a Validity Argument Through Abductive Reasoning with an Empirical Demonstration of the Latent Class Analysis
Bibliographic record
Abstract
This article proposes and demonstrates a methodology for test score validation through abductive reasoning. It describes how abductive reasoning can be utilized in support of the claims made about test score validity. This methodology is demonstrated with a real data example of the Canadian English Language Proficiency Index Program (CELPIP)-General test—a program assessing functional English language ability in the community and workplace. Abductive reasoning seeks the enabling conditions through which a claim about a person's ability makes sense. For example, it makes sense that a person has strong functional language proficiency if he or she has been regularly using English to write emails and meet with colleagues at work. A valid test score should be affected by the extent of a person's engagement with such enabling conditions. Empirical evidence that warrants such an abductively reasoned claim is illustrated through a latent class analysis within a structural equation model. Evidence is examined to investigate whether certain classes of test takers who have been differentially engaging in the enabling conditions do, in fact, predict a person's CELPIP-General performance. The steps of the methodology are summarized in the closing section.
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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 | 0.197 | 0.446 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".