Validation of tests using an argument-based approach: a review based on the PRISMA model
Bibliographic record
Abstract
In test assessment, the validation process is crucial for ensuring the proper interpretation and use of scores, as well as establishing reliability in the results. The Argument-Based Approach (ABA), introduced by Kane, provides a structured framework for validation by defining inferences, warrants, and assumptions. Although widely recognized, research indicates that the empirical application of ABA remains limited, with many studies neglecting evaluations of clarity, coherence, and plausibility. This study conducted a systematic review using the PRISMA framework, analyzing 28 empirical articles about ABA published between 2014 and 2023. Articles were sourced from databases such as ERIC and Web of Science, selected based on quality and relevance to educational contexts. Most of the reviewed studies originated from North America and focused on English language education and medicine. Common inferences included Generalization and Extrapolation; however, few studies addressed warrants and assumptions, reflecting a partial application of ABA and insufficient evaluations of validity. Despite its utility, the ABA's implementation is hindered by gaps in evaluations focusing on clarity, coherence, and plausibility. Standardized guidelines are recommended for assessing these aspects in future research, expanding the framework's applicability to strengthen its empirical foundation.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".