When Assessment Data Are Words: Validity Evidence for Qualitative Educational Assessments
Bibliographic record
Abstract
Quantitative scores fail to capture all important features of learner performance. This awareness has led to increased use of qualitative data when assessing health professionals. Yet the use of qualitative assessments is hampered by incomplete understanding of their role in forming judgments, and lack of consensus in how to appraise the rigor of judgments therein derived. The authors articulate the role of qualitative assessment as part of a comprehensive program of assessment, and translate the concept of validity to apply to judgments arising from qualitative assessments. They first identify standards for rigor in qualitative research, and then use two contemporary assessment validity frameworks to reorganize these standards for application to qualitative assessment.Standards for rigor in qualitative research include responsiveness, reflexivity, purposive sampling, thick description, triangulation, transparency, and transferability. These standards can be reframed using Messick's five sources of validity evidence (content, response process, internal structure, relationships with other variables, and consequences) and Kane's four inferences in validation (scoring, generalization, extrapolation, and implications). Evidence can be collected and evaluated for each evidence source or inference. The authors illustrate this approach using published research on learning portfolios.The authors advocate a "methods-neutral" approach to assessment, in which a clearly stated purpose determines the nature of and approach to data collection and analysis. Increased use of qualitative assessments will necessitate more rigorous judgments of the defensibility (validity) of inferences and decisions. Evidence should be strategically sought to inform a coherent validity argument.
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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 | 0.700 | 0.923 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.022 | 0.016 |
| Science and technology studies | 0.010 | 0.052 |
| Scholarly communication | 0.037 | 0.042 |
| Open science | 0.008 | 0.023 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".