Validity of Test Score Interpretations for Three Self-Concept Measures Based on Differing Theoretical Models
Bibliographic record
Abstract
Three theoretical models have been proposed to represent self-concept: (a) unidimensional; (b) multidimensional; (c) multidimensional hierarchical. Inventories have been developed under each of the three competing theoretical models; which model best represents self-concept is unclear. Typically, self-concept construct validation has utilized various approaches including correlational, multitrait-multimethod, and factor analytic methods. Another method, however, for assessing validity would be to determine the consequences of score interpretations using different measures specific to each of the theoretical models. This paper examined Messick's notion (1989) of the validity of test-score interpretations as applied to three of the most widely used measures derived under each of the three different theoretical models of self-concept. Results suggest that overall multidimensional measures are more consistent in classifying individual's self-concept than unidimensional measures.
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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 | 0.042 | 0.210 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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; 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".