Cracks, Gaps, and Holes in Validation Practice as Evidenced From a Validation Synthesis of the English Version of the Rosenberg Self-Esteem Scale
Bibliographic record
Abstract
Abstract: We conducted a validation synthesis of the English version of the Rosenberg Self-Esteem Scale. Twenty-eight studies met our criteria for inclusion. Using the 2014 Standards for Educational and Psychological Testing ( AERA, APA, & NCME, 2014 ) as a frame of reference, we examined the sources of validity and reliability evidence gathered; identified trends, gaps, and issues with this evidence; and made recommendations to guide future validation practice. None of the studies directly cited any edition of the Standards. Validity evidence has relied predominantly on internal structure, followed by relations to other variables, and little to no other sources of evidence. Reliability evidence was reported in most studies but consisted exclusively of internal consistency estimates. Increased awareness and use of the Standards and better training in measurement could help improve validation practice. Recommendations to researchers include becoming more familiar with modern validity theory, paying more attention to sources of validity evidence that address score meaning, presenting clear and explicit a priori expectations of the evidence needed to support the validity of intended inferences, and evaluating obtained evidence with a more critical eye to determine the degree to which theoretical rationales and the evidence support the meaning of test scores for a given use, sample, and context.
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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 | 0.557 | 0.693 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".