An Examination of Different Scale Usage Correction Procedures to Enhance Cross-Cultural Data Comparability
Bibliographic record
Abstract
This study aims to examine different scale usage correction procedures that are meant to enhance the cross-cultural comparability of Likert scale data. Specifically, we examined a priori study design (i.e., anchoring vignettes and overclaiming) and post hoc statistical procedures (i.e., ipsatization and extreme response style correction) in data from the 2012 Programme for International Student Assessment across 64 countries. We analyzed both original item responses and corrected item scores from two targeted scales in an integrative fashion by using multilevel confirmatory factor analysis and multilevel regressions. Results indicate that mean levels and structural relations varied across the correction procedures, although the psychological meaning of the constructs examined did not change. Furthermore, scores were least affected by these procedures for females who did not repeat a grade and students with higher math achievement. We discuss the implications of our findings and offer recommendations for researchers who are considering scale usage correction procedures.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".