Cross-cultural adaptation and psychometric properties of the Iowa-Netherlands Comparison Orientation Measure for the Brazilian context
Bibliographic record
Abstract
INTRODUCTION: The Iowa-Netherlands Comparison Orientation Measure (INCOM) was developed to measure individual differences in social comparison orientation and has been widely used in research and various different settings. OBJECTIVES: The aim of this study was to adapt the online version of the INCOM and to evaluate its psychometric parameters when applied to a Brazilian population of university students. METHODS: The procedures were divided into two steps: step 1 - cross-cultural adaptation and analysis of content validity, and step 2 - assessment of psychometric characteristics. Step 1 comprised the processes of translation, evaluation by an expert committee, evaluation by the target population, and back-translation. For step 2, 1,065 university students were recruited and then factor analysis, analysis of reliability, and analysis of validity based on external measures were performed. RESULTS: The adaptation process yielded satisfactory results, including good indicators of content validity. Exploratory factor analysis revealed a two-dimensional structure and adequate factor loadings, except for item 11, which was excluded from the final version. Additionally, the final version of the scale had adequate fit indices (χ2 = 148.45, degrees of freedom [df] = 26; p < 0.001; root mean square error of approximation [RMSEA] = 0.06; comparative fit index [CFI] = 0.99; and Tucker-Lewis index [TLI] = 0.98). Evidence of reliability (Cronbach's alpha = 0.83) was observed and there were positive correlations with negative affect (r = 0.36) and negative correlations with positive affect and self-esteem (r = -0.15; r = -0.41, respectively). CONCLUSION: The Brazilian version of the INCOM presents satisfactory psychometric parameters and can thus be used to measure social comparison orientation.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".