How essential is the essential resilience scale? Differential item functioning for Chinese and English versions
Bibliographic record
Abstract
The Essential Resilience Scale (ERS) is a locally developed measure conceptualized by Chinese researchers and demonstrated strong psychometric properties in Chinese residents. To date, no study has tested the adaptation of the ERS in another culture and measurement equivalence of the locally developed Chinese ERS and the English adaptation proposed by Chen and colleagues (2016). Thus, the present study aims to contribute to the literature through (1) assessing the psychometric properties of the Chinese and English versions of the ERS using item response theory (Stage 1), testing the measurement equivalence of the Chinese and English version of the ERS using differential item functioning (Stage 2), and establish its criterion validity through the associations with the Resiliency Scale for Young Adults (RSYA) and Satisfaction with Life Scale (SWLS; Stage 3). \n \nUndergraduate students from China (N=375) and Canada (N=378) were recruited to complete the ERS and criterion validity measures (i.e., Resiliency Scale for Young Adults, Satisfaction with Life Scale). The item response theory parameterization using Samejima’s (1969) graded response model showed discrimination parameters ranged from 1.02 to 2.22 and 0.32 to 1.97 in the Chinese and English versions, respectively. Differential item functioning (DIF) analyses showed six of 15 items were flagged for DIF; five of these items showing nonuniform DIF revealed more discriminant items for the Chinese version compared to the English version. Cross-cultural comparisons using the ERS could present biases if not corrected for DIF. Finally, both the Chinese and English versions of the ERS were associated with Resiliency Scale for Young Adults subscales and satisfaction with life in the expected directions. \n \nAlthough, the Chinese version of the ERS demonstrated strong psychometric properties, the present results suggest its English counterpart may not be a suitable measure for resiliency. This study contributes to existing research in examining trait resilience with a locally developed measure in China and emphasized on the importance of evaluating measurement equivalence cross-culturally whenever adapting a measure for use with another cultural, linguistic, or unique group.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".