Adaptation and Validation of the Essential Resilience Scale for Chinese College Students
Authors
Department of Preschool Education, ZIBO Normal College (China)
Faculty of Education Science & Technology, Universiti Teknologi Malaysia (Malaysia)
Article Information
DOI: 10.47772/IJRISS.2026.100700810
Subject Category: Education
Volume/Issue: 10/7 | Page No: 12025-12038
Publication Timeline
Submitted: 2026-07-27
Accepted: 2026-08-01
Published: 2026-08-13
Abstract
In the digital-intelligent era, college students are facing increasingly complex psychological pressures, interpersonal adaptation challenges, and risks of covert bullying. As an important protective psychological resource, psychological resilience therefore requires effective measurement tools for assessment. This study aimed to adapt and validate the Essential Resilience Scale (ERS) among Chinese college students, providing a reliable measurement tool for subsequent research on psychological resilience, covert bullying prevention, and dance/movement therapy interventions. The original ERS consists of 15 items across three dimensions: Physical Resilience, Emotional Resilience, and Social Resilience. To enhance its contextual relevance for college students, three items were slightly revised while retaining the original theoretical structure. A total of 561 college students from Shandong Province, China, participated in the study. Overall, 94.1% of the participants were female, 67.0% were aged 20–21 years, and 63.1% were second-year students. A two-phase research design was adopted. In the pilot study, 330 valid responses were collected and used for item analysis and exploratory factor analysis. In the formal study, 231 valid responses were collected and used for confirmatory factor analysis, convergent validity, discriminant validity, and reliability testing. The item analysis results showed that all items demonstrated satisfactory discriminatory power, with CR values ranging from 9.24 to 13.59 and corrected item-total correlations ranging from 0.506 to 0.632. Exploratory factor analysis using principal axis factoring with direct oblimin rotation supported a clear three-factor structure, accounting for 57.720% of the variance, with pattern coefficients ranging from .626 to .824. Confirmatory factor analysis further indicated that the three-factor model fitted the data well, χ²(87) = 90.830, p = .368, χ²/df = 1.044, CFI = .998, TLI = .998, and RMSEA = .014. The scale also demonstrated satisfactory convergent validity, discriminant validity, and internal consistency reliability. Overall, the adapted ERS showed good psychometric properties and can be considered a reliable and valid instrument for assessing psychological resilience among Chinese college students.
Keywords
Essential Resilience Scale; psychological resilience; college students; scale adaptation
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References
1. Bartlett, M. S. (1950). Tests of significance in factor analysis. British Journal of Mathematical and Statistical Psychology, 3(2), 77–85. [Google Scholar] [Crossref]
2. Chen, J., Li, H., & Xue, Y. (2021). Intervention of expressive arts therapy on depression and anxiety among college students: A case study of Beijing Normal University. Art Education, (12), 38–41. [Google Scholar] [Crossref]
3. Chen, X., Wang, Y., & Yan, Y. (2015). The essential resilience scale: instrument development and prediction of perceived health and behaviour. Stress and Health, 32(5), 533–542. https://doi.org/10.1002/smi.2659 [Google Scholar] [Crossref]
4. Field, A. (2018). Discovering Statistics Using SPSS (5th ed.). Sage. [Google Scholar] [Crossref]
5. Fornell, C., & Larcker, D. F. (1981). Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics. Journal of Marketing Research, 18(3), 382. https://doi.org/10.2307/3150980 [Google Scholar] [Crossref]
6. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage. [Google Scholar] [Crossref]
7. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135. [Google Scholar] [Crossref]
8. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. [Google Scholar] [Crossref]
9. Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36. [Google Scholar] [Crossref]
10. Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). Guilford Press. [Google Scholar] [Crossref]
11. Li, L. (2024). The relationship between malicious creativity and bullying among college students and its intervention study [Master’s thesis, Chongqing Normal University]. [Google Scholar] [Crossref]
12. Lynn, M. R. (1986). Determination and quantification of content validity. Nursing Research, 35(6), 382-386. https://doi.org/10.1097/00006199-198611000-00017 [Google Scholar] [Crossref]
13. Ma, M., & Cai, M. (2025). Investigation of the current situation of campus bullying and research on legal prevention and control: Based on a survey of four universities in Gansu Province. Society and Public Welfare, 135–137. [Google Scholar] [Crossref]
14. Magulandanm, Liu, J., Chen, Z. L., & Zhao, Q. (2019). The effects of dance therapy on interpersonal communication ability and emotions among college students. Journal of Shanghai University of Sport, 43(2), 86–90. [Google Scholar] [Crossref]
15. Mao, Q., Mastnak, W., & Guan, R. (2025). Chinese ethnic dance therapy: Cultural anthropology and health science perspectives on Tujia ethnic dances. Frontiers in Psychology, 16, Article 1561150. [Google Scholar] [Crossref]
16. Mastnak, W., & Mao, Q. (2021). Review and evaluation of dance therapy. Journal of Beijing Dance Academy, (1), 111–120. [Google Scholar] [Crossref]
17. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric Theory (3rd ed.). McGraw-Hill. [Google Scholar] [Crossref]
18. Pallant, J. (2020). SPSS Survival Manual (7th ed.). Open University Press. [Google Scholar] [Crossref]
19. Polit, D. F., & Beck, C. T. (2006). The content validity index: Are you sure you know what’s being reported? Critique and recommendations. Research in Nursing & Health, 29(5), 489–497. [Google Scholar] [Crossref]
20. Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson. [Google Scholar] [Crossref]
21. Tang, J., & Tang, Y. (2026). The relationship between moral disengagement in campus bullying and cyberbullying behavior among college students. Psychological Monthly, 21(5), 133–136. [Google Scholar] [Crossref]
22. Yue, L. (2025, November 13). Mental health education for college students in the digital-intelligent era. Social Sciences Weekly, p. 005. [Google Scholar] [Crossref]
23. Zhang, W., Zhang, M., & Pan, B. (2026). Campus bullying research in China over the past three decades: Review and prospects. Psychological Exploration, 46(1), 23–30. [Google Scholar] [Crossref]
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