Psychometric properties of the Chinese version of the Quiet Quitting Scale

Abstract

Background

The phenomenon of quiet quitting has attracted considerable attention within the nursing community. Elevated quiet quitting levels can weaken the work environment, reducing healthcare efficiency and quality. However, appropriate assessment tools to measure this phenomenon remain unavailable in China. This study aimed to translate the Quiet Quitting Scale into Chinese and evaluate its psychometric properties.

Methods

The Brislin translation model was used to translate the Quiet Quitting Scale into Chinese. A total of 420 nurses were recruited from hospitals in Central China. Item analysis was conducted using the Critical Rate, correlation coefficient, and internal consistency methods. Reliability was evaluated through internal consistency, split-half reliability, and test-retest reliability. The content validity of the Chinese version was assessed using the Delphi method. Exploratory factor analysis and confirmatory factor analysis were performed to evaluate the construct validity of the Chinese version. Data were analyzed using SPSS 29.0 and Mplus 8.0 software.

Results

The Chinese version of the Quiet Quitting Scale demonstrated robust content validity (S-CVI/Ave: 0.989, CVR:0.800-1.000). The three-factors model was obtained by using exploratory factor analysis, explaining 77.93%, and confirmatory factor analysis supported acceptable construct validity (χ2/df = 2.224, CFI = 0.963, TLI = 0.945, RMSEA = 0.077). Additionally, the scale demonstrated a Cronbach’s α of 0.856, split-half reliability was 0.921, test-retest reliability was 0.851, and McDonald’s Omega was 0.887.

Conclusions

The Chinese version of the Quiet Quitting Scale (QQS) demonstrated strong reliability and validity, making it a valuable tool for assessing quiet quitting behaviors among nurses in both clinical and managerial contexts. This scale can help identify at-risk individuals and support the development of targeted interventions aimed at improving work quality and efficiency. Future research should assess the scale’s applicability across various healthcare settings and its long-term impact.