Zhu Yuhang, Cong Xianzhu, Qiu Zhenliang, Jeffrey Ricky, Li Ranran, Jing Li, Zhu Gaopei, Yang Xi, Li Shuang, Wang Jinling, Xu Xu, Zhu Hongliu, Wang Xinjian, Huang Ling, Sun Xueqin, Wu Di, Zhang Kai, Miao Xunhong, Wen Rui, Huang Qinglang, He Zhuang, Li Juan, Cosma Alina, Shi Fuyan, Wang Suzhen
Teaching and Research Section of Health Statistics, School of Public Health, Shandong Second Medical University, No. 7166 Baotong West Street, Weicheng, Weifang, Shandong, 261053, China.
No. 1 High School of Anhui Sixian County, No. 172 Sishui Boulevard, Hongcheng Sub-district, Sixian, Suzhou, Anhui, 234300, China.
BMC Psychol. 2025 Feb 5;13(1):100. doi: 10.1186/s40359-024-02136-3.
The Symptom Checklist (SCL) developed by the Health Behaviour in School‑aged Children (HBSC) study is widely used to capture the psychosomatic complaints (PSC) of non-clinical children and adolescents. Although its psychometric properties have been well established internationally, the performance of the Mandarin Chinese version remains unclear. This study evaluates the Mandarin Chinese HBSC-SCL's psychometric properties, develops its norm, and creates the corresponding scoring algorithm.
Data were collected from a two-wave cross-sectional survey conducted between June 20 and July 11, 2022, across eight Chinese Human Geography Regions (CHGRs). The sample included 3290 junior secondary school students, obtained through convenience sampling (first wave) and multistage, stratified, cluster sampling (second wave). The surveys were administered anonymously in the school setting, using a paper-and-pencil, self-administered questionnaire. The Mandarin Chinese HBSC-SCL's unidimensionality was verified using confirmatory factor analysis (CFA), and its psychometric properties were comprehensively evaluated using the partial credit model (PCM) of the Rasch measurement method. Based on the above scientific evidence, the population-based norm and norm-referenced scoring algorithm were developed and created.
The CFA confirmed that the HBSC-SCL can be considered unidimensional in the Chinese Mainland. Evidence-based on the six features of the Rasch model indicated that the Mandarin Chinese HBSC-SCL has satisfactory psychometric properties. All 5-category rating scales of eight items appropriately differentiated the students' PSC and demonstrated strong goodness-of-fit. This version also exhibited good unidimensionality and local independence. The Rasch model generated two kinds of reliability indicators, with the item indicators performing well. The person-item map demonstrated acceptable person and item matches, and provided new perspectives for future improvements. Additionally, no substantial uniform differential item functioning (UDIF) was detected across 13 groups (e.g., survey waves, gender, chronological age).
The Mandarin Chinese HBSC-SCL demonstrates satisfactory psychometric properties and performs well in the Chinese Mainland context. It provides concise self-reported PSC measures for junior secondary school students, potentially applicable to a broader Chinese-speaking population. Its ease of administration, scoring, and interpretation makes it suitable for routine school monitoring, large-scale population surveys, and clinical applications. Additionally, the population-based norm and norm-referenced scoring algorithm support the broader application of this version and offer new insights for interpreting PSC sum scores.
学龄儿童健康行为(HBSC)研究开发的症状清单(SCL)被广泛用于获取非临床儿童和青少年的心身症状(PSC)。尽管其心理测量特性在国际上已得到充分确立,但中文普通话版本的表现仍不明确。本研究评估了中文普通话版HBSC-SCL的心理测量特性,制定了其常模,并创建了相应的评分算法。
数据收集自2022年6月20日至7月11日在八个中国人文地理区域(CHGR)进行的两波横断面调查。样本包括3290名初中学生,通过便利抽样(第一波)和多阶段、分层、整群抽样(第二波)获得。调查在学校环境中以匿名方式进行,使用纸笔自填问卷。使用验证性因素分析(CFA)验证中文普通话版HBSC-SCL的单维度性,并使用拉施测量方法的部分计分模型(PCM)全面评估其心理测量特性。基于上述科学证据,制定并创建了基于人群的常模和常模参照评分算法。
CFA证实HBSC-SCL在中国大陆可被视为单维度的。基于拉施模型的六个特征的证据表明,中文普通话版HBSC-SCL具有令人满意的心理测量特性。八个项目的所有5级评定量表都能适当地区分学生的PSC,并显示出很强的拟合优度。该版本还表现出良好的单维度性和局部独立性。拉施模型生成了两种可靠性指标,项目指标表现良好。人-项目图显示了可接受的人与项目匹配,并为未来的改进提供了新的视角。此外,在13个组(如调查波次、性别、实足年龄)中未检测到实质性的均匀差异项目功能(UDIF)。
中文普通话版HBSC-SCL表现出令人满意的心理测量特性,在中国大陆背景下表现良好。它为初中学生提供了简洁的自我报告PSC测量方法,可能适用于更广泛的华语人群。其易于实施评分和解释的特点使其适用于学校常规监测、大规模人群调查和临床应用。此外,基于人群的常模和常模参照评分算法支持该版本的更广泛应用,并为解释PSC总分提供了新的见解。