Abstract: Objective To validate and evaluate the structure of Chinese Preschoolers′ Caregivers′ Feeding Behavior Scale(CPCFBS) using network analysis, so as to provide reference for further improvement and revision of the scale. Methods Network structure was estimated using Gaussian Graphical Model with a dataset of feeding behaviors in 768 preschoolers′ caregivers. Dimensionality was detected using exploratory graph analysis(EGA). The network structural consistency was tested using EGA bootstrap. Structural reliability and validity were examined using model fit indices and reliability coefficients. Results Based on the network loadings and structure consistency results, a seven-dimensional EGA network containing 34 items was explored. Compared with the original scale structure, one unstable item was deleted, and some items of the dimensions of "Encourage Healthy Eating" and "Behavior-restricted Feeding" were re-clustered, while the rest of the network dimensions remained unchanged. The structure consistency evaluation indexes were all greater than 0.75, meaning that the structure was stable. The absolute fit and relative fit of EGA structure were better than the original structure. The EGA structure had better reliability than the original structure. Conclusion After optimizing the structure of CPCFBS and improving the stability of the scale by network analysis, the revised CPCFBS is more applicable to the evaluation of feeding behaviors in China.

Key words: feeding behavior, scale structure, network analysis

摘要： 目的 运用网络分析法对《学龄前儿童照护人喂养行为量表》(CPCFBS)的结构进行验证与评估,为进一步修订完善量表提供依据。方法 采用高斯图域模型对768名学龄前儿童照护人喂养行为量表数据进行网络结构估计, 使用探索性图域分析模型检测量表网络维度结构,采用EGA bootstrap指标评价结构一致性;使用模型拟合指数和信度系数检验量表信效度。结果 依据网络载荷值和结构一致性结果,网络模型检测出一个34条目7维度的网络结构。与原始量表结构比较,删除了1个条目,“饮食观念限制”和“饮食行为限制”维度部分条目重新聚类,其余维度结构不变。量表网络结构一致性指标全部大于0.75,结构稳定。网络结构的模型拟合度和信度指标优于原始量表结构。结论 网络分析模型进一步优化了CPCFBS结构,提高了量表稳定性,修订后更适用于我国学龄前儿童照护人喂养行为的评价。

关键词: 喂养行为, 量表结构, 网络分析