Estimating and validating the structure of feeding behavior networks

Purpose: Network analysis has been widely used in psychometrics over the past decade, yet it is unknown that whether this methodology could be applied in the field of child health assessment such as caregivers’ feeding behavior and child eating behavior. Our study leveraged network psychometrics method to estimating and examining the network structure of Chinese Preschoolers’ Caregivers’ Feeding Behavior Scale (CPCFBS), and compared the applicability of network methods in the feeding behavior scale. Methods: The CPCFBS was previously applied in a sample of 768 preschoolers’ caregivers, used to estimate the structure of feeding behavior networks. Network structure was estimated with Gaussian Graphical Model. Dimensionality was detected using Exploratory Graph Analysis (EGA). The network structural consistency was tested using EGA bootstrap. The network structure was compared with the original structure using model fit indices and reliability. Results: A seven-dimensional EGA network was explored after rearranging four items and deleting one item with unstable structural consistency. The absolute fit and relative fit of EGA structure were better than the original structure. The EGA structure had nearly same values of the reliability with the original structure. Conclusion: Our study presented a novel perspective for feeding behavior analytical strategies, and demonstrated that network analysis was applicable and superior in exploring the structure of feeding behavior scales. Level of evidence: Level V, cross-sectional descriptive study.