Validation and evaluation of the structure of Chinese Preschoolers′ Caregivers′ Feeding Behavior Scale based on network analysis

ZHANG Hao, ZHANG Hai-yue, YUAN Jing, ZHANG Yu-hai, JIANG Xun, SHANG Lei

Chinese Journal of Child Health Care ›› 2023, Vol. 31 ›› Issue (1) : 15-20.

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Chinese Journal of Child Health Care ›› 2023, Vol. 31 ›› Issue (1) : 15-20. DOI: 10.11852/zgetbjzz2022-0879
Original Articles

Validation and evaluation of the structure of Chinese Preschoolers′ Caregivers′ Feeding Behavior Scale based on network analysis

  • ZHANG Hao1,2, ZHANG Hai-yue1, YUAN Jing3, ZHANG Yu-hai1, JIANG Xun4, SHANG Lei1
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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

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ZHANG Hao, ZHANG Hai-yue, YUAN Jing, ZHANG Yu-hai, JIANG Xun, SHANG Lei. Validation and evaluation of the structure of Chinese Preschoolers′ Caregivers′ Feeding Behavior Scale based on network analysis[J]. Chinese Journal of Child Health Care. 2023, 31(1): 15-20 https://doi.org/10.11852/zgetbjzz2022-0879

References

[1] Scaglioni S, De Cosmi V, Ciappolino V, et al. Factors influencing children′s eating behaviours[J]. Nutrients, 2018, 10(6):706.
[2] 张静驰,陈轩,朱继文,等.母亲喂养行为对学前儿童饮食行为的影响[J].中国妇幼保健,2020,35(18):3400-3403.
Zhang JC, Chen X, Zhu JW, et al. Effect of mother′s feeding behavior on the dietary behavior of preschool children[J]. Matern Child Health Care China, 2020,35(18):3400-3403.
[3] Beckers D, Karssen LT, Vink JM, et al. Food parenting practices and children′s weight outcomes:A systematic review of prospective studies[J]. Appetite, 2021, 158:105010.
[4] 袁静,江逊,尚磊.喂养行为评价量表研究进展[J].中国妇幼健康研究,2019, 30(5):527-531.
Yuan J, Jiang X, Shang L. Research progress of feeding behavior evaluation methods[J]. Chin J Matern Child Health Res, 2019, 30(5):527-531.
[5] Guo S, Wang Y, Fries LR, et al. Infant and preschooler feeding behaviors in Chinese families:A systematic review[J]. Appetite, 2022, 168:105768.
[6] 周楠. 我国学龄前儿童家庭喂养状况研究进展[J].中国学校卫生,2017, 38(11):1757-1760.
Zhou N. Research progress on preschoolers′ family feeding status in China[J]. Chin J Sch Health,2017, 38(11):1757-1760.
[7] Yuan J, Zhang Y, Xu T, et al. Development and preliminary evaluation of Chinese Preschoolers′ Caregivers′ Feeding Behavior Scale[J]. J Acad Nutr Diet, 2019, 119(11):1890-1902.
[8] 孙晓军,周宗奎.探索性因子分析及其在应用中存在的主要问题[J].心理科学, 2005, 28(6):162-164,170.
Sun XJ, Zhou ZK. Exploratory factor analysis and its main problems in application[J]. Psychol Sci, 2005, 28(6):162-164,170.
[9] Christodoulou A, Michaelides M, Karekla M. Network analysis:A new psychometric approach to examine the underlying ACT model components[J]. J Contextual Behav Sci, 2019, 12:285-289.
[10] Epskamp S, Waldorp LJ, Mõttus R, et al. The gaussian graphical model in cross-sectional and time-series data[J]. Multivariate Behav Res, 2018, 53(4):453-480.
[11] Golino HF, Epskamp S. Exploratory graph analysis:A new approach for estimating the number of dimensions in psychological research[J]. PLoS One, 2017, 12(6):e0174035.
[12] Epskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy:A tutorial paper[J]. Behav Res Methods, 2018, 50(1):195-212.
[13] Golino H, Shi D, Christensen AP, et al. Investigating the performance of exploratory graph analysis and traditional techniques to identify the number of latent factors:A simulation and tutorial[J]. Psychol Methods, 2020, 25(3):292-320.
[14] Robinaugh DJ, Hoekstra RHA, Toner ER, et al. The network approach to psychopathology:A review of the literature 2008—2018 and an agenda for future research[J]. Psychol Med, 2020, 50(3):353-366.
[15] Borsboom D, Deserno MK, Rhemtulla M, et al. Network analysis of multivariate data in psychological science[J]. Nat Rev Methods Primers, 2021, 1:58.
[16] Savage JS, Rollins BY, Kugler KC, et al. Development of a theory-based questionnaire to assess structure and control in parent feeding(SCPF)[J]. Int J Behav Nutr Phys Act, 2017, 14(1):9.
[17] Kan KJ, de Jonge H, van der Maas HLJ, et al. How to compare psychometric factor and network models[J]. J Intell, 2020, 8(4):35.
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