five

Measurement invariance across ages.

收藏
Figshare2025-09-26 更新2026-04-28 收录
下载链接:
https://figshare.com/articles/dataset/Measurement_invariance_across_ages_/30222895
下载链接
链接失效反馈
官方服务:
资源简介:
The primary goal of this study was to examine children’s social skills (SS) development by comparing various factors (countries, genders, and age groups). Additionally, this study aimed to analyze how background variables predict the ongoing development of children’s SS. The study involved a total of 3050 Hungarian-speaking children (4–8 years of age) residing in Hungary and Slovakia. First, we investigated measurement invariance (MI) and Latent mean difference (LMD) of the SS assessment test (a part of the DIFER test – Diagnostic Systems for Assessing Development) to assess school readiness in Hungarian preschool children. The findings showed that the SS assessment test is reliable and consistent across different groups of Hungarian preschool children, as it demonstrated MI across all four levels (configural, metric, scalar, and residual) and significant LMD. It was also found that children from Hungary demonstrated superior SS when compared to their counterparts from Slovakia. Furthermore, the analysis of gender revealed that female students exhibited more advanced SS than male students. Additionally, older children displayed significantly higher levels of SS compared to younger children within their respective age groups. Although parental education emerged as a significant predictor of children’s SS development across the entire sample, distinct variations were observed when analyzing each country separately. In both countries, the gender and age of students were identified as highly significant factors contributing to their SS development. Therefore, this study carries substantial importance for educators and researchers alike, offering valuable insights into the assessment and cultivation of students’ SS development.
创建时间:
2025-09-26
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作