Abstract
Many previous studies did not consider the nested structure of the school. If we ignore the multi-level nested structure of the group feature, it can be an ecological or atomistic fallacy. It can mislead the inaccurate conclusion while we are interpreting the result of the analysis. In this study, we apply a multi-level structural equation approaches to find out the modeling of smart learning intention. Samples are 2,670 data from Heo and Goo(2017) study. We used Mplus 8 for analysis of multi-level structural equation modeling.BR From the result, model 2 with school type is fitter than model 1. We can find that all effects are significant in the students’ level. We also find there is a significant difference in usefulness for the school type.
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