scholarly journals Structural equation model trees.

2013 ◽  
Vol 18 (1) ◽  
pp. 71-86 ◽  
Author(s):  
Andreas M. Brandmaier ◽  
Timo von Oertzen ◽  
John J. McArdle ◽  
Ulman Lindenberger
Keyword(s):  
Structural Equation Model ◽  
Structural Equation ◽  
Equation Model ◽  
Model Trees

scholarly journals Predicting Students' Attitudes Toward Collaboration: Evidence From Structural Equation Model Trees and Forests

2021 ◽  
Vol 12 ◽  
Author(s):  
Jialing Li ◽  
Minqiang Zhang ◽  
Yixing Li ◽  
Feifei Huang ◽  
Wei Shao
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Structural Equation Model ◽  
Structural Equation ◽  
Equation Model ◽  
Data Driven ◽  
Group Model ◽  
Data Driven Decision Making ◽  
Model Trees ◽  
Students Attitudes

Numerous studies have shed some light on the importance of associated factors of collaborative attitudes. However, most previous studies aimed to explore the influence of these factors in isolation. With the strategy of data-driven decision making, the current study applied two data mining methods to elucidate the most significant factors of students' attitudes toward collaboration and group students to draw a concise model, which is beneficial for educators to focus on key factors and make effective interventions at a lower cost. Structural equation model trees (SEM trees) and structural equation model forests (SEM forests) were applied to the Program for International Student Assessment 2015 dataset (a total of 9,769 15-year-old students from China). By establishing the most important predictors and the splitting rules, these methods constructed multigroup common factor models of collaborative attitudes. The SEM trees showed that home educational resources (split by “above-average or not”), home possessions (split by “disadvantaged or not”), mother's education (split by “below high school or not”), and gender (split by “male or female”) were the most important predictors among the demographic variables, drawing a 5-group model. Among all the predictors, achievement motivation (split by “above-average or not”) and sense of belonging at school (split by “above-average or not” and “disadvantaged or not”) were the most important, drawing a 6-group model. The SEM forest findings proved the relative importance of these variables. This paper discusses various interpretations of these results and their implications for educators to formulate corresponding interventions. Methodologically, this research provides a data mining approach to discover important information from large-scale educational data, which might be a complementary approach to enhance data-driven decision making in education.

Mplus Trees: Structural Equation Model Trees Using Mplus

2020 ◽  
pp. 1-11
Author(s):  
Sarfaraz Serang ◽  
Ross Jacobucci ◽  
Gabriela Stegmann ◽  
Andreas M. Brandmaier ◽  
Demi Culianos ◽  
...  
Keyword(s):  
Structural Equation Model ◽  
Structural Equation ◽  
Equation Model ◽  
Model Trees
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