Statistical Modeling Techniques in Educational Research: Data Mining, Structural Equation Models and Hierarchical Linear Models

Statistical Modeling Techniques in Educational Research:Data Mining, Structural Equation Models and Hierarchical Linear Models Educational research has advanced theoretical approaches by defining and designing integrated models oriented up to the study of relationships between constructs not directly observable embedded in complex contexts. In parallel, data analysis software has allowed testing these complex models in reasonable computing environments in terms of time and effort. This techno-scientific revolution in the developed software for quantitative data analysis has transformed scientific practice in educational research. In this paper is assumed the growing trend in educational research on design and fit of comprehensive models of educational phenomena. More specifically we will focus on the statistical decision trees, in structural equation models and multilevel models, which come to dominate the landscape of educational research since the mid 70's and have recently been generalized thanks to technological development. The impact on educational research has been a widespread use of sophisticated statistical models than ever so far. It is necessary to think about what theoretically grounded models have to be tested with statistical tools as powerful as those being described in this paper.