Structural Equation Modeling in Educational Research

Concepts and Applications

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Over the years, researchers have developed statistical methods to help them investigate and interpret issues of interest in many discipline areas. These methods range from descriptive to inferential to multivariate statistics. As the psychometrics measures in education become more complex, vigorous and robust methods were needed in order to represent research data efficiently. One such method is Structural Equation Modeling (SEM).
SEM is a statistical technique that allows the simultaneous analysis of a series of structural equations. It also allows a dependent variable in one equation to become an independent variable in another equation. It is a comprehensive statistical approach to testing hypotheses about relations among observed and latent variables. SEM is commonly known as causal modeling, or path analysis, which hypothesizes causal relationships among variables and tests the causal models with a linear equation system. As educational research questions become more complex, they need to be evaluated with more sophisticated tools. The pervasive use of SEM in the literature has shown that SEM has a potential to be of assistance to modern educational researchers.
This book will bring together prominent educators and researchers from around the world to share their contemporary research on structural equation modeling in educational settings. The chapters provide information on recent trends and developments and effective applications of the different models to answer various educational research questions. This book is a critical and specialized source that describes recent advances in SEM in international academia.

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Preliminary Material
著者: Myint Swe Khine and Issa M. Saleh
页码: i–ix
Adding Value to International Professional Development
著者: Bob Burstow
页码: 225–243
List of Contributors
著者: Myint Swe Khine and Issa M. Saleh
页码: 367–373
Index
著者: Myint Swe Khine and Issa M. Saleh
页码: 375–377
Educational Researchers and their students
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