Structural Equation Modeling (SEM) is a statistical approach to testing hypothesis about the relationships among observed and latent variables. The use of SEM in research has increased in psychology, sociology, and economics in recent years. In particular educational researchers try to obtain the complete image of the process of education through the measurement of personality differences, learning environment, motivation levels and host of other variables that affect the teaching and learning process. With the use of survey instruments and interviews with students, teachers and other stakeholders as a lens, educators can assess and gain valuable information about the social ecology of the classrooms that could help in improving the instructional approach, classroom management and the learning organizations. A considerable number of research have been conducted to identify the factors and interactions between students’ characteristics, personal preferences, affective traits, study skills, and various other factors that could help in better educational performance. In recent years, educational researchers use Structural Equation Modeling (SEM) as a statistical technique to explore the complex and dynamic nature of interactions in educational research and practice. SEM is becoming a powerful analytical tool and making methodological advances in multivariate analysis. This book presents the collective works on concepts, methodologies and applications of SEM in educational research and practice. The anthology of current research described in this book will be a valuable resource for the next generation educational practitioners.
Inhoudsopgave
Part I – Theoretical Foundations; Chapter 1 Applying Structural Equation Modeling (SEM) in Educational Research: An Introduction; Chapter 2 Structural Equation Modeling in Educational Research: A Primer; Part II – Structural Equation Modeling in Learning Environment Research; Chapter 3 Teachers’ Perceptions of the School as a Learning Environment for Practice-based Research: Testing a Model That Describes Relations between Input, Process and Outcome Variables; Chapter 4 Development of an English Classroom Environment Inventory and Its Application in China; The Effects of Psychosocial Learning Environment on Students’ Attitudes towards Mathematics; Investigating Relationships between the Psychosocial Learning Environment, Student Motivation and Self-Regulation; Chapter 7 In/Out-of-School Learning Environment and SEM Analyses on Attitude towards School; Chapter 8 Development of Generic Capabilities in Teaching and Learning Environments at the Associate Degree Level; Part III – Structural Equation Modeling in Educational Practice; Chapter 9 Latent Variable Modeling in Educational Psychology: Insights from a Motivation and Engagement Research Program; Chapter 10 Linking Teaching and Learning Environment Variables to Higher Order Thinking Skills: A Structural Equation Modeling Approach; Chapter 11 Influencing Group Decisions by Gaining Respect of Group Members in E-Learning and Blended Learning Environments: A Path Model Analysis; Chapter 12 Investigating Factorial Invariance of Teacher Climate Factors across School Organizational Levels; Part IV – Conclusion; Chapter 13 Structural Equation Modeling Approaches in Educational Research and Practice; Author Biographies.
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