Structural Equation Model and its Applications | 结构方程模型及其应用 (普通话)

Go to Course: https://www.coursera.org/learn/jiegou-fangcheng-moxing

Introduction

### Course Review: Structural Equation Model and its Applications | 结构方程模型及其应用 (普通话) **Overview:** The course "Structural Equation Model and its Applications" (SEM) offered on Coursera is an excellent opportunity for anyone interested in advanced data analysis techniques in various fields such as sociology, psychology, education, economics, management, and marketing. This course delves into structural equation modeling, a cutting-edge statistical method that addresses the limitations of traditional regression and factor analyses, providing comprehensive insights into complex interdependencies among multiple variables. With a robust curriculum that blends theoretical knowledge with practical application, students will explore a range of topics including variance analysis, regression analysis, path analysis, and factor analysis. The course also introduces the LISREL software, widely recognized for its capabilities in conducting SEM, thereby equipping participants with essential tools for multi-level data and longitudinal data analysis. **Course Structure:** The syllabus is logically structured, progressing from basic concepts to more advanced applications. Below is a summary of the weekly modules: 1. **Introduction**: An overview of SEM and its significance in data analysis. 2. **Exploratory and Confirmatory Factor Analysis**: Techniques for identifying underlying relationships in data. 3. **Principles of SEM**: Fundamental statistical principles that guide SEM. 4. **Confirmatory Factor Analysis**: A deep dive into validating factor structures. 5. **Multi Trait-Multi Method Models**: Analysis of complex models that consider multiple measurement methods. 6. **Overall Model**: Concepts related to comprehensive structural models. 7. **Higher-Order Factor Analysis**: Techniques for dealing with multifaceted models. 8. **Simplex Models**: An introduction to simplex modeling. 9. **Multi-group SEM Analysis**: Insights into conducting SEM across different groups. 10. **Modeling and Analysis Steps**: A detailed guide through the process of performing SEM. 11. **Data Issues**: Practical discussions on data challenges and model fitting problems. 12. **Reading SPSS Data**: Techniques for incorporating SPSS datasets into SEM analysis. 13. **Final Examination**: An assessment to gauge understanding and application of the course material. **Who Should Take This Course?** This course is ideally suited for individuals with a foundational understanding of statistics, including concepts such as standard deviation, t-tests, and correlation coefficients. Participants should be familiar with both regression and factor analysis to fully benefit from the course discussions. **Recommended Resources:** The accompanying textbook, "Structural Equation Model and Its Applications," serves as a valuable resource for deeper understanding and practical examples using LISREL software. **Why You Should Take This Course:** 1. **Comprehensive Understanding**: The course's structured approach ensures participants develop a solid understanding of SEM, making it valuable for those in research-oriented careers. 2. **Practical Application**: By focusing on software application (LISREL), students will leave the course with applicable skills for real-world data analysis. 3. **Diverse Applications**: The techniques learned can be applied in various fields, making the knowledge acquired transferable across disciplines. Whether you're a graduate student, a researcher, or a professional in a data analytics role, "Structural Equation Model and its Applications" is an indispensable addition to your skill set. The course not only enhances your methodological arsenal but also prepares you for the complexities inherent in modern-day data analysis. I wholeheartedly recommend it for anyone looking to advance their analytical capabilities in social science research or related areas.

Syllabus

课程资料