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

The Chinese University of Hong Kong via Coursera

Go to Course: https://www.coursera.org/learn/structural-equation-model-cantonese

Introduction

### Course Review: "Structural Equation Models and Their Applications | 结构方程模型及其应用 (粤语)" #### Overview The course "Structural Equation Models and Their Applications" offered on Coursera is a comprehensive introduction to one of the most advanced statistical methodologies applied in various fields such as sociology, psychology, education, economics, management, and marketing. Structural equation modeling (SEM) is a critical tool for analyzing complex relationships in multi-variate data and addressing the limitations of traditional regression and factor analyses. This course is presented in Cantonese and Mandarin, catering to learners who have foundational knowledge in statistics. It immerses students in the theoretical underpinnings and practical applications of SEM, utilizing the LISREL software. Topics covered include both exploratory and confirmatory factor analyses, the substantive principles of SEM, model specifications, and fitting models to empirical data. #### Course Structure The course consists of twelve lessons structured to systematically guide learners through the world of structural equation modeling. The lessons are as follows: 1. **Introduction to SEM** - An overview of the course content and structure. 2. **Exploratory and Confirmatory Factor Analysis** - Differences and applications of both approaches. 3. **Principles of SEM** - Basic concepts and statistical principles underlying SEM. 4. **Confirmatory Factor Analysis** - Applications and methodologies specific to confirmatory analysis. 5. **Multi-Group Models** - Exploring relationships across different groups with SEM. 6. **Full Models** - Building and analyzing comprehensive SEMs. 7. **Higher-Order Factor Analysis** - A more advanced look into analyzing layered relationships in data. 8. **Simplex Models** - Exploring simplex structures and their applications. 9. **Multi-Group SEM Analysis** - Advanced methodologies for comparing groups within SEM frameworks. 10. **Steps in SEM Modeling and Analysis** - Detailed workshop on executing SEM analyses. 11. **Data Challenges** - Addressing common data handling and fitting issues in SEM. 12. **Reading SPSS Data** - Techniques for importing and handling SPSS data sets. The course culminates in a final exam, allowing learners to solidify their knowledge and demonstrate their understanding of the material. #### Prerequisites Before enrolling, students are expected to have a foundation in statistical knowledge, including familiarity with tools like SPSS, SAS, or similar software, as well as a basic understanding of regression and exploratory factor analysis. This course is categorized as advanced, ensuring that participants are adequately prepared for the deep dive into SEM. #### Learning Outcomes Upon completion of the course, learners should be able to: - Understand and articulate the differences between traditional ANOVA and SEM. - Conduct exploratory and confirmatory analyses using practical applications. - Build and analyze structural equation models for diverse types of data. - Interpret the results of SEM analyses effectively, providing insights into the relationships present in the data. - Utilize LISREL software competently to execute SEM. #### Recommendation I highly recommend this course for anyone looking to deepen their analytical skills in data analysis using structural equation modeling. Its rigor and structured approach are perfect for graduate students, researchers, and professionals in quantitative fields. The inclusion of LISREL software training is particularly valuable, considering its widespread use in academic and applied research settings. If you are ready to tackle complex relationships in your data and want to upgrade your analytical toolkit, "Structural Equation Models and Their Applications" is definitely worth your time and investment! Embrace the challenge and significantly enhance your data analysis capabilities with this enlightening course.

Syllabus

课程资料

第一课：简介 (参考：第一章 引言)

第二课：探索性与验证性因子分析 (参考：第一章 引言)

第三课：SEM原理 (参考：第二章 结构方程模型简介)

第四课：验证性因子分析 (参考：第三章应用示范I 一、验证性因子分析)

第五课：多质多法模型 (参考：第三章应用示范I 二、多质多法模型)

第六课：全模型 (参考：第三章应用示范I 三、全模型)

第七课：高阶因子分析 (参考：第三章应用示范 四、高阶因子分析)

第八课：单纯形模型 (参考：第四章应用示范II：单纯形和多组模型 一、单纯形模型)

第九课：多组SEM分析 (参考：第四章应用示范II：单纯形和多组模型 二、多组验证性因子分析 三、多组分析：均值结构模型)

第十课：结构方程建模和分析步骤 (参考：第五章结构方程建模和分析步骤)

第十一课：涉及数据的问题 (参考：第六章专题讨论——涉及资料的问题 第七章专题讨论——涉及模型拟合的问题 第八章拟合指数)

第十二课：读取SPSS数据 (参考：附录III通过SPSS读取数据)

期末考