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Prof. Kit Tai Hau 侯傑泰

课程介绍:

在社会学、心理学、教育学、经济学、管理学、市场学等研究领域的数据分析中,结构方程建模是当前最前沿的统计方法中应用最广、研究最多的一个。它包含了方差分析、回归分析、路径分析和因子分析,弥补了传统回归分析和因子分析的不足,可以分析多因多果的联系、潜变量的关系,还可以处理多水平数据和纵向数据,是非常重要的多元数据分析工具。本课程系统地介绍结构方程模型和LISREL软件的应用,内容包括:结构方程分析(包括验证性因子分析)的基本概念、统计原理、在社会科学研究中的应用、常用模型及其LISREL程序、结果的解释和模型评价。学员应具备基本的统计知识(如:标准差、t-检验、相关系数),理解回归分析和因子分析的概念。 注:本课程配套教材为《结构方程模型及其应用》(以LISREL软件为例)。

修课背景要求:

讲学语言:普通话及广东话 / 简体中文

这是一个艰深的高阶课程,学员应有下述的知识及训练:(i) 使用SPSS, SAS或其他类似软件包;(ii) 回归;和(iii) 因子分析(探索性因子分析)。

课程目标:

完成课程之后,学生的预期学习成果是:

1. 能够说出与传统的ANOVA和回归分析法相比,结构方程模型的优点;

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课程介绍:

在社会学、心理学、教育学、经济学、管理学、市场学等研究领域的数据分析中,结构方程建模是当前最前沿的统计方法中应用最广、研究最多的一个。它包含了方差分析、回归分析、路径分析和因子分析,弥补了传统回归分析和因子分析的不足,可以分析多因多果的联系、潜变量的关系,还可以处理多水平数据和纵向数据,是非常重要的多元数据分析工具。本课程系统地介绍结构方程模型和LISREL软件的应用,内容包括:结构方程分析(包括验证性因子分析)的基本概念、统计原理、在社会科学研究中的应用、常用模型及其LISREL程序、结果的解释和模型评价。学员应具备基本的统计知识(如:标准差、t-检验、相关系数),理解回归分析和因子分析的概念。 注:本课程配套教材为《结构方程模型及其应用》(以LISREL软件为例)。

修课背景要求:

讲学语言:普通话及广东话 / 简体中文

这是一个艰深的高阶课程,学员应有下述的知识及训练:(i) 使用SPSS, SAS或其他类似软件包;(ii) 回归;和(iii) 因子分析(探索性因子分析)。

课程目标:

完成课程之后,学生的预期学习成果是:

1. 能够说出与传统的ANOVA和回归分析法相比,结构方程模型的优点;

2. 能够在仪器上进行验证性因子分析;

3. 能够用结构方程模型分析简单的全模型;

4. 通过计算出各种拟合指数和运用其他评估标准,能够比较并选出适合的模型;

5. 能够基于相应的参数统计修改模型;

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What's inside

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)
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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

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
适合具备心理学、教育学、经济学、管理学或市场学背景的学生。
要求学员具备对标准差、t检验和相关系数的理解,以及对回归分析和因子分析的概念有所了解。
针对社会科学领域的量化研究提供实用的数据分析方法。
由教授结构方程模型方面的专家任教,讲授质量有保障。
采用中文授课,语言障碍较小。
提供多元数据分析相关理论和实务,有助于提升研究能力。

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Reviews summary

Valuable sem course for beginners

This course on Structural Equation Modeling (SEM) and its applications is generally well-received by students. Reviewers commend the clear instruction and find the course to be well-suited for beginners. While some reviewers mention that specific test content was not covered during lectures, the overall feedback is positive.
Good for those new to SEM
"clear instruction, a very good course for beginner to learn SEM."
Some test content not covered in lectures
"候教授你好,有些測驗的內容沒有教"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Structural Equation Model and its Applications | 结构方程模型及其应用 (粤语) with these activities:
Read LISREL User's Guide
Build a strong foundation in using LISREL software for structural equation modeling.
View LISREL 8 on Amazon
Show steps
  • Download and install LISREL software.
  • Review the LISREL User's Guide for an overview of the software's capabilities.
  • Explore the different menu options and dialog boxes in LISREL.
Develop a Research Question
Formulate a clear and focused research question that will guide your structural equation modeling analysis.
Browse courses on Research Methods
Show steps
  • Identify a research topic of interest.
  • Review literature related to your topic.
  • Develop a specific research question that addresses a gap in knowledge.
Learn Basic Structural Equation Modeling
Gain a solid understanding of the fundamental principles and concepts of structural equation modeling.
Show steps
  • Enroll in an online tutorial or workshop on structural equation modeling.
  • Follow along with the provided materials and complete the exercises.
  • Attend any live Q&A sessions or discussion forums.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Draft a Structural Equation Model
Develop a structural equation model that represents your research question and hypotheses.
Show steps
  • Identify the variables and relationships that will be included in your model.
  • Draw a path diagram representing your model.
  • Specify the model parameters and constraints.
Practice Model Estimation
Gain proficiency in using LISREL software to estimate and evaluate structural equation models.
Show steps
  • Load your data into LISREL.
  • Specify the model parameters and constraints.
  • Estimate the model parameters using Maximum Likelihood estimation.
  • Evaluate the model fit and make any necessary modifications.
Develop a Model Interpretation
Write a detailed report or present your structural equation model findings in a clear and concise manner.
Show steps
  • Describe the model structure and its implications.
  • Interpret the parameter estimates and their statistical significance.
  • Discuss the implications of your findings for your research question and hypotheses.
Peer Review Sessions
Gain valuable feedback on your structural equation model and its interpretation from peers.
Show steps
  • Find a peer who is also taking the course or has experience with structural equation modeling.
  • Share your model and findings with your peer.
  • Receive feedback on your model structure, interpretation, and presentation.
Attend an SEM Conference
Expand your knowledge and network with professionals in the field of structural equation modeling at an industry conference.
Show steps
  • Search for upcoming structural equation modeling conferences.
  • Register for the conference and submit an abstract of your research.
  • Attend the conference to learn from experts and present your findings.
  • Network with other attendees to exchange ideas and build connections.
Build a Library of Resources
Develop a personalized repository of helpful resources on structural equation modeling for future reference.
Show steps
  • Collect articles, books, and online materials on structural equation modeling.
  • Organize the resources into categories or folders.
  • Use a reference manager or create a bibliography for easy access.

Career center

Learners who complete Structural Equation Model and its Applications | 结构方程模型及其应用 (粤语) will develop knowledge and skills that may be useful to these careers:
Econometrician
Econometricians use statistical methods to analyze economic data. This course provides a comprehensive overview of SEM, a statistical technique that is specifically designed to analyze complex relationships among variables. SEM is a valuable tool for Econometricians because it allows them to model and test complex hypotheses, and to identify the underlying structure of data. Taking this course will give you the skills you need to become a successful Econometrician and advance your career in econometrics.
Data Engineer
Data Engineers build and maintain the infrastructure that is used to store and process data. This course provides a solid foundation in SEM, a statistical technique that is specifically designed to analyze complex relationships among variables. SEM is a valuable tool for Data Engineers because it allows them to model and test complex hypotheses, and to identify the underlying structure of data. Taking this course will give you the skills you need to become a successful Data Engineer and advance your career in data engineering.
Market Researcher
Market Researchers use data to understand consumer behavior and make informed decisions about marketing campaigns. This course provides a solid foundation in SEM, a statistical technique that is specifically designed to analyze complex relationships among variables. SEM is a valuable tool for Market Researchers because it allows them to model and test complex hypotheses, and to identify the underlying structure of data. Taking this course will give you the skills you need to become a successful Market Researcher and advance your career in market research.
Research Scientist
Research Scientists use data to solve problems and make new discoveries. This course provides a comprehensive overview of SEM, a statistical technique that is specifically designed to analyze complex relationships among variables. SEM is a valuable tool for Research Scientists because it allows them to model and test complex hypotheses, and to identify the underlying structure of data. Taking this course will give you the skills you need to become a successful Research Scientist and advance your career in research.
Quantitative Researcher
Quantitative Researchers use statistical models to analyze data and solve business problems. This course provides a solid foundation in structural equation modeling (SEM), a powerful statistical technique for modeling complex relationships among variables. SEM is widely used in market research, finance, and other fields where researchers need to understand the underlying structure of data. Taking this course will give you the skills you need to become a successful Quantitative Researcher and advance your career in data analysis.
Management Consultant
Management Consultants use data to help businesses solve problems and improve performance. This course provides a comprehensive overview of SEM, a statistical technique that is specifically designed to analyze complex relationships among variables. SEM is a valuable tool for Management Consultants because it allows them to model and test complex hypotheses, and to identify the underlying structure of data. Taking this course will give you the skills you need to become a successful Management Consultant and advance your career in management consulting.
Operations Research Analyst
Operations Research Analysts use mathematical and statistical techniques to solve complex business problems. This course provides a comprehensive overview of SEM, a statistical technique that is specifically designed to analyze complex relationships among variables. SEM is a valuable tool for Operations Research Analysts because it allows them to model and test complex hypotheses, and to identify the underlying structure of data. Taking this course will give you the skills you need to become a successful Operations Research Analyst and advance your career in operations research.
Statistician
Statisticians use data to solve problems and make informed decisions. This course provides a comprehensive overview of SEM, a statistical technique that is specifically designed to analyze complex relationships among variables. SEM is a valuable tool for Statisticians because it allows them to model and test complex hypotheses, and to identify the underlying structure of data. Taking this course will give you the skills you need to become a successful Statistician and advance your career in statistics.
Data Analyst
Data Analysts use data to solve business problems and make informed decisions. This course provides a comprehensive overview of SEM, a statistical technique that is specifically designed to analyze complex relationships among variables. SEM is a valuable tool for Data Analysts because it allows them to model and test complex hypotheses, and to identify the underlying structure of data. Taking this course will give you the skills you need to become a successful Data Analyst and advance your career in data analysis.
Financial Analyst
Financial Analysts use data to make informed decisions about investments. This course provides a comprehensive overview of SEM, a statistical technique that is specifically designed to analyze complex relationships among variables. SEM is a valuable tool for Financial Analysts because it allows them to model and test complex hypotheses, and to identify the underlying structure of data. Taking this course will give you the skills you need to become a successful Financial Analyst and advance your career in finance.
Data Scientist
Data Scientists use data to solve problems and make new discoveries. This course provides a comprehensive overview of SEM, a statistical technique that is specifically designed to analyze complex relationships among variables. SEM is a valuable tool for Data Scientists because it allows them to model and test complex hypotheses, and to identify the underlying structure of data. Taking this course will give you the skills you need to become a successful Data Scientist and advance your career in data science.
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. This course provides a solid foundation in SEM, a statistical technique that is specifically designed to analyze complex relationships among variables. SEM is a valuable tool for Machine Learning Engineers because it allows them to model and test complex hypotheses, and to identify the underlying structure of data. Taking this course will give you the skills you need to become a successful Machine Learning Engineer and advance your career in machine learning.
Software Developer
Software Developers design and build software applications. This course provides a solid foundation in SEM, a statistical technique that is specifically designed to analyze complex relationships among variables. SEM is a valuable tool for Software Developers because it allows them to model and test complex hypotheses, and to identify the underlying structure of data. Taking this course will give you the skills you need to become a successful Software Developer and advance your career in software development.
Business Analyst
Business Analysts use data to solve problems and improve performance. This course provides a comprehensive overview of SEM, a statistical technique that is specifically designed to analyze complex relationships among variables. SEM is a valuable tool for Business Analysts because it allows them to model and test complex hypotheses, and to identify the underlying structure of data. Taking this course will give you the skills you need to become a successful Business Analyst and advance your career in business analysis.
Product Manager
Product Managers are responsible for the development and launch of new products. This course provides a solid foundation in SEM, a statistical technique that is specifically designed to analyze complex relationships among variables. SEM is a valuable tool for Product Managers because it allows them to model and test complex hypotheses, and to identify the underlying structure of data. Taking this course will give you the skills you need to become a successful Product Manager and advance your career in product management.

Reading list

We've selected 34 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Structural Equation Model and its Applications | 结构方程模型及其应用 (粤语).
這本繁體中文書籍由具有多年教學和研究經驗的作者撰寫,深入淺出地講解結構方程模型的基本概念和應用實務,特別適合中文讀者學習和參考。
A comprehensive guide to SEM; provides detailed coverage of the LISREL and EQS software programs; includes a number of helpful examples and exercises.
Provides a comprehensive overview of SEM, including both the foundations and applications of SEM. It valuable resource for researchers who want to learn more about SEM.
Provides a comprehensive overview of structural equation modeling (SEM), including advanced techniques such as multilevel modeling and longitudinal analysis. It valuable resource for researchers who want to use SEM to analyze complex data.
Provides a comprehensive overview of SEM using AMOS, a popular software program for SEM. It valuable resource for researchers who want to use SEM to analyze complex data.
Provides a step-by-step guide to using Mplus, a popular software program for SEM. It useful resource for researchers who want to learn how to use Mplus to analyze their data.
For those seeking deep insights on advanced applications of SEM, this book serves as an extensive guide that covers specific methodologies like complex models, latent growth modeling, multilevel modeling and mediation analysis.
This introductory text provides a gentle start to structural equation modeling, offering a conceptual understanding of the theoretical foundations and practical guidance in applying SEM using LISREL, PRELIS, and SIMPLIS software.
統計學中結構方程模型的應用介紹,著重於模型建立、參數估計和假設檢定。
Provides a basic introduction to SEM. It good resource for researchers who are new to SEM.
Tailored specifically to educational research, this book provides practical guidance on modeling complex educational data, offering insights into measurement, causation, and intervention effectiveness.
Offers a focused exploration of longitudinal structural equation modeling, providing a thorough examination of core concepts, modeling techniques, and advanced applications.
這本書提供了結構方程式模型在研究中的實務應用,對於課程中有關結構方程式建模和分析步驟等主題,可作為補充教材。
提供多元統計分析的全面概述,包括結構方程式模型。對於希望深入了解多元統計分析的讀者來說,這是一本有用的資源。
提供社會和行為科學中結構方程式模型的概述。對於希望了解結構方程式模型在這些領域的應用的讀者來說,這是一本有用的資源。
提供使用 AMOS 軟體進行結構方程式模型的概述。對於希望了解如何使用 AMOS 進行結構方程式模型分析的讀者來說,這是一本有用的資源。
提供使用 R 進行偏最小二乘結構方程式模型 (PLS-SEM) 的概述。對於希望了解 PLS-SEM 在管理和經濟等領域應用的讀者來說,這是一本有用的資源。
Provides a comprehensive overview of multivariate statistical methods, including chapters on structural equation modeling.
Provides a comprehensive overview of multilevel structural equation modeling, a technique used to analyze data that is nested within higher-level units.
Provides a more in-depth look at structural equation modeling, including advanced topics such as Bayesian estimation and multi-level modeling.

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