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Prof. Kit Tai Hau 侯傑泰
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）。
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What's inside
Syllabus
课程资料
第一课：简介 (參考：第一章 引言)
第二课：探索性与验证性因子分析 (參考：第一章 引言)
Read more
Syllabus
课程资料
第一课：简介 (參考：第一章 引言)
第二课：探索性与验证性因子分析 (參考：第一章 引言)
Read more
第三课：SEM原理 (參考：第二章 结构方程模型简介)
第四课：验证性因子分析 (參考：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 (參考：第三章应用示范I 二、多质多法模型)
第六课：全模型 (參考：第三章应用示范I 三、全模型)
第七课：高阶因子分析 (參考：第三章应用示范 四、高阶因子分析)
第八课：单纯形模型 (參考：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 (參考：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 (參考：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 (參考：第六章专题讨论——涉及数据的问题 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 (參考：附录III通过SPSS读取数据)
期末考
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides a solid basis for continuing education and research in social sciences, psychology, and education
Develops an intuitive understanding of advanced statistical methods in social sciences
Facilitates effective data analysis and interpretation for various research projects
Enhances understanding of causal relationships among variables and latent constructs
Offers hands-on experience with practical applications using LISREL software
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Save Structural Equation Model and its Applications | 结构方程模型及其应用 (普通话) to your list so you can find it easily later:
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Reviews summary
Solid sem intro course
This course is a great introduction to Structural Equation Modeling, especially for beginners. Professor Hou does a very good job at explaining difficult concepts and making the course easy to follow. The course is taught in Chinese, but the videos have English subtitles available. While this course is not without its flaws, such as a lack of project-based learning and outdated materials, it is still a valuable resource for learning about SEM.
The course syllabus is comprehensive and easy to follow.
"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。"
Professor Hou is an excellent instructor who presents complex concepts in a clear and concise way.
"教授的讲解浅显易懂，由浅入深，并不需要强大的数学基础就能明白课堂内容。"
"侯老师上课认真仔细，字斟句酌，重要知识点重复论述，让我这个初学者受益匪浅。"
The course is taught in Chinese, which may be a barrier for some students.
"i am unable to unenroll. can you please unenroll me. i dont understand the language"
Some of the course materials are outdated and could be improved.
"it seems the course group has not fixed the errors in the course nor answered questions in the forum for quiet a long period of time."
The course could benefit from more hands-on learning opportunities.
"I do hope this course could be more project-oriented and give us more lab assignments that involve real data."
Activities
Be better prepared
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during
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in Structural Equation Model and its Applications | 结构方程模型及其应用 (普通话) with these
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结构方程模型报告
Show steps
让你应用SEM技术解决实际问题，并展示你的分析和解释能力。
Browse courses on
LISREL
Show steps
-
收集数据并构建结构方程模型
-
使用LISREL软件分析模型
-
解释分析结果并撰写报告
Show all one activities
结构方程模型报告
Show steps
让你应用SEM技术解决实际问题，并展示你的分析和解释能力。
Browse courses on
LISREL
Show steps
Show steps
Show steps
- 收集数据并构建结构方程模型
- 使用LISREL软件分析模型
- 解释分析结果并撰写报告
Show all one activities
Career center
Learners who complete Structural Equation Model and its Applications | 结构方程模型及其应用 (普通话) will develop knowledge and skills
that may be useful to these careers:
Psychometrician
Psychometrician
A Psychometrician is a professional who develops and uses psychological tests. They use a variety of methods, including statistical analysis, data mining, and item response theory. As a Psychometrician, having a strong understanding of SEM is essential, as it is used to develop and validate psychological tests. By taking this course, you will learn how to use SEM to conduct psychometric studies. This knowledge will help you to develop and validate psychological tests.
Data Scientist
Data Scientist
A Data Scientist is a professional who uses data to solve business problems. They use a variety of techniques, including statistical modeling, machine learning, and data visualization. As a Data Scientist, having a strong understanding of structural equation modeling (SEM) is crucial. SEM is a powerful statistical technique for understanding the relationships between observed variables and latent variables, which are variables that cannot be directly observed. By taking this course, you will learn the basic concepts of SEM and how to apply it to real-world data. This knowledge will give you a competitive advantage in the job market, as data science is one of the fastest-growing fields.
Statistician
Statistician
A Statistician is a professional who uses statistics to solve problems in a variety of fields, including science, engineering, and business. They use a variety of methods, including data analysis, statistical modeling, and machine learning. As a Statistician, having a strong understanding of SEM is helpful, as it is a powerful statistical technique that can be used to identify the relationships between different variables. By taking this course, you will learn the basic concepts of SEM and how to apply it to real-world data. This knowledge will give you a competitive advantage in the job market, as statisticians are in high demand.
Market Researcher
Market Researcher
A Market Researcher is a professional who studies consumer behavior to help businesses make informed decisions. They use a variety of methods, including surveys, interviews, and focus groups. As a Market Researcher, having a strong understanding of SEM is helpful, as it can be used to identify the factors that influence consumer behavior. By taking this course, you will learn how to use SEM to design and conduct market research studies. This knowledge will help you to develop effective marketing campaigns and make better decisions about product development.
Data Analyst
Data Analyst
A Data Analyst is a professional who uses data to identify trends and patterns. They use a variety of techniques, including data mining, statistical analysis, and data visualization. As a Data Analyst, having a strong understanding of SEM is helpful, as it can be used to identify the relationships between different variables. By taking this course, you will learn how to use SEM to conduct data analysis studies. This knowledge will help you to make better decisions about how to use data to improve business outcomes.
Survey Researcher
Survey Researcher
A Survey Researcher is a professional who designs and conducts surveys. They use a variety of methods, including questionnaire design, sampling, and data analysis. As a Survey Researcher, having a strong understanding of SEM is beneficial, as it can be used to identify the factors that are contributing to a survey response. By taking this course, you will learn how to use SEM to conduct survey research studies. This knowledge will help you to design and conduct surveys.
Business Analyst
Business Analyst
A Business Analyst is a professional who helps businesses to identify and solve problems. They use a variety of techniques, including data analysis, process mapping, and stakeholder interviews. As a Business Analyst, having a strong understanding of SEM is beneficial, as it can be used to identify the factors that are contributing to a business problem. By taking this course, you will learn how to use SEM to conduct business analysis studies. This knowledge will help you to make better decisions about how to improve business processes and solve problems.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst is a professional who uses mathematics and statistics to analyze financial data. They use a variety of methods, including statistical modeling, machine learning, and data visualization. As a Quantitative Analyst, having a strong understanding of SEM is beneficial, as it can be used to identify the relationships between different financial variables. By taking this course, you will learn how to use SEM to conduct financial analysis studies. This knowledge will help you to make better decisions about how to invest your money.
Financial Analyst
Financial Analyst
A Financial Analyst is a professional who studies financial data. They use a variety of methods, including statistical analysis, data mining, and financial modeling. As a Financial Analyst, having a strong understanding of SEM is helpful, as it can be used to identify the relationships between different financial variables. By taking this course, you will learn how to use SEM to conduct financial analysis studies. This knowledge will help you to make better decisions about how to invest your money.
Epidemiologist
Epidemiologist
An Epidemiologist is a professional who studies the distribution and patterns of health-related events. They use a variety of methods, including statistical analysis, data mining, and geographic information systems. As an Epidemiologist, having a strong understanding of SEM is helpful, as it can be used to identify the factors that are contributing to health-related events. By taking this course, you will learn how to use SEM to conduct epidemiological studies. This knowledge will help you to make better decisions about how to prevent and treat health-related events.
Research Scientist
Research Scientist
A Research Scientist is a professional who conducts research in a variety of fields, including science, engineering, and medicine. They use a variety of methods, including experiments, simulations, and data analysis. As a Research Scientist, having a strong understanding of SEM is beneficial, as it can be used to identify the factors that are contributing to a research problem. By taking this course, you will learn how to use SEM to conduct research studies. This knowledge will help you to make better decisions about how to design and conduct research studies.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is a professional who develops and deploys machine learning models. They use a variety of tools and technologies, including programming languages, machine learning libraries, and cloud computing platforms. As a Machine Learning Engineer, having a strong understanding of SEM is helpful, as it can be used to identify the relationships between different features. By taking this course, you will learn how to use SEM to develop and deploy machine learning models. This knowledge will help you to make better decisions about how to use machine learning to solve business problems.
Economist
Economist
An Economist is a professional who studies the production, distribution, and consumption of goods and services. They use a variety of methods, including statistical analysis, mathematical modeling, and data visualization. As an Economist, having a strong understanding of SEM is helpful, as it can be used to identify the relationships between different economic variables. By taking this course, you will learn how to use SEM to conduct economic studies. This knowledge will help you to make better decisions about how to allocate resources and improve the economy.
Actuary
Actuary
An Actuary is a professional who uses mathematics and statistics to assess risk. They use a variety of methods, including statistical modeling, machine learning, and data visualization. As an Actuary, having a strong understanding of SEM is helpful, as it can be used to identify the relationships between different risk factors. By taking this course, you will learn how to use SEM to conduct risk assessments. This knowledge will help you to make better decisions about how to manage risk.
Data Engineer
Data Engineer
A Data Engineer is a professional who designs and builds data pipelines. They use a variety of tools and technologies, including databases, data warehouses, and data lakes. As a Data Engineer, having a strong understanding of SEM is helpful, as it can be used to identify the relationships between different data sources. By taking this course, you will learn how to use SEM to design and build data pipelines. This knowledge will help you to make better decisions about how to collect and store data.
For more career information including salaries, visit:
OpenCourser.com/course/95lpb0/structural
Reading list
We've selected 11 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 (普通话).
Comprehensive guide to structural equation modeling (SEM) for business researchers and provides a thorough overview of the theory, application, and interpretation of SEM in business research.
In this book, an applied emphasis is maintained by considering the developmental processes that each of the example models represents and by considering the implications of the results found for each example model.
A textbook introducing students to structural equation modeling with a focus on the most frequently used SEM software packages, LISREL, PRELIS, and AMOS.
Introduces Bayesian structural equation modeling (BSEM) using empirical examples in a variety of disciplines, including social sciences, business, and health.
Follows the introductory textbook by the same authors, providing a foundation for understanding the more advanced topics of structural equation modeling (SEM) that are discussed.
Serves as an advanced and comprehensive guide to the applications of structural equation modeling (SEM) and covers advanced topics including constraints, nonlinear relations, power analysis, and estimation.
This handbook covers the entire field of structural equation modeling (SEM) in a single comprehensive volume, consisting of 65 chapters authored by the world’s leading SEM experts.
Provides an introduction to structural equation modeling (SEM) and covers the basics of SEM, including the different types of models, how to fit models, and how to interpret the results.
Provides a practical guide to structural equation modeling (SEM) and covers the most common situations encountered when using SEM, along with examples that demonstrate how to resolve each issue.
Covers the basics of structural equation modeling (SEM) and provides clear and concise explanations of the key concepts involved in SEM.
Provides a non-technical introduction to structural equation modeling (SEM) and covers the basics of SEM without any complex mathematics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/95lpb0/structural
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Effort
9 hours of videos and quizzes
Via
Coursera
Institution
The Chinese University of Hong Kong
Instructor
Prof. Kit Tai Hau 侯傑泰
Language
中文
Good to know
Provides a solid basis for continuing education and research in social sciences, psychology, and education
Develops an intuitive understanding of advanced statistical methods in social sciences
Facilitates effective data analysis and interpretation for various research projects
Enhances understanding of causal relationships among variables and latent constructs
Offers hands-on experience with practical applications using LISREL software
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