結構方程模式研究

Couse objectives This course will provide students with an introduction to the theory and application of structural equation models (SEMs). Students will learn fundamental concept and skills to conduct SEMs including path analysis, confirmatory factor analysis, and general SEMs incorporating both measurement and structural components. Learning Outcomes There are five major learning outcomes of this course: 1. To understand the concepts related to SEMs and when and how to apply SEMs; 2. To be able to specify models and understand indices measuring goodness-of-fit of a model; 3. To be able to apply statistical software to analyze data; 4. To understand the power issues and missing data handling techniques for SEMs; 5. To be able to interpret the statistical results for your own studies and other studies that use SEMs.

Students are expected to have some basic knowledge on ANOVA, Multiple Regression, and Multivariate Data Analysis. Students who have not taken the required courses have to meet with me before they register for this course.

Instructions and Missed/Late Assignment • In this course, we will use R. Please bring your own laptop and have R and RStudio installed. If you find yourself in need of a computer, please reach out to me. • Late submission of assignments or lab projects will result in score deduction (please inform me ahead of time through email for late submission).

Assignments, Tests, and Grading 1. Participation/Attendance (Attendance and participation for course discussion is expected.) 2. Lab projects (20%; We will work on five lab projects during the class.) 3. Homework Assignments (25%; There will be five homework assignments throughout the semester; you are encouraged to work with others on the HW or you can consult anyone, but please acknowledge them in your assignment.) 4. Midterm exam (15%) 5. Final exam (15%) 6. Final project: Final presentation + Abstract (25%); • Use existing data to build a SEM model: You may analyze your own data or data collected by other individual (as long as that individual has not analyzed the data addressing the same research questions you are attempting to answer). • Students are encouraged to work as a team (you can either work individually or work with a partner; i.e., maximum two students per group). Students (or teams) should schedule an individual meeting with me to talk about their final project . • The final project should include the following four sections: introduction, method, results, and discussion. • Every student (or every team) will have 20 minutes to present the project. • Final Assignment: a two-page summary (not including reference, tables, and figures).

• 課程簡介
• Path model II
• Measurement models, exploratory and confirmatory factor analysis
• Putting together: models with both measurement and structural components
• More models
• Multiple group comparison, mean structure in SEM
• Mean structure in SEM II, measurement invariance
• Advanced topics

Textbooks Course slides, other readings and materials will be provided in each class. Datasets will be provided when working through problems and assignments. 1. Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling (4th Ed.). New York, NY: Guilford. [Required] 2. Kaplan, D. (2009). Structural Equation Modeling: Foundations and Extensions (2nd Ed.). Thousand Oaks, CA: Sage. [Optional] 3. Brown, T. A. (2006). Confirmatory Factor Analysis for Applied Research. New York, NY: Guilford. [Optional] 4. Bollen, K. A. (1989). Structural equations with latent variables. New York, NY: Wiley. [Optional]

地點

HA324

時間

Mondays 10:00am-12:00pm (by appointment)

聯絡方式

Email: mtlo@nycu.edu.tw Office: 03-5712121*58063 *Please email me for a prompt response. When you email, please include your name and course in the title of the email.