Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Fall Semester
Year 2025 352470 Statistics Analysis of Multi-Variance 1 2.0 2 ★ Sheng-Lun Cheng GTVE 6 0 　

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2025	352470	Statistics Analysis of Multi-Variance	1	2.0	2	★	Sheng-Lun Cheng	GTVE	6	0

Course Plan：

Instructor Sheng-Lun Cheng　Office Hours

Email scheng@ntut.edu.tw

Last Updated 2025-06-02 21:58:09

Course Objective This course offers an in-depth introduction to Partial Least Squares Structural Equation Modeling (PLS-SEM), a powerful and flexible technique for analyzing complex relationships among observed and latent variables in applied research. The course is grounded in the use of the SEMinR package in R, providing students with a hands-on, code-based learning experience that emphasizes practical application over mathematical derivation. Throughout the semester, students will develop a strong conceptual and analytical foundation in PLS-SEM, beginning with the distinction between reflective and formative measurement models, and advancing through the evaluation of structural models and the interpretation of model outputs. Particular emphasis will be placed on the following topics: model specification, measurement validation, structural path estimation, bootstrapping for significance testing, predictive relevance assessment, and advanced modeling techniques such as mediation, moderation, and higher-order constructs.

Course Schedule Week 1: Introduction to PLS-SEM and Review Hayes's Conditional Process Analysis Week 2: Getting Started with R, RStudio, and SEMinR Week 3: Building Basic Path Models in SEMinR Week 4: Reflective Measurement Models: Specification Week 5: Reflective Measurement Models: Assessment Week 6: Formative Measurement Models: Specification Week 7: Formative Measurement Models: Assessment Week 8: Structural Model Evaluation Week 9: Midterm Week 10: Bootstrapping and Significance Testing Week 11: Predictive Power and PLSpredict Week 12: Mediation Analysis Week 13: Moderation Analysis Week 14: Moderated Mediation (Conditional Process Modeling) Week 15: Higher-Order Constructs Week 16: Multi-Group Analysis and Measurement Invariance Week 17: Reporting and Visualizing PLS-SEM Results Week 18: Final

Evaluation and grading policy Attendance 20% Final presentation and report 80%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Course materials will be prepared by the instructor. Optional: Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Springer. https://doi.org/10.1007/978-3-030-80519-7

The access to curricular consultation The best way to reach out to me is through my email: scheng@ntut.edu.tw. I am happy to speak with you during my office hours.

The course corresponds to the SDGs SDG1：消除貧窮（No Poverty）
SDG4：優質教育（Quality Education）
SDG10：減少不平等（Reduced Inequalities）

Does the course incorporate AI AI1：使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process）
AI2：鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）

Note
Google Meet will be used in case in-person classes are suspended due to the pandemic. The instructor will send an invitation link to the whole class in advance.

Instructor	Sheng-Lun Cheng　Office Hours
Email	scheng@ntut.edu.tw
Last Updated	2025-06-02 21:58:09
Course Objective	This course offers an in-depth introduction to Partial Least Squares Structural Equation Modeling (PLS-SEM), a powerful and flexible technique for analyzing complex relationships among observed and latent variables in applied research. The course is grounded in the use of the SEMinR package in R, providing students with a hands-on, code-based learning experience that emphasizes practical application over mathematical derivation. Throughout the semester, students will develop a strong conceptual and analytical foundation in PLS-SEM, beginning with the distinction between reflective and formative measurement models, and advancing through the evaluation of structural models and the interpretation of model outputs. Particular emphasis will be placed on the following topics: model specification, measurement validation, structural path estimation, bootstrapping for significance testing, predictive relevance assessment, and advanced modeling techniques such as mediation, moderation, and higher-order constructs.
Course Schedule	Week 1: Introduction to PLS-SEM and Review Hayes's Conditional Process Analysis Week 2: Getting Started with R, RStudio, and SEMinR Week 3: Building Basic Path Models in SEMinR Week 4: Reflective Measurement Models: Specification Week 5: Reflective Measurement Models: Assessment Week 6: Formative Measurement Models: Specification Week 7: Formative Measurement Models: Assessment Week 8: Structural Model Evaluation Week 9: Midterm Week 10: Bootstrapping and Significance Testing Week 11: Predictive Power and PLSpredict Week 12: Mediation Analysis Week 13: Moderation Analysis Week 14: Moderated Mediation (Conditional Process Modeling) Week 15: Higher-Order Constructs Week 16: Multi-Group Analysis and Measurement Invariance Week 17: Reporting and Visualizing PLS-SEM Results Week 18: Final
Evaluation and grading policy	Attendance 20% Final presentation and report 80%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Course materials will be prepared by the instructor. Optional: Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Springer. https://doi.org/10.1007/978-3-030-80519-7
The access to curricular consultation	The best way to reach out to me is through my email: scheng@ntut.edu.tw. I am happy to speak with you during my office hours.
The course corresponds to the SDGs	SDG1：消除貧窮（No Poverty） SDG4：優質教育（Quality Education） SDG10：減少不平等（Reduced Inequalities）
Does the course incorporate AI	AI1：使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process） AI2：鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
Note	Google Meet will be used in case in-person classes are suspended due to the pandemic. The instructor will send an invitation link to the whole class in advance.