NCHU Course Outline

Course Name	(中) 結構方程模式(8161)
Course Name	(Eng.) Structural Equation Models
Offering Dept	Graduate Institute of Technology Management
Course Type	Elective	Credits	3	Teacher	SUM, PUI-FAIJOHN
Department	Graduate Institute of Technology ManagementPh.D	Language	English	Semester	2025-SPRING
Course Description	Various statistical models being applied in management researches, like factor analysis model and regression model, could be considered as special cases structural equation model (SEM). SEM is a statistical model which consists of both observable variables and latent variables. Observable variables are the data collected. Latent variables are used for modeling the hidden factors governing the generation of the observable variables. This course will introduce the mathematical background behind the SEM. Students have need to do a lot of mathematical derivations to understand the theories behind such models. Moreover, students need to self-learn a programming language, like Matlab or Python, to implement such parametric estimation algorithms.
Prerequisites				self-directed learning in the course	N

Relevance of Course Objectives and Core Learning Outcomes(%)

Teaching and Assessment Methods for Course Objectives

Course Objectives

Competency Indicators

Ratio(%)

Teaching Methods

Assessment Methods

To give a comprehensive introduction on structural equation model, including (1) model specification, (ii) parametric estimation algorithms and (iii) validation techniques.

topic Discussion/Production

Exercises

Discussion

Lecturing

Written Presentation

Oral Presentation

Assignment

Study Outcome

Quiz

Internship

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Course Content and Homework/Schedule/Tests Schedule

Week	Course Content
Week 1	Course introduction.
Week 2	Structural equation models.
Week 3	Structural equation models.
Week 4	Compared with discrete time state-space model.
Week 5	Parametric estimation.
Week 6	Parametric estimation.
Week 7	Optimization techniques.
Week 8	Optimization techniques.
Week 9	Mid-Term examination.
Week 10	EM algorithm.
Week 11	Predictive inference.
Week 12	Predictive inference
Week 13	Model assessment.
Week 14	Model assessment.
Week 15	Advanced topics.
Week 16	Advanced topics.
Week 17	Project presentation.
Week 18	Final examination.

Evaluation

Assignment (Mathematical Proof): 20%
Assignment (Program Implementation): 20%
Seminar on Matlab, Python, or other languages: 10%
Project: 50%

Textbook & other References

John Sum, Tutorial on Structural Equation Modeling, unpublished manuscript, December 2019.

Teaching Aids & Teacher's Website

Office Hours

Tuesday 13:00-14:00

Sustainable Development Goals, SDGs(Link URL)

include experience courses：N

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Update Date, year/month/day：None	Printed Date, year/month/day：2025 / 11 / 08
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