Introduction to SEM
Course Information
Acknowledgement
Instructors
Course overview
Schedule
Learning goals
Background knowledge
Resources
Literature
Reading questions
Software
Logistics
Weekly preparation
Evaluation
Attendance
Using GenAI
AI index
Acceptable usage in this course
Including AI-generated content in your work
Referencing and academic integrity
Assignments
Assignment 1: Path Analysis
Required Components
Evaluation
Submission
Assignment 2: Structural Equation Modeling
Required Components
Evaluation
Submission
Elaboration & Tips
Progression
Theoretical Model & Research Question
Assumptions
Reporting Standards
Data
Coping with Covid
Feminist Perspectives Scale
Hypersensitive Narcissism Scale & Dirty Dozen
Kentucky Inventory of Mindfulness Skills
Depression Anxiety Stress Scale
Nomophobia
Recycled Water Acceptance
Procedures
Formatting
Length
Submission
Grading
Group Assignments
Individual Assignment
Resits
Example Assignment
Rules
Resources
Collaboration
Academic integrity
GenAI disclosure statement
Strict stuff
Software Setup
Typographic Conventions
Installing software
Installing R
Installing RStudio
Installing packages
Course Data
Note on Data Updates
1
Introduction to R
1.1
Lecture
1.1.1
Recordings
1.1.2
Slides
1.2
Reading
1.3
At-Home Exercises
1.4
In-Class Exercises
Recap
2
Statistical Modeling & Path Analysis
2.1
Lecture
2.1.1
Recordings
2.1.2
Slides
2.1.3
Materials from the lecture meeting
2.2
Reading
Reference
Questions
Answers
Suggested Reading (Optional)
2.3
At-Home Exercises
2.3.1
Setup
2.3.2
2.3.3
2.3.4
2.3.5
2.3.6
2.3.7
2.3.8
2.4
In-Class Exercises
2.4.1
Setup
2.4.2
Data Exploration
2.4.3
Linear Modeling
2.4.4
Categorical IVs
3
Mediation & Moderation
3.1
Lecture
3.1.1
Recordings
3.1.2
Slides
3.2
Reading
Reference
Questions
Answers
Reference
Questions
Answers
3.3
At-Home Exercises
3.3.1
Setup
3.3.2
Mediation
3.3.3
Moderation
3.4
In-Class Exercises
3.4.1
Setup
3.4.2
Mediation
3.4.3
Moderation
4
CFA I
4.1
Lecture
4.1.1
Recordings
4.1.2
Slides
4.2
Reading
Reference
Questions
Answers
4.3
At-Home Exercises
4.3.1
Setup
4.3.2
4.3.3
4.3.4
4.3.5
4.3.6
4.3.7
4.3.8
4.3.9
4.3.10
4.3.11
4.3.12
4.3.13
4.3.14
4.3.15
4.3.16
4.3.17
4.3.18
4.4
In-Class Exercises
4.4.1
4.4.2
4.4.3
4.4.4
4.4.5
4.4.6
4.4.7
4.4.8
4.4.9
4.4.10
4.4.11
4.4.12
5
CFA II
5.1
Lecture
5.1.1
Recordings
5.1.2
Slides
5.2
Reading
References
5.3
At-Home Exercises
5.3.1
5.3.2
5.3.3
5.3.4
5.3.5
5.3.6
5.4
In-Class Exercises
5.4.1
Basic CFA
5.4.2
Post-Hoc Model Modifications
5.4.3
Model Comparisons
5.5
Extra Resources
6
Fall Break
7
Full SEM
7.1
Lecture
7.1.1
Recording
7.1.2
Slides
7.2
Reading
Reference
Questions
7.3
At-Home Exercises
7.3.1
7.3.2
7.3.3
7.3.4
7.3.5
7.3.6
7.3.7
7.3.8
7.4
In-Class Exercises
7.4.1
7.4.2
7.4.3
7.4.4
7.4.5
7.4.6
7.4.7
7.4.8
7.4.9
8
Multiple Group Models
8.1
Lecture
8.1.1
Recordings
8.1.2
Slides
8.2
Reading
8.3
At-Home Exercises
8.3.1
Multiple-Group Path Analysis
8.3.2
Multiple-Group CFA
8.4
In-Class Exercises
8.4.1
Setup
8.4.2
Measurement Invariance
8.4.3
Testing Between-Group Differences
8.4.4
Multiple-Group SEM for Moderation
Introduction to Structural Equation Modeling
Instructors
Coordinator:
dr. Kyle M. Lang
Lectures:
dr. Kyle M. Lang
Practicals:
dr. Beth Grandfield
dr. Noémi Schuurman