Structural Equation Modeling

2023/2024

Учебный год

ENG

Обучение ведется на английском языке

3

Кредиты

Кто читает:

Департамент социологии

Статус:

Курс по выбору

Когда читается:

2-й курс, 1 модуль

Преподаватель

Соколов Борис Олегович

Course Syllabus

Abstract

The course is intended to give an introduction to the foundational concepts and basic computational techniques of structural equation modeling (SEM) and their implementation in a popular SEM software tool, R package lavaan. The topics covered by the course are exploratory and confirmatory factor analysis (E/CFA), path models, and structural equation models. In addition, practical issues of estimation, visualization and presentation of various types of SEM models are discussed. To succeed in this course, students are assumed to have basic knowledge of statistics and be familiar with several conventional statistical methods, most importantly regression analysis. In addition, for practical exercises we will use R programming environment, so another major prerequisite is a basic knowledge of R.

Learning Objectives

• The main goals of this course are (a) to help students learn the foundational concepts of structural equation modelling, (b) to explain them the key principles of model building, assessment, comparison, and modification in SEM, and (c) to illustrate how they can use a powerful statistical software, R, to utilize these concepts and principles in real-data applications of SEM.

Expected Learning Outcomes

• Apply different approaches to theory testing in SEM
• Build, estimate, assess, compare and modify confirmatory factor an/or structural models using R packages lavaan and semTools
• Conduct mediation analysis in lavaan
• Understand and apply in practice basic principles of model building, model evaluation and model modification in CFA and SEM
• Understand basic assumptions of CFA and SEM models
• Understand foundational concepts of confirmatory factor analysis (CFA) and structural equation modeling (SEM)
• Understand the concepts of moderation and mediation in SEM
• Visualize various types of measurement and structural models using R package semPlot

Course Contents

• Introduction
• Confirmatory Factor Analysis – 1: Basics of CFA
• Confirmatory Factor Analysis – 2: Model Correction and Validity Assessment.
• Confirmatory Factor Analysis – 3: Non-normal and categorical data.
• Structural Models
• Mediation analysis

Assessment Elements

• Home assignment 1
  Take-home written assignment, in which you should analyze a real data set using FA/SEM methods discussed in Topics 1-4. All assignments have to be submitted by email to the course instructor by 18:10, October 13, 2023.
• Home assignment 2
  Take-home written assignment, in which you should analyze a real data set using FA/SEM methods discussed in Topics 5-6. All assignments have to be submitted by email to the course instructor by 18:10, October 20th, 2023.
• Class activity
  Active involvement in discussions, correct responses to my questions and smart questions to me, presentations of your homework, etc. Please notice that in the first place I will evaluate the quality of your participation, not frequency (although one smart comment on the final day of the course will definitely not earn you an excellent grade for this component).
• Final exam
  Take-home written assignment, in which you should analyze a real data set using FA/SEM methods. Specifically, you should first use exploratory methods to develop a meaningful, theoretically interpretable factor model. Then you apply the confirmatory approach to assess your model’s quality and modify it, if necessary. Finally, you are asked to test whether your latent variable(s) is non-trivially related to a set of external variables. All assignments have to be submitted by email to the course instructor by 18:10 of October 20th (Wednesday), 2021 (the deadline is preliminary and can be changed later). Notice that in the final paper you may either (1) analyze a data set provided by the instructor or (2) analyze your own data. Regardless of your specific data preference, the same grading principles and criteria (see above) will be applied to the assessment of your final submission in both cases.

Interim Assessment

• 2023/2024 1st module
  0.15 * Class activity + 0.35 * Final exam + 0.25 * Home assignment 1 + 0.25 * Home assignment 2

Bibliography

Recommended Core Bibliography

• Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research, Second Edition (Vol. Second edition). New York: The Guilford Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=831411
• Kline, R. B. (2016). Principles and Practice of Structural Equation Modeling, Fourth Edition (Vol. Fourth edition). New York: The Guilford Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1078917

Recommended Additional Bibliography

• Westland, J. C. (2019). Structural Equation Models : From Paths to Networks (Vol. 2nd ed). Cham, Switzerland: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2097529

Authors

• SOKOLOV BORIS OLEGOVICH