Основы моделирования структурными уравнениями

Магистратура 2021/2022

Лучший по критерию «Полезность курса для Вашей будущей карьеры»

Лучший по критерию «Полезность курса для расширения кругозора и разностороннего развития»

Лучший по критерию «Новизна полученных знаний»

Статус: Курс обязательный (Сравнительные социальные исследования / Comparative Social Research)

Направление: 39.04.01. Социология

Кто читает: Департамент социологии

Где читается: Факультет социальных наук

Когда читается: 1-й курс, 4 модуль

Формат изучения: без онлайн-курса

Охват аудитории: для своего кампуса

Преподаватели: Пашкевич Анна Валерьевна

Прогр. обучения: Сравнительные социальные исследования

Язык: английский

Кредиты: 4

Контактные часы: 32

Full 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), measurement invariance, path models, and structural equation models. In addition, practical issues of estimation (including Bayesian 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 anal-ysis. In addition, for practical exercises we will use R programming environment, so an-other major prerequisite is a basic knowledge of R. The course is held in the format of master classes.

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 (3) 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
Conduct multigroup confirmatory factor analysis (MGCFA) and test for measurement in-variance using R packages lavaan and semTools
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 concept of measurement invariance and apply different approaches to measurement invariance testing
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.
Multi-Group CFA and Measurement Invariance
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 Lectures 1-4. All assignments have to be submitted by email to the course instructor by 23:59, May 22nd, 2020.
Class activity
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 meaning-ful, 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 23:59, June 18th, 2020. Notice that in the final paper you may either (1) analyze a data set provided by the in-structor or (2) analyze your own data. Re-gardless of your specific data preference, the same grading principles and criteria (see be-low) will be applied to the assessment of your final submission in both cases. The deadline for submission of your exam paper is noon of June 13th, 2020. Then all papers will be evaluated by the instructor and preliminary grades will be announced. Then those course participants who disagree with their preliminary grades will have an opportunity to defend their project papers (by publicly presenting them in class). Presentations must be given using Power-Point or LaTeX software.
Home assignment 2
Take-home written assignment, in which you should analyze a real data set using FA/SEM methods discussed in Lectures 5-7. All assignments have to be submitted by email to the course instructor by 15:10, June 9th, 2020.
Home assignment 1
Take-home written assignment, in which you should analyze a real data set using FA/SEM methods discussed in Lectures 1-4. All assignments have to be submitted by email to the course instructor by 23:59, May 22nd, 2020.
Class activity
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 meaning-ful, 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 23:59, June 18th, 2020. Notice that in the final paper you may either (1) analyze a data set provided by the in-structor or (2) analyze your own data. Re-gardless of your specific data preference, the same grading principles and criteria (see be-low) will be applied to the assessment of your final submission in both cases. The deadline for submission of your exam paper is noon of June 13th, 2020. Then all papers will be evaluated by the instructor and preliminary grades will be announced. Then those course participants who disagree with their preliminary grades will have an opportunity to defend their project papers (by publicly presenting them in class). Presentations must be given using Power-Point or LaTeX software.
Home assignment 2
Take-home written assignment, in which you should analyze a real data set using FA/SEM methods discussed in Lectures 5-7. All assignments have to be submitted by email to the course instructor by 15:10, June 9th, 2020.

Interim Assessment

2021/2022 4th module
0.25 * Home assignment 2 + 0.35 * Final exam + 0.25 * Home assignment 1 + 0.15 * Class activity

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

SO KHYONDZHUN -
SOKOLOV BORIS OLEGOVICH

Course Syllabus