2026/2027
Моделирование структурными уравнениями
Статус:
Маго-лего
Где читается:
Факультет социальных наук
Когда читается:
1, 2 модуль
Онлайн-часы:
40
Охват аудитории:
для своего кампуса
Преподаватели:
Павлова Ирина Анатольевна
Язык:
английский
Кредиты:
6
Контактные часы:
10
Course Syllabus
Abstract
This course is designed for students in applied statistics and data analysis providing a comprehensive foundation in Structural Equation Modeling (SEM), progressing from fundamental concepts to advanced applications within a unified analytical framework. Participants will develop the practical skills to specify, estimate, and interpret complex multivariate models, including confirmatory factor analysis (CFA) and full latent variable path models. The curriculum emphasizes the critical role of model fit assessment and the iterative process of model respecification, equipping students to distinguish between good and poor-fitting theoretical models. Through applied sessions using libraries in R, students will translate substantive research questions into testable hypotheses The pedagogical approach is decidedly applied, with each theoretical lecture paired with a computer-based case study to ensure immediate and practical knowledge transfer. Upon completion, participants will be confident researchers capable of critically evaluating SEM literature and applying these powerful techniques to their own data.
Learning Objectives
- To provide you with an understanding of the basic principles of latent variable structural equation modeling and lay the foundation for future learning in the area.
- To explore the advantages and disadvantages of latent variable structural equation modeling, and how it relates to other methods of analysis.
- To develop your familiarity, through hands on experience, with the major structural equation modeling programs, so that you can use them and interpret their output.
- To develop and/or foster critical reviewing skills of published empirical research using structural equation modeling.
Expected Learning Outcomes
- Be able to use the major SEM programs to estimate common types of models: Models with latent variable interactions.
- Be able to use the major SEM programs to estimate common types of models: Models with multiple mediating effects.
- Be able to use the major SEM programs to estimate common types of models: Multi-equation path analysis models
- Be able to use the major SEM programs to estimate common types of models: Multi-group models with mean structures.
- Be able to use the major SEM programs to estimate common types of models: Multi-level models (If time permits).
- Be able to use the major SEM programs to estimate common types of models: Path models with fixed, non-zero error terms
- Have a working knowledge of the different ways to analyze models with covariance structures.
- Have an understanding common problems related to model specification, identification, and estimation.
- Know how to translate conceptual thinking into models that can be estimated.
- Know the basic idea of implied matrices and what is happening in SEM.
- Know the major structural equation modeling programs.
Course Contents
- Section 1. Introduction and Structural Models (Path Analysis)
- Section 2. Measurement Model and General Model
- Section 3. Measurement
- Section 4. Measurement Invariance
- Section 5. Testing construct validity
- Section 6. MTMM approach for network measurement
Interim Assessment
- 2026/2027 2nd module0.3 * SEM Project 1 + 0.4 * SEM Project 2 + 0.3 * SEM Project 3
Bibliography
Recommended Core Bibliography
- 9781119579007 - Kamel Gana, Guillaume Broc - Structural Equation Modeling with lavaan - 2018 - Wiley - http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1995239 - nlebk - 1995239
- 9781462504466 - Rick H. Hoyle - Handbook of Structural Equation Modeling - 2014 - The Guilford Press - http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=462840 - nlebk - 462840
- Handbook of structural equation modeling, , 2012
- 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
- Netemeyer, R. G., Sharma, S., & Bearden, W. O. (2003). Scaling Procedures : Issues and Applications. Thousand Oaks, Calif: SAGE Publications, Inc. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=321358
- Raykov, T., & Marcoulides, G. A. (2006). A First Course in Structural Equation Modeling (Vol. 2nd ed). Mahwah, NJ: Routledge. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=188193
- Structural equation modeling : foundations and extensions, Kaplan, D., 2009
Recommended Additional Bibliography
- 9781315871318 - Jason T. Newsom - Longitudinal Structural Equation Modeling : A Comprehensive Introduction - 2015 - Routledge - http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1016425 - nlebk - 1016425
- Byrne, B. M. (1998). Structural Equation Modeling With Lisrel, Prelis, and Simplis : Basic Concepts, Applications, and Programming. Mahwah, N.J.: Psychology Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=582749
- Byrne, B. M. (2016). Structural Equation Modeling With AMOS : Basic Concepts, Applications, and Programming, Third Edition (Vol. Third edition). New York: Routledge. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1249128
- Discovering structural equation modeling using Stata, Acock, A. C., 2013
- Introduction to structural equation modeling using IBM SPSS statistics and EQS, Blunch, N. J., 2016
- Structural equation modeling : applications using Mplus, Wang, J., 2012