Магистратура
2025/2026
Моделирование структурными уравнениями
Статус:
Курс обязательный (Аналитика данных и прикладная статистика / Data Analytics and Social Statistics)
Когда читается:
2-й курс, 1, 2 модуль
Онлайн-часы:
40
Охват аудитории:
для своего кампуса
Преподаватели:
Павлова Ирина Анатольевна
Язык:
английский
Course Syllabus
Abstract
This course is designed for students in applied statistics and data analysis who would like to acquire a significant familiarity with the statistical techniques known collectively as "structural equation modeling," "causal modeling," or "analysis of covariance structures."
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
- 2025/2026 2nd module0.33 * SEM Project 1 + 0.33 * SEM Project 2 + 0.34 * SEM Project 3
Bibliography
Recommended Core Bibliography
- 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
Recommended Additional Bibliography
- 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