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Regular version of the site
Master 2020/2021

Analysis of Covariance Models

Area of studies: Applied Mathematics and Informatics
When: 2 year, 1, 2 module
Mode of studies: offline
Open to: students of all HSE University campuses
Instructors: Valentina Kuskova
Master’s programme: Applied Statistics with Network Analysis
Language: English
ECTS credits: 4

Course Syllabus

Abstract

This course is designed for MASNA students 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

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

Expected Learning Outcomes

  • Know the basic idea of implied matrices and what is happening in SEM.
  • Know the major structural equation modeling programs.
  • Know how to translate conceptual thinking into models that can be estimated.
  • 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: Path models with fixed, non-zero error terms
  • 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: Latent variable multi-equation models.
  • Be able to use the major SEM programs to estimate common types of models: Formative indicator models.
  • Be able to use the major SEM programs to estimate common types of models: Second-order factor 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: Models with latent variable interactions.
  • Be able to use the major SEM programs to estimate common types of models: Latent growth curve models, latent state-trait-occasion models, etc.
  • Have an understanding common problems related to model specification, identification, and estimation.
  • Have a working knowledge of the different ways to analyze models with covariance structures.
  • Be able to use the major SEM programs to estimate common types of models: Multi-level models (If time permits).
Course Contents

Course Contents

  • Course Introduction
    a. Course Requirements b. A Model of the Research Process
  • Problem Selection and Conceptualization
    a. Choosing a Worthwhile Topic b. Defining Constructs c. Generating Hypotheses
  • Fundamentals of LVSEM (Part 1)
  • Basic Model
    a. Path Diagrams b. Rules for Determining Model Parameters c. Model Implied Covariance Structure
  • Fundamentals of LVSEM (Part 2)
    a. Parameter Estimation b. Identification
  • Fundamentals of LVSEM (Part 3)
    a. Model Testing and Evaluation b. Two-Step Approach for Testing Models
  • Software Programs
    LISREL 8.8, Amos 6, Mplus 4.21, EQS 6.1
  • Observed Variable Models – Path Analysis
    a. What is path analysis? b. Example model c. Modeling Measurement Error in Path Analysis Models
  • Testing Mediation
    a. Direct and Indirect Effects b. Testing Indirect Effects
  • Effect Decomposition
    a. Latent Variable Structural Equation Models b. What is confirmatory factor analysis? c. What is a structural regression model? d. The Consequences of Measurement Error e. Controlling for Method Biases and “Third Variables”
  • Measurement Model Specification
    a. Types of Measurement Relations b. Specification of Second-Order Measurement Relationships c. Item Parceling
  • Assessing Construct Validity and Reliability
    a. Validity b. Reliability c. Scaling Procedures
  • Multiple Groups Analysis
    a. Multiple Group Analyses b. Analysis of Mean Structures c. Imposing Constraints Within and Between Groups d. Cross-Validation of Measurement and/or Structural Relationships e. Examples
  • Latent Variable Interactions
    a. Why use this? b. Model Specification
  • Latent Change Analysis
    a. What is latent change analysis? b. Simple One Factor LCA Model c. Level and Shape Model d. Studying Correlates and Predictors of Latent Change
  • Special Topics
Assessment Elements

Assessment Elements

  • non-blocking Answers to Readings Questions
  • non-blocking Basics Exam
  • non-blocking Path Analysis and Mediating Effects
  • non-blocking Latent Variable Model
  • non-blocking Moderating Effects with Latent Variables
  • non-blocking Special Topic Presentation
Interim Assessment

Interim Assessment

  • Interim assessment (1 module)
    0.1 * Answers to Readings Questions + 0.6 * Basics Exam + 0.3 * Path Analysis and Mediating Effects
  • Interim assessment (2 module)
    0.4 * Answers to Readings Questions + 0.4 * Basics Exam + 0.2 * Path Analysis and Mediating Effects
Bibliography

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
  • Byrne, B. M. (2000). Structural Equation Modeling With AMOS : 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=54805