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Regular version of the site
Master 2021/2022

Analysis of Covariance Models

Category 'Best Course for New Knowledge and Skills'
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: 6

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

• 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

• 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 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

• Basics Exam
• Path Analysis and Mediating Effects
• Latent Variable Model
• Moderating Effects with Latent Variables
• Special Topic Presentation Interim Assessment

• Interim assessment (1 module)
• Interim assessment (2 module)
0.25 * Latent Variable Model + 0.25 * Moderating Effects with Latent Variables + 0.25 * Path Analysis and Mediating Effects + 0.25 * Special Topic Presentation 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