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Regular version of the site
Master 2019/2020

Analysis of Covariance Models

Category 'Best Course for Career Development'
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
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
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 * Latent Variable Model + 0.4 * Moderating Effects with Latent Variables + 0.2 * Special Topic Presentation
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