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Scientific Coordination

Dr.
André Ernst
Tel: +49 221 4703736

Administrative Coordination

Claudia O'Donovan-Bellante
Tel: +49 621 1246-221

Introduction to Structural Equation Modeling for Cross Sectional Data

About
Location:
Mannheim, B6 4-5
 
General Topics:
Course Level:
Format:
Software used:
Duration:
Language:
Fees:
Students: 300 €
Academics: 450 €
Commercial: 900 €
 
Keywords
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Lecturer(s): Jochen Mayerl, Henrik Kenneth Andersen

About the lecturer - Jochen Mayerl

About the lecturer - Henrik Kenneth Andersen

Course description

Social science research is often faced with the problem that social phenomena (e.g., authoritarianism, anti-foreigner attitudes) are not directly observable. Such latent constructs must therefore be operationalized by means of measurement models. Structural equation modeling (SEM) is a procedure that can be used to empirically validate measurement models and to test causal relationships between latent variables.
 
The workshop introduces the logic of structural equation modeling and the basics of its application to empirical analyses. Participants will learn to use SEM working with the lavaan package for R on examples of the ALLBUS data.
 
Topics include:
 
- specification and estimation procedures
- confirmatory factor analysis
- path analysis
- moderator and mediator analysis
- multiple group analysis and testing for measurement equivalence
- methodological pitfalls


Target group

Participants will find the course useful if:
  • they are interested in learning to incorporate latent variables into their empirical analyses


  • Learning objectives

    By the end of the course participants will:
  • be able to carry out analyses in the SEM framework for cross-sectional data
  • understand the uses of and strategies for working with latent variables
  •  
    Organisational structure of the course
    The workshop is structured roughly around two sessions per day. Each session covers a different topic (or set of related topics) and each is separated into an input part (presentation) and an applied part (lab).  
    Prof. Dr. Mayerl will introduce and discuss a methodological topic in the presentation part of the session. This part of the session has the character of an informal lecture and participants are encouraged to take part actively by asking questions and critically discussing the content.
    Afterwards, Dr. Andersen will lead the course in the lab section and demonstrate the application of the core concepts using the lavaan package for R. Once the basic application has been demonstrated, participants will work independently or in small groups on lab exercises. Prof. Dr. Mayerl and Dr. Andersen will support participants in completing the exercises and walk them through the solutions afterwards (if necessary).


    Prerequisites

  • Good grasp of multiple linear regression
  • Basic understanding of principal component analysis and related exploratory techniques
  • Familiarity with moderation and mediation models
  • Basic experience with the statistical software R
  •  
     
    Software and hardware requirements
    - Participants require current versions of R (at least 4.2.2) and RStudio (at least 2022.12.0 build 353).
    - R packages that should be installed on participants' laptops are:
    -- haven
    -- lavaan
    -- semPlot
    -- semTools


    Schedule

    Recommended readings