Data Science / Statistics / R Foundation / Statistics / Structural Equation Modelling

Statistics - Structural Equation Modelling


ID 2858614
Classroom 2 days
Webinar 4 days
Method Lecture with examples and exercises.
Prequisite General knowledge of math
Audience Data Analysts


  • Lunch / Catering
  • Assistance for hotel / travel bookings
  • Comelio certificate
  • Flexible: Free cancellation up until 10 days before the training


Structural equation modelling (SEM) is a statistical technique for testing and estimating causal relations using a combination of statistical data and qualitative causal assumptions. Structural equation models (SEM) allow both confirmatory and exploratory modeling, meaning they are suited to both theory testing and theory development. Confirmatory modeling usually starts out with a hypothesis that gets represented in a causal model. The concepts used in the model must then be operationalized to allow testing of the relationships between the concepts in the model. The model is tested against the obtained measurement data to determine how well the model fits the data. Among the strengths of SEM is the ability to construct latent variables: variables which are not measured directly, but are estimated in the model from several measured variables each of which is predicted to 'tap into' the latent variables. This allows the modeler to explicitly capture the unreliability of measurement in the model, which in theory allows the structural relations between latent variables to be accurately estimated. Factor analysis, path analysis and regression all represent special cases of SEM.

Training Dates

  • 2020-Nov-02 - Nov-05
  • 2021-Jan-11 - Jan-14
  • 2021-Mar-22 - Mar-25
  • 2021-May-31 - Jun-03

590 EUR +VAT

Location | Enrollment


Introduction to Structural Equation Modeling

Equivalent models - Steps in performing SEM analysis: Model specification, Estimation of free parameters, Assessment of model and model fit, Model modification, Sample size and power, Interpretation and communication - Advanced uses - SEM-specific software

Path Analysis

Causality - Latent variable model - Path modeling - Path coefficient - Path tracing rules

Causal Analysis using AMOS

Analysis of SEM with latent variables (causal analysis) - General modeling and verification process - Construct operationalization - Confirmatory factor analysis for testing reflective measurement models of latent variables (hypothetical constructs) - Testing of hypothesis using the analysis of covariance

Variants and Extensions

Characteristics of formative measurement models - MIMIC models - Second-order factor analysis (SFA) - multi-group causal analysis and the comparative analysis of causal models in several groups (samples) - Differences between the LISREL approach and the PLS approach - Universal structure modeling


Marco Skulschus (born in Germany in 1978) studied economics in Wuppertal (Germany) and Paris (France) and wrote his master´s thesis about semantic data modeling. He started working as a lecturer and consultant in 2002.

  • Grundlagen empirische Sozialforschung ISBN 978-3-939701-23-1
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  • Oracle PL/SQL ISBN 978-3-939701-40-8
  • MS SQL Server - T-SQL Programmierung und Abfragen ISBN 978-3-939701-69-9

- He works as an IT-consultant and project manager. He developed various Business Intelligence systems for industry clients and the public sector. For several years now, he is responsible for a BI-team in India which is mainly involved in BI and OLAP projects, reporting systems as well as statistical analysis and Data Mining.


He led several research projects and was leading scientist and project manager of a publicly funded project about interactive questionnaires and online surveys.