Structural Equation Modelling and Causal Inference - online


08/11/2023 - 10/11/2023

Organised by:



Ozan Aksoy


Intermediate (some prior knowledge)


Short Course Coordinator

video conference logo

Venue: Online


This is an intensive 3-day online course on Structural Equation Modelling (SEM) with hands-on practical sessions.

The first day introduces/refreshes the basics of SEM, focusing on the building blocks of structural and measurement models, and structural regression models which include both a structural and a measurement component. Some attention will be paid to multiple group analysis and measurement invariance.

The second day treats SEM for longitudinal and missing data. Panel data models such as cross-lagged models and latent growth curve models will be studied and Full Information Maximum Likelihood method for handling missing data will be introduced.

The third day discusses SEM for causal inference. Fixed-effects versus random effects, cross-legged panel models with individual fixed effects, and instrumental variable designs within the SEM framework will be discussed. An informal comparison of SEM with Directed Acyclic Graphs (DAGs) will feature in the course too. Each day includes a 3-hour online/live lecture and a 3-hour hands-on supervised computer practical.

Course Contents

The course covers:

  • Basics of Structural Equation Modelling (SEM)
  • Structural (regression type) models
  • Measurement models (Confirmatory Factor Analysis, Multi-trait multi-method models)
  • Multiple group analysis and measurement invariance
  • Full Information Maximum Likelihood Estimation
  • Longitudinal SEM (cross-lagged models, latent-curve models)
  • SEM for causal inference (fixed versus random effects models, cross-lagged panel models with fixed effects, instrumental variable models).

Course structure

The course is taking place on 8th, 9th, 10th November 2023, from 10:00AM until 5:00PM, and consists of a 3-hour lecture in the morning followed by a 3-hour computer practical in the afternoon. There will be a 1-hour lunch break from 13:00 until 14:00.

The lecture introduces the statistical concepts and tools, while the computer practical involves hands-on exercises and analyses of the material treated in the lecture.

Day 1

Lecture and computer practical on

  • Basics of Structural Equation Modelling (SEM)
  • Structural (regression type) models
  • Measurement models (Confirmatory Factor Analysis, Multi-trait multi-method models)
  • Multiple group analysis and measurement invariance

Day 2

Lecture and computer practical on

  • Longitudinal SEM (cross-lagged models, latent-curve models)
  • Full Information Maximum Likelihood Estimation
  • SEM versus DAGs

Day 3

Lecture and computer practical on

  • Fixed versus random effects models
  • Cross-lagged panel models with fixed effects
  • Instrumental variable models

Learning Outcomes

By the end of the course participants will:

  •  Be able to fit Structural Equation Models to real-world cross-sectional and longitudinal data.
  •  Be able to understand and interpret the results of a wide range of SEMs.
  •  Be able to critically evaluate the assumptions and requirements for causal inference with SEM.

The Presenters/Speakers

Ozan Aksoy is Associate Professor of Social Science at the UCL Social Research Institute at University College London. He has been a member of the UCL Q-Step centre which provides social science students with state-of-the-art training in social data science, through courses in quantitative research methods, data analysis and visualization. He has more than 15 years of experience in teaching methods and statistics in the social sciences. His research interests include cooperation, trust, and religious behaviour. He uses game theory, statistical and computational methods, and laboratory and natural experiments as research tools. He is the recipient of the 2019 Raymond Boudon Award for Early Career Achievement and has been since 2022 an elected fellow of the European Academy of Sociology. His recent work has been published, among others, in American Sociological Review, American Journal of Sociology, Social Forces, Nature Human Behaviour, Sociological Science, and European Sociological Review.

Course requirements

  • Familiarity with R/Rstudio and a basic understanding of regression/ANOVA will be required.
  • The primary working packages in the lab sessions will be R, RStudio and Lavaan.
  • Wherever possible, analogous code for Stata-SEM and MPlus will be provided.

Target Audience

The main target are students and practitioners in sociology, social psychology, epidemiology, and cognate disciplines. Next to a theoretical understanding of SEM, the focus will also be on practical, hands-on applications of SEM. So, the module will be relevant for government researchers and non-academic practitioners too.

Required reading:

Rex Kline (2023) Principle and Practice of Structural Equation Modelling, 5th edition: Guilford

The lavaan tutorial (


The fee per teaching day is: • £30 per day for students • £60 per day for staff working for academic institutions, Research Councils and other recognised research institutions, registered charity organisations and the public sector • £100 per day for all other participants In the event of cancellation by the delegate a full refund of the course fee is available up to two weeks prior to the course. NO refunds are available after this date. If it is no longer possible to run a course due to circumstances beyond its control, NCRM reserves the right to cancel the course at its sole discretion at any time prior to the event. In this event every effort will be made to reschedule the course. If this is not possible or the new date is inconvenient a full refund of the course fee will be given. NCRM shall not be liable for any costs, losses or expenses that may be incurred as a result of its cancellation of a course, including but not limited to any travel or accommodation costs. The University of Southampton’s Online Store T&Cs also continue to apply.

Website and registration:




Linear regression, Instrumental variables estimation, Hierarchical models, Mixed models, Random effects, Cross-lagged panel models, Growth curve models, Confirmatory factor analysis, Structural equation models, Mplus, R, Stata

Related publications and presentations:

Linear regression
Instrumental variables estimation
Hierarchical models
Mixed models
Random effects
Cross-lagged panel models
Growth curve models
Confirmatory factor analysis
Structural equation models

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