Structural Equation Modelling and Causal Inference - online (fully booked)

Date:

08/11/2023 - 10/11/2023

Organised by:

UCL

Presenter:

Ozan Aksoy

Level:

Intermediate (some prior knowledge)

Contact:

Short Course Coordinator
ioe.shortcourses@ucl.ac.uk

video conference logo

Venue: Online

Description:

This is an intensive 3-day 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.

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)

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.

Pre-requisites:

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

Course availability:

Please note that the course is fully booked. If you would like to be added on the waiting list or attend a future date, please email ioe.shortcourses@ucl.ac.uk .

 

Cost:

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.

Region:

International

Keywords:

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 from our eprints archive:

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

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