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Structural Equation Modelling with STATA

Key Details

Date: 27th April 2020 – 1st May 2020
Time: 09:00-17:30
Venue: Computer Room A & B, Main Building. Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King's College London. View Map.


Course Aim

This 5-day course is aimed at post-graduate research students, researchers and professionals. The course is an introduction into path analysis and structural equation modelling using the STATA software.

The course features an introduction to the logic of SEM, including assumptions, model specification, identification and estimation. Models for continuous and discrete response variables and continuous and discrete latent variables will be covered.  Growth, autoregressive, MIMIC, and instrumental variable models will be included.


This workshop will assume that participants have a good knowledge of basic regression and statistical modelling (as can be gained in the BHI Introduction to Statistical Modelling course running in January) any syntax based statistical software, such as STATA (such as can be obtained from the BHI Introduction to Programming course running in October or the BHI Introduction to STATA course running in January).

Participants will need to bring their own laptop computer with STATA installed.

Learning Outcomes

Subject specific: Knowledge, Understanding and Skills

On successful completion of this course the student should be able to

  • To properly interpret a path diagram, and its parameter estimates.
  • To gain insight into the impact of measurement error on estimates of structural parameters.
  • To specify a structural equation model appropriate to an example study data setting.
  • To specify simple structural equation model using graphical and script-based interfaces.
  • To implement a structural equation model in Stata and be able to interpret the output.
  • To be able to access and interpret postestimation statistics, tests and functions of parameters.
  • To achieve the above for continuous and discrete response and latent variables.
  • To make use of face-to-face learning to apply SEM to a typical example dataset to address a particular scientific problem.

General: Knowledge, Understanding and Skills

On successful completion of this module the student should be able to

  • Identify key features of data, client goals and objectives, and operationalize into an appropriate model Develop and rehearse ability
  • To communicate analysis findings clearly and accurately
  • Interact in a group.

Cost and Booking

  • External Early bird: £855 (till 27/02/20, price thereafter £950)
  • KCL Staff Early bird: £641.25 (till 27/02/20, price thereafter £712.5)
  • KCL Student Early bird: £427.5 (till 27/02/20, price thereafter £475)
  • Other student Early bird: £641.25 (till 27/02/20, price thereafter £712.5)

That is, 50% discount to King's College London PhD students, 25% discount to other students and staff at King's College London and King's Health Partners.


Booking for this course is open.

For more information and details on how to register, please visit:

A 10% early bird discount will be available until 27th February 2020 and booking will close on 20th April 2020

Course Team

Professor Andrew Pickles (Academic Lead)

AndrewPicklesFrom an early background in natural sciences, urban planning and geography Andrew joined the MRC Child Psychiatry Unit as statistician in 1986, moved to the Dept of Biostatistics in Manchester in 1998, returning to the IoPPN in 2010.  

His principal interests have been in multivariate longitudinal data, with most applications being in what is now called life-course analysis. These studies have involved genomic, physiological, psychological and social measures and processes and have spanned neonates to geriatrics. A good part of this work has focussed on autism, with recent work highlighting intervention studies and longer term outcomes. 

Equally rewarding have been the impact on statistical modelling and software (e.g. structural equation modelling in Stata) and the clinical impact on patient care (e.g. NICE guidelines).

Research profile

See Andrew's research profile here.

Professor Richard Emsley (Academic Lead)

RichardEmsleyI joined the Institute of Psychiatry, Psychology and Neuroscience at King's College London as Professor of Medical Statistics and Trials Methodology in January 2018.

Prior to this, I held a personal Chair at The University of Manchester as Professor of Medical Statistics and was Deputy Director of the Manchester Clinical Trials Unit.

Research interests

My research interests are in clinical trials methodology, and developing statistical methods for efficacy and mechanisms evaluation using causal inference approaches. The applications of these methods include randomised trials of complex interventions in mental health, and trial designs and associated analysis methods in precision medicine.

My research programme aims to answer four key questions about treatments when analysing clinical trials:

  1. Are treatments effective?
  2. How do they work?
  3. What factors make them work more effectively?
  4. Which patients are they most effective for?

I develop clinical trial designs which aim to answer questions about treatments more quickly and using fewer patients.

Expertise and public engagement

Within the Department of Biostatistics and Health Informatics, I lead the Clinical Trials Methodology research theme, and am the lead for the joint department and Maudsley BRC Methodology Patient and Public Involvement group.

Externally, I am an investigator on the MRC-NIHR Trials Methodology Research Partnership, and co-lead of its Statistical Analysis Working Group. I am the founder of the European Causal Inference Meeting (Euro-CIM). I sit on the NIHR Health Technology Assessment Researcher-led Committee.

Research profile

See Richard's research profile here.


Your place will not be confirmed until payment has been made. Failure to cancel without sufficient notice will forfeit your course fee and access to future courses. If you would like to pay by internal transfer, please contact

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