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A 2 day course: 19th - 20th March 2018.

This module will introduce participants to latent variables (variables which are not directly measured themselves) and to the use of factor analysis in investigating relationships between latent variables and observed, or measured, variables.  These techniques will then be extended into the wider area of structural equation modelling, where complex models involving several latent variables will be introduced.


The module is aimed at researchers and research students who have experience of statistical modelling (up to linear regression) and hypothesis testing, who wish to develop techniques to analyse more complex data involving latent variables.  The aim of the module is to provide a background of theory with opportunities to apply the techniques in practice, and each session will consist of a lecture/ demonstration and a practical.  The software packages used will be IBM SPSS and AMOS, no prior knowledge of the structural equation modelling package AMOS will be assumed.

Important please note:

If the course is fully booked please do complete the registration, as then you will be placed on a waiting list. You will be allocated a place if more places become available or if people cancel.

We will make every attempt to accommodate Lancaster University staff and postgraduate research students on our courses. However, if a course becomes fully booked we reserve the right to give priority to students on the MSc in Statistics, MSc in Data Science, and external participants.

Details of course fees.

Cancellation Policy

Registrations are transferable to another course or individual at any time. Full refunds will be given for cancellation 10 or more working days before the course start date. Otherwise the full course fee will be charged.

Accommodation Details 

Monday, March 19, 2018
All Day Event
Postgraduate Statistics Centre
Dr Andrew Titman
Taught Session - For Externals, Staff & Students
Structural Equation Modelling

Topics covered will include:

  • introduction to latent variables and measurement error
  • exploratory and confirmatory factor analysis; measurement models
  • structural equation models
  • theoretical issues involved in the development and application of structural equation models.

Learning: Students will learn through the application of concepts and techniques covered in the module to real data sets. Students will be encouraged to examine issues of substantive interest in these studies.

On successful completion students will be able to:

  • investigate data using factor analysis
  • build and verify appropriate measurement models for latent constructs
  • confirm hypotheses and develop structural equation models
  • apply theoretical concepts
  • identify and solve problems
  • analyse data using appropriate techniques
  • interpret statistical output


  • Byrne, B.M. (2010) Structural Equation Modelling with AMOS: Basic Concepts, Applications and Programming. New York: Routledge
  • Kline, R. B. (2010) Principles and Practices of Structural Equation Modelling London: The Guildford Press.