Advanced Structural Equation Modelling and Generalized Latent Variable Modelling with Mplus

Advanced Structural Equation Modelling and Generalized Latent Variable Modelling with Mplus

To book a place in the course, please use our booking form. You can review details on fees here. The course fee is for a single course, and includes 28 hours of face-to-face teaching over 5 days, and lunch on 4 days. Once your booking has been processed please visit the e-store to pay by credit or debit card.


Generalized Latent Variables Modelling (GLVM) extends Structural Equation Modelling (SEM) by seamlessly integrating models for continuous and discrete observed variables as well as continuous and discrete latent variables.

Before the emergence of GLVM, latent variable models were quite restrictive of the types of data that could be used to fit them and the assumptions about the latent variable distributions that had to be made in order to estimate the models. Such restrictions placed limitations on the substantive theories that could be evaluated with these models, Procrustean constraints that forced theory to accommodate the model rather than the other way round.

With GLVM modellers are freed from many of the somewhat arbitrary distinctions between earlier generations of Latent Variable models. For instance, Factor Analysis and Latent Class Analysis assume continuous and discrete latent variable distributions respectively, and are often still viewed as polar opposites suitable for very different applications. With GLVM aspects of these two models can be blended, producing hybrid models potentially far more suitable for many applications.

For example consider modelling clinical depression (a latent variable) in a general population. A large majority the population will show no symptoms of clinical depression at all, forming a discrete class of the ‘non-depressed’. A smaller group will show symptoms of depression to varying degrees, forming a continuous dimension of variation in severity of depression. This hybrid distribution, a mixture of latent classes and a latent dimensional ‘factor’, can be modelled effortlessly using a GLVM framework.

This course introduces GLVM using the Mplus statistical package

The course will:

  1. Introduce a suite of latent variable models that make different assumptions about the distributions of the observed and latent variables: Confirmatory Factor Analysis, Item Response Theory, Latent Profile Analysis and Latent Class Analysis.
  2. Show how to estimate these models using the Mplus statistical package.
  3. Show how to interpret the output of these models.
  4. Show how these models can be used to test substantive research questions.

Course Requirements

This course assumes that students are experienced users of linear and logit/probit regression models.

Familiarity with latent variable models (e.g. Factor Analysis, Latent Class Analysis) would be an advantage but the course does not assume it.

No prior experience with Mplus is assumed.

Experience of using text-based command files, such as SPSS Syntax files or Stata Do files, would be an advantage as Mplus uses similar text-based command files.

Preliminary Reading

Bartholomew, Knott & Moustaki (2011). Latent Variable Models and Factor Analysis: A Unified Approach(3rd Ed.). London: Wiley.

Byrne, B. M. (2012). Structural Equation Modeling with Mplus. Basic Concepts, Application, Programming. New York, NY: Routledge.

Muthen, B. O. (2002). Beyond SEM: General latent variable modellingBehaviormetrika, 29, 81-117.

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