The Cathie Marsh Centre for Census and Survey Research

Structural Equation and Latent Class Modelling

Optional for SRMS

Lecturers Dr. Nick Shryane and Prof. Tarani Chandola

Timetable Term 2. Five day course. See course timetable for details

Course Aims


Students should have completed introductory/intermediate training in statistical analysis and research design, such that they are familiar with:


Teaching and Learning Methods

Each of the five course days will consist of 2 teaching/workshop blocks and an exercise. The teaching/workshop blocks comprise a 1 hour lecture followed by a 1 hour computer practical/tutorial. The computer practical/tutorial will involve hands-on computer work, guided by the course tutor and with students assisted by GTAs. The content of the exercise will vary, but generally will require students to work at their own pace on a set problem, with assistance from the tutor and GTAs available. Sessions will also feature class discussions and critical evaluation of published SEMs.

Intended Learning Outcomes

On completion of this unit successful students will be able to demonstrate:

Knowledge and understanding: Understand the nature of structural equation modelling and its relationship to other statistical methods, specifically regression, path, and latent variable models. Distinguish between categorical and continuous variables, both observed and latent. Identify the contexts when different structural equation models are appropriate.

Intellectual skills: be able to critically evaluate an example of structural equation modelling published in a scholarly journal. Be able to translate conceptual theory/hypothesis into appropriate structural equation models. Make appropriate scientific inferences from the results of structural equation models.

Practical skills: use MPLUS to specify and fit a range of structural equation models to ‘real’ datasets (e.g. the European Social Survey). Interpret and graph the parameter estimates generated by different structural equation models.

Transferable skills and personal qualities: write a report that synthesises evidence from relevant literature and the student’s own analysis.Exercise self-management skills in terms of pacing workload and meeting deadlines. Gain experience in analysing quantitative social data.


Critique of a published SEM study: 600 words (20%), report based on SEM analysis of data: 2,400 words (80%)

Preliminary reading

Byrne, B. M. (2011). Structural Equation Modeling with Mplus. Basic Concepts, Applications, and Programming. Routledge Academic. 

Kline, K. (2005). Principles and Practice of Structural Equation Modelling (2nd Ed.). New York: Guildford.



University of Manchester CCSR MSc SRMS