The Cathie Marsh Centre for Census and Survey Research
By accessing this site you agree to be tracked by Google analytics cookies.

Introduction to Structural Equation Modelling using Mplus

Book on Introduction to Data Analysis Part 1 Dates: 10-12th March 2015

We will be launching a new website in September 2014 and will be taking bookings from 1st September onwards. To book a place, please email Places will be confirmed after 1st September depending on availability.

Duration: 3 days
Level: Intermediate
Course Fee: £585 (£420 for those from educational and charitable institutions)

CCSR offers 5 free places to research staff and students within the Faculty of Humanities at the University of Manchester and the North West Doctoral Training Centre.

Course Leader: Dr Nick Shryane and Professor Tarani Chandola

Course Requirements: Participants should be familiar with statistical modelling using linear regression and binary logistic or probit regression.

Course Overview

Structural Equation Models (SEM) amalgamate regression analysis, path analysis and factor analysis, allowing for more richly detailed statistical models to be specified and compared to data than by using these techniques individually. Historically, SEM models were confined to the analysis of continuous observed data, limiting their usefulness in applied social research, where many phenomena are inherently discrete or are measured only with coarse-grained instruments. Advances in recent years have made SEM methods for categorical data available to applied researchers. This course aims to train quantitative social scientists to use the Mplus programme in the application of structural equation modelling techniques to non-continuous observed data. The course also aims to integrate approaches that assume latent dimensions of variation (e.g. factor analysis) with approaches that assume unobserved groups or categories (e.g. latent class analysis). 


Provisional Course Syllabus 

Day 1

Session 1: Introducing Mplus

Session 2: Regression models for binary categorical data

Session 3: Path Analysis I: continuous dependent variables

Session 4: Path Analysis II: categorical dependent variables

Day 2

Session 5: Continous latent variables I: Modelling continuous observed data: Factor Analysis

Session 6: Continous latent variables II: Modelling binary observed data: Item-Response

Session 7: Structural Equation Modelling

Session 8: Multi-group Structural Equation Modelling

Day 3

Session 9: Categorical latent variables I: Mixture Models

Session 10: Categorical latent variables II: Latent Class and Latent Profile Analysis

Session 11: Repeated measures modelling I: autoregressive and cross lagged panel models

Session 12: Repeated measures modelling II: linear and non-linear growth models


Book on Introduction to Data Analysis Part 1

University of Manchester CCSR