Introduction to Structural Equation Modelling using Mplus
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 email@example.com. Places will be confirmed after 1st September depending on availability.
Duration: 3 days
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.
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
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
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
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