Course 2 - Longitudinal Modelling in MPLUS: Integrating Biological Data
Course Directors: Edward (Ted) Barker and Andrew Pickles
This course has two overall goals: 1) to introduce longitudinal applications of structural equation modelling in the Mplus software, and 2) to demonstrate how biological data can be integrated with environmental and social data within models of this kind.
This course develops the statistical theory required to understand both:
- longitudinal path analytic; and
- latent models of growth and change.
However, the emphasis is on data-analytic practice and hands-on experience rather than on mathematical statistics. The course is centered on applied topics that are integral in estimating auto-regressive path-analysis, latent growth curves and growth mixture models. Most analytic topics are followed by a “hands on” practical session. Participants will learn to do straightforward path analysis, growth curve and mixture analysis and interpret the model assumptions and the results sensibly.
The Mplus software will be introduced using prepared examples and data provided by the course Instructors. Special focus will be center on the research questions that lead to the use of either auto-regressive path analysis (certain variables influencing other variables), latent growth curves (i.e., population growth averages) and general mixture models (i.e., a population comprised of two or more distinct growth trajectories), including specialized applications of these analytical routines. The course will also highlight how ‘traditional’ applications of longitudinal modeling can be generalized to include biological data, including brain imaging, DNA methylation, molecular genetics and infectious diseases.
Course fees: £700
KCL staff and students receive a reduced rate of £350.
For further information, please contact the Summer School Coordinator.
Registration for the course is now OPEN. Please click here to apply.
**Course registrations are now open.**
The MRC SGDP Summer School is part of the KCL SUMMER INSTITUTE.