- This event has passed.
Mplus: A beginner’s course in longitudinal data analysis
14th September 2018 @ 8:00 am - 5:00 pm
£395| Organised by | CambridgeSEM, Selwyn College, Cambridge |
| Presenter | Dr Gabriela Roman |
| Date | 14/09/2018 – 16/09/2018 |
| Venue | Selwyn College, University of Cambridge, Grange Road, Cambridge |
| Map | View in Google Maps (CB3 9DQ) |
| Contact | Gabriela: [email protected] |
| Description | This 3-day course is a beginner’s course in SEM with Mplus. It is hosted by Selwyn College, University of Cambridge. The only prerequisites needed for this course are: to be familiar with the general structure of the input and output files of Mplus; knowledge about confirmatory factor analysis; (optional): knowledge about measurement invariance.
The first day provides an introduction to longitudinal data analysis and includes auto-regressive models and longitudinal factor analysis with invariance constraints. The second day is focused on individual trajectories over time and includes linear, quadratic and piecewise growth models. The third day is focused on group-based trajectory models and includes latent class growth models, growth mixture models and longitudinal latent class analysis. You can find further details here: https://mpluscambridge.com/ |
| Level | Entry (no or almost no prior knowledge) |
| Cost | £395 |
| Website and registration | |
| Region | East of England |
| Keywords | Quantitative Data Handling and Data Analysis, Longitudinal Data Analysis, Cross-lagged panel models, Growth curve models, Growth mixture models, Latent class growth analysis, Latent Variable Models, Latent class analysis, Factor analysis, Confirmatory factor analysis, Structural equation models |
| Related publications and presentations | Quantitative Data Handling and Data Analysis Longitudinal Data Analysis Cross-lagged panel models Growth curve models Growth mixture models Latent class growth analysis Latent Variable Models Latent class analysis Factor analysis Confirmatory factor analysis Structural equation models |