The workshop starts with a short recap on structural equation modeling. Then, we will discuss how to import data into Mplus, and how to specify a model using Mplus syntax. Participants are familiarized with the important parts of the input and output files. Also, we will discuss model fit indices, how to avoid common mistakes, and how to deal with error messages.
Please note that we only discuss very simple models: a basic regression model and a confirmatory factor analysis. If you’re interested in more complex models (e.g. latent class analysis, multilevel models, latent growth models), please take a look at our Mplus summer school courses taking place in July: Introduction to Structural Equation Modeling using Mplus and Advanced course on using Mplus
This one-day workshop is not about analyzing complex models: The main goal is to get you started with Mplus.
Good working knowledge of multivariate analysis is assumed. Some knowledge of Structural Equation Modeling is helpful, not mandatory. If you want to prepare, you could read (not obligatory):
Participants are requested to bring their own laptop for the computer practicals. Access to Mplus, R and/or SPSS will be provided through MyWorkplace.
Please note that there are no graded activities included in this course. Therefore, we are not able to provide students with a transcript of grades. You will obtain a certificate upon completion of this course.
For an overview of all our summer school courses offered by the Department of Methodology and Statistics please click here.
Research Master Students, PhD students or post-graduate researchers from a variety of fields will benefit from the course. A max. of 20 number of participant will be allowed in this course.
The main objective of the course is to acquire a basic understanding of how to use Mplus.
One day (10.00 – 17.00). Lectures and computer lab exercises will be alternated during this informal one-day workshop.
Tuition fee for PhD candidates from the Faculty of Social and Behavioural Sciences from Utrecht University will be funded by the Graduate School of Social and Behavioural Sciences.
Irma Reyersen | E: firstname.lastname@example.org