Introduction to Structural Equation Modeling using Mplus

About

We offer a five-day course on how to perform basic structural equation modeling (SEM) analyses using Mplus, where you can interact with Mplus experts. No previous knowledge of Mplus is assumed, but prior knowledge of SEM, although not mandatory, will make this course more useful.

Many researchers in the social and behavioral sciences are using, or want to use, SEM to investigate their theories. Mplus is a popular and flexible software package for doing SEM.

The course starts with an introduction on how to use Mplus to perform regression analysis and how to ‘communicate’ with Mplus (e.g. how to specify a model, and how to deal with error messages). In the following days, basic models relevant for social scientists will be discussed, including multiple group models, confirmatory factor analysis, and cross-lagged panel models, and important topics such as moderation, mediation and testing for measurement invariance are covered. These days consist of lectures by Mplus experts and computer labs where participants practice with Mplus. On the last day, you can meet with Mplus experts for individual consultation, and work on your own data.

Prerequisites

Researchers are expected to have a basic knowledge of regression analysis (e.g., know how to perform basic statistical tests such as t-test and how to interpret model parameters, as well as the standard errors and p-values). These techniques are discussed in most books on multivariate statistics (e.g., Andy Field: Discovering Statistics; Tabachnick and Fidell: Using multivariate statistics).

Some knowledge of SEM and software like AMOS, LISREL, Mx or EQS is helpful, but not mandatory. If you have no experience with SEM, please

visit https://www.goquantfish.com/courses/mplus-for-beginners
and/or

read the following paper: Hox, J. J. & Bechger, T.M. (2007). An introduction to structural equation modelling. Family Science Review, 11, 354-373. Accesible via https://www.joophox.net/publist/semfamre.pdf
No previous knowledge of Mplus is assumed. You also do not need to have any knowledge of matrix algebra, calculus, or likelihood theory.

Recommended combination & Related courses

A good follow-up is our summer course ‘Advanced longitudinal modeling in Mplus’ (S23), in which participants learn to use SEM to analyze longitudinal data.
The substantive content of this course largely overlaps with the summer course Structural Equation Modeling in R using lavaan (S65), where each have their own specializations. These two courses can, therefore, be viewed as largely equivalent alternatives that target different audiences by employing different software (i.e. Mplus vs lavaan) and instructional formats (i.e. on-location vs online/e-learning).

More information

Participants are requested to bring their own laptop. If needed, software will be available online (please make sure to install this before the start of the course, you will receive an e-mail with information about this).

Course leader

Dr. Rebecca Kuiper

Target group

Participants (Research master Students, PhD students, or post-graduate researchers) from a variety of fields, including sociology, psychology, education, human development, marketing, business, biology, medicine, political science, and communication sciences may benefit from the course.

The selection will be done on a first-come-first-served basis. Please note that there is always a one-day delay between the registration system and the information on the website about the availability of places.

There are no restrictions on who can participate beyond the prerequisites; academics, researchers, and professional participants are all welcome to register for the course.

We also offer tailor-made M&S courses and in-house M&S training. If you want to look at the possibilities, please contact Dr. Laurence Frank at pe.dsai@uu.nl.

Aim of the course

Course aim

The main objective of the course is to acquire a basic understanding of how to use Mplus for SEM as applied in the social and behavioral sciences. Moreover, participants will learn how to analyze datasets with Mplus, to interpret the output and to report the results.

Fee info