This 3 day course has been exceptionally well delivered by an active researcher who is extremely knowledgeable, patient and thoughtful to the needs of the audience. I particularly enjoyed the blend between lecture material and practical skills development using MPlus, supported by excellent handout material and supporting electronic files. I would highly recommend this course to anyone who is new to MPlus.

Honestly, I loved it. If someone asked me if I would recommend the experience I would say yes, definitely. I started without any knowledge about Mplus and in just three sessions I came out with many ideas to apply to my own research. I think the organization of the course was perfect. The explanations were always clear, with numerous examples. A subject as arduous as statistics was easy to understand. To this also contributed the fact that they made breaks every hour and quarter!

This is the best course I have ever taken – Gabriela makes these complex and advanced techniques simple and easily digestible, prepare to learn a great deal and have a lot of fun doing it!

It was so incredibly helpful and I actually feel as though you gave me a very important gift in my career.  I have many skills as a researcher but never saw complex methods as something that would be accessible to me.  I had always seen statistics as something to be feared and struggled with but your course actually gave me the most wonderful enthusiasm and excitement about working with my data.  It’s made me so much more confident going forward and I will be forever grateful for that.  I have had so many ideas since your course that it’s hard to focus on just one.

This MPlus course was fantastic. It not only provided me with the skills and confidence to conduct the analyses in MPlus but, also, the details of why, how, and what the analyses were about. Furthermore, I now know how to read and interpret the output files to assess and modify my models.

I attended the Structural Equation Modeling with Mplus course this autumn and found it very helpful. Gabriela explains and illustrates statistical models and Mplus programming in ways that ensures understanding, practical knowledge, and interpretation of the results. Her pedagogical ways in teaching are extraordinary and she explains difficult topics very well and ensures integrated understanding from the audience. The course, with good theoretical introductions and examples, ensures that every participant will progress in practice and understanding throughout the course.

Mplus course in Structural Equation Modelling (SEM) is truly unique. The course offers important topics about SEM and factor analysis that are all taught by a wonderfully supportive instructor who made me love statistics, even more; especially that it was not taught in a technical way. All the examples that were used made it easy to understand the statistics behind it. I wholeheartedly recommend it to anyone who is interested in statistics and looking to gain the essential skills needed for their academic careers, including graduates and researchers from different fields.

I did three courses in SEM in different UK universities last year, and this is by far the best. This course is pragmatic, it teaches you what you need without dwelling into complex mathematical explanations but at the same time, the basics are covered so you can be confident you will do your analysis rigorously.

The Mplus course is practical oriented. Many useful and helpful examples are provided in a cooperative atmosphere. And I really enjoyed the group atmosphere of the course.

Attending the course turned out to be the best decision I ever made. The course has indeed provided me with the knowledge and skills necessary to analyze my data and turn them into meaningful causal models. Advanced topics such as mixture modeling, which I thought I’d never ever grasp, was made very simple. In sum, the course was awesome and enjoyable and I would highly recommend it.

Before starting on the course I didn’t know anything about SEM or MPlus. By the end of the course I felt confident enough to try these techniques on my own data. It was a very supportive and flexible course taught by a very knowledgeable tutor.

The most useful course I went to. If you want to start working with Mplus, I strongly recommend taking this course. Everything is right: the pace, the reasonable size of the group, the theory kept to a bearable amount and the multiple applications and examples to get hands on the data analysis straight away. Gabriela makes sure everyone follows and is not stuck before moving on, so that we actually learn and apply the new knowledge straight away. I started using Mplus for longitudinal data analysis (latent growth curve modelling) for my own research after the course and the course material is my bible – I am referring to it all the time!

I really enjoyed the Mplus course. You don’t feel left behind, as it is practical and hands-on. I did not know a thing about Mplus nor SEM before I started the course and now I was already able to use some of the techniques on my own data…with success!

This MPlus course with Gabriela was a great experience! It offered me a profound theoretical basis on SEM together with a lot of useful tips-and-tricks to work on my own data. Gabriela’s enthusiasm really encouraged me and she managed to explain complex ideas in a really comprehensible way. I would recommend this course to anyone interested in MPlus!

I recently attended the structural equation modeling course run by Dr Gabriela Roman at Cambridge University. It was an instant relief to be a part of this course since I knew I would be using SEM for one of my PhD chapters but had no prior knowledge of this technique, and the course was run at a very comfortable pace, it was highly comprehensive and enjoyable. I benefitted very much, taking away with me invaluable knowledge of the basic foundations of SEM as well as insight into the intricacies of actually running these models and dealing with common and unusual problems- the latter of which I have been very grateful for recently when working with my own data! I highly recommend anyone who will be working with SEM to attend this course, it was excellent.

This is a course that you should take, no matter whether you studied SEM before or not, or whether you used Mplus before or not. The scope of the topics covered in this course caters for pretty much every SEM researcher.

The course is very useful; the clear instruction explains how to trouble shoot difficulties in Mplus, leaving students confident at approaching future analysis.

Gabriela’s course is unique in being taught by a real statistics expert who can answer any question you can imagine. The fact that she can also teach the basics with clarity makes this class highly valuable.

Importantly, the book offers a plethora of nuggets of knowledge that are near-impossible to find out there (at least it’s impossible to find them without scouring the internet and various obscure fora), like how set up loop plots or how to flexibly use the “model constraint” command for your own analyses.

A whole set of different mediation models. Whatever your research topic, you are bound to want to answer the “why” and “how” questions, which will call for the introduction of causal chains and mediators. But mediation models can vary greatly and having this book handy will really  make a difference.

Trouble-shooting problems in a detailed way. For example, what can you do if you get a standardized coefficient greater than 1?

Detailed examples of a multitude of regression models, with explanations of how to interpret the output files. If you’re a beginner and need to do a zero-regression Poisson regression because your outcome variable is a count variable (e.g., nr. of crimes or nr. of hours spend playing video games), you can find how to specify a simple model in the user guide. But interpreting the output is a whole different ball-game and this book really provides all the hand-holding you might need.

Easy explanation of the Maths behind regression models (I’ve put this first because it’s such a relief to find all this infomation in one place, instead of struggling to learn it from fragmented sources – but if this is not your cup of tea, move on to the next point).