Multilevel Structural Equation Modeling
A 5-Day Seminar Taught by Kristopher Preacher, Ph.D.
To see a sample of the course materials, click here.
This course is currently full. If you would like to be added to the waitlist, please send us an email at firstname.lastname@example.org.
Hierarchically clustered (multilevel or nested) data are common in the social sciences, medical fields, and business research. Clustered data violate the assumption of independence required by ordinary statistical methods. Increasingly complex research designs and hypotheses have created a need for sophisticated methods that go beyond standard multilevel modeling (MLM). This course will introduce a variety of extensions to MLM, including cutting-edge multilevel structural equation modeling (MSEM) to handle complex designs and modeling objectives. Throughout the workshop, empirical examples will be presented to illustrate key concepts. A strong background in structural equation modeling (SEM) is not necessary.
Please note, this course will include all material from the 2-day Multilevel Modeling: A Second Course.
On Day 1 we will begin by reviewing the basics of MLM. Next, Mplus will be introduced as a flexible and powerful software environment for fitting basic and advanced multilevel models. Then we will cover several advanced MLM topics.
Basic MLM topics include:
- The motivation for MLM
- Key concepts
- Equation conventions
- The univariate two-level MLM with fixed and random coefficients
Advanced MLM topics include:
- Conducting power analysis for MLM using a general Monte Carlo technique
- Fitting multivariate multilevel models
- Modeling cross-classified data
On Day 2 multilevel structural equation modeling will be introduced as a general approach for more complex modeling tasks. After a brief overview of single-level SEM, we will turn to the development of MSEM and the important advantages of MSEM over MLM (e.g., inclusion of latent variables, complex causal pathways, upper-level outcomes, and model fit assessment). Standard SEM and MLM will be recast as special cases of MSEM. Next we will cover a variety of MSEM topics:
- Multilevel path analysis
- Multilevel exploratory and confirmatory factor analysis
- Model fit in MSEM
On Days 3-5 we will continue to explore special applications of MSEM. Advanced topics will include:
- Multilevel structural models with latent variables
- Applications to three-level (and higher-level) data
- Multilevel reliability estimation
- Multilevel mediation analysis
- Multiple group models
- Estimating, plotting, and probing interaction effects
- Moderation in MLM and MSEM
- Modeling discrete (e.g., binary, count) dependent variables
- Interval estimates for nonnormal statistics
- Handling convergence problems: A bag of tricks
- Conducting Monte Carlo simulation studies
Days 3-5 will also involve small group exercises to get practice using Mplus to fit models and conduct power analyses and Monte Carlo studies. Informal homework assignments will be given on Days 1-4, and discussed the following morning.
Throughout the five-day course, models will be presented in several formats—path diagrams, equations, and software syntax. Data and Mplus syntax for all of the examples will be included in the workshop materials.
Participants in this seminar can expect to gain:
- Mastery of advanced topics in MLM
- A deeper understanding of the relationship between MLM and SEM
- The ability to use multilevel SEM to test complex structural hypotheses
- Resources to conduct power analysis for virtually any multilevel design
- The ability to fluently interpret and translate among path diagrams, model equations, and Mplus syntax for advanced MLM and MSEM
- Strategies for tackling convergence problems and estimation errors
- Programming skills for conducting Monte Carlo studies to assess model feasibility prior to data collection.
- Documented Mplus syntax templates for fitting a variety of models to multilevel data.
Who should attend?
This seminar is designed for researchers who have had some exposure to multilevel modeling and/or structural equation modeling (e.g., from seminars, workshops, or courses) and who want to deepen and extend their knowledge. At a minimum, participants should have a good working knowledge of basic principles of statistical inference (e.g., standard errors, hypothesis tests, confidence intervals), and should also have a good understanding of the theory and practice of linear regression.
Mplus will be used for all worked examples, but prior knowledge of Mplus is not essential. You are welcome and encouraged to bring your own laptop computer with Mplus installed (including the multilevel or combination add-on). However, this is not required. Participants will still benefit from the comprehensive set of slides and syntax that they can apply at home.
Location, Format, and materials
The class will meet from 9 am to 5 pm each day with a 1-hour lunch break at Temple University Center City, 1515 Market Street, Philadelphia, PA 19103.
Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking.
Registration and Lodging
The fee of $1895.00 includes all course materials.
If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).
Lodging Reservation Instructions
A block of guest rooms has been reserved at the Club Quarters Hotel, 1628 Chestnut Street, Philadelphia, PA at a special rate of $139 per night. This location is about a 5-minute walk to the seminar location. In order to make reservations, call 203-905-2100 during business hours and identify yourself by using group code STH728 or click here. For guaranteed rate and availability, you must reserve your room no later than Friday, June 28, 2019.
If you need to make reservations after the cut-off date, you may call Club Quarters directly and ask for the “Statistical Horizons” rate (do not use the code or mention a room block) and they will try to accommodate your request.
“I came to this workshop with strong backgrounds in both MLM and SEM, but limited knowledge of MSEM. After reading a variety of papers prior to the course, I arrived with a long list of questions. At the end of the week, I am pleased to say that Kris answered each and every one of them. We covered a wide range of fundamental topics in MSEM, and I feel extremely confident in moving forward using these analyses in my own work. Kris is an excellent teacher and went above and beyond to make himself available to patiently answer everyone’s questions and help people with their own research. I would enthusiastically recommend this course (and Kris’s teaching in general) to students with SEM and MLM experience hoping to learn how to merge those methods!”
Timothy Hayes, Florida International University
“The MSEM course was extremely helpful in fostering my statistical knowledge. I came into the course as a bit of a beginner, but Kris conveyed the material effectively to the variety of skill levels present in the classroom. I found the one-on-one consultation portion the most helpful, as I was allowed the opportunity to address my remaining questions from the material, along with my own statistical questions in my research. I would recommend the course to anyone looking to obtain a better understanding of MLM. I’m walking out of the course much better prepared in my statistical analyses.”
Claire Tomlinson, Indiana University
“Dr. Preacher did an excellent job explaining complex statistical methods in an intuitive way. He provides excellent notes and many resources and examples of code. He is also quite patient when fielding questions and provides numerous “on the fly” examples that greatly augment the course. I look forward to applying what I learned to address new hypotheses that before the course, I would not have had a clue how to approach. In other words, 10/10 would recommend!”
Michael Murphy, Carnegie Mellon University
“The instructor (K. Preacher) was patient and diligent in answering questions from many different levels of expertise. In our Multilevel SEM course, a lot of valuable information was covered, and he spent time assisting my team with developing a model for our specific project. Course content was taught thoughtfully. Some content was more advanced, so I would recommend strongly that future participants take introductory courses in Mplus and regression and various analytical methods prior to this course. Overall, an excellent opportunity with a very impressive expert!”
Lily Jiang, Indiana University
“This course solidified my knowledge on a diverse array of concepts, ranging from entry SEM concepts to complex MSEM, power analyses, and simulation methods. I am confident I will apply these tools in my work with much more knowledge than I could have done with self-guided study. Really appreciated this course!”
“Great examples and a bunch of Mplus code to tailor to your analysis.”
Salma Musaad, University of Illinois at Urbana–Champaign
“This class presumed little prerequisite knowledge on SEM and, in 5 days, achieved confident knowledge and practice in using Mplus for MLM and MSEM. Instructor notes, student manuals, and extensive references provide ample help in applying MSEM after the class.”
Robert Stoddard, Carnegie Mellon University
“I really like this class a lot. Dr. Preacher is extremely knowledgeable on this subject and incredibly patient with questions. He will set aside time specifically for one-on-one consultation. During the consultation sessions, you can ask questions on the course or your own dataset. He went to great lengths to help you resolve the problems. If he was unable to offer a solution right away, he made suggestions and pointed to relevant literature. Personally, I benefitted from this course by getting more acquainted with MLM of different variants. This will undoubtedly help advance my research and teaching in the future.”
“I found the course extremely useful and applicable to my research. The course provides information on a topic that is needed in research.”
Alexander Madsen Sandvik, Norwegian School of Economics