A Second Course
A 2-Day Seminar Taught by Kristopher Preacher, Ph.D.
To see a sample of the course materials, click here.
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 “second course” in MLM will introduce a variety of MLM extensions, including cutting-edge multilevel structural equation modeling (MSEM) to handle complex designs and modeling objectives. Throughout the seminar, empirical examples will be presented to illustrate key concepts. A background in structural equation modeling (SEM) is not necessary.
On Day 1 we will begin by reviewing the basics of MLM, including
- The motivation for MLM
- Key concepts
- Equation conventions
- The univariate two-level MLM with fixed and random coefficients
Mplus will be introduced as a flexible and powerful software environment for fitting basic and advanced multilevel models. Next, we will cover several advanced MLM topics, including
- Estimating, plotting, and probing interaction effects
- Modeling cross-classified data
- Modeling discrete (e.g., binary, count) dependent variables
- Conducting power analysis for MLM using a general Monte Carlo technique
- Fitting multivariate multilevel models
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 exploratory and confirmatory factor analysis
- Multilevel path analysis
- Multilevel structural models with latent variables
- Multilevel mediation analysis
- Multilevel reliability estimation
- Applications to cross-classified and three-level data
Throughout the two-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
- Documented Mplus syntax templates for fitting a variety of multilevel models.
Who should attend?
This seminar is designed for researchers who have some prior experience with multilevel modeling (e.g., in a seminar, workshop, or course) 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 $995.00 includes all seminar 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 $159 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 STSH18 or click here. For guaranteed rate and availability, you must reserve your room no later than Tuesday, September 18, 2018.
If you make reservations after the cut-off date, ask for the Statistical Horizons room rate (do not use the code) and they will try to accommodate your request.
– Introduction: What does this course cover?
– Review of multilevel modeling (MLM basics, definitions, and equations)
– Introduction to Mplus MLM syntax and capabilities
*Estimating, plotting, and probing interaction effects in MLM
*Modeling cross-classified data
*Multilevel models with discrete dependent variables
*Power analysis and sample size determination for MLM
*Multivariate multilevel models
– Overview of single-level structural equation modeling
– Introduction to multilevel SEM
– MSEM path diagram conventions and equations
– MSEM implementation in Mplus
*Multilevel confirmatory factor analysis
*Measurement invariance in multilevel factor models
*Multilevel exploratory factor analysis
*Multilevel path analysis
*General multilevel SEM with latent variables
*Three-level structural models
*Multilevel reliability estimation
(*Mplus examples provided and discussed.)
“This is the second course I’ve taken on MLM, and it was far and away one of the best statistics workshops I’ve been to. Preacher is a terrific instructor – he is incredibly knowledgeable yet easy to understand. I feel confident I will be able to quickly and easily apply what I learned. This was very much worth the time and money. I strongly recommend this course to anyone interested in MLM. Thank you Statistical Horizons and Dr. Preacher!”
Shaun Pichler, California State University, Fullerton
“One of the best statistical courses that I have attended so far. The instructor is excellent and the content and material on multilevel modeling are conclusive.”
Sakun Boon-itt, Thammasat Business School
“I am undertaking MSEM to analyze observational (real world) oncology HCRU data. After having read Mplus books on MLM and the literature, Kristopher confirmed my understanding and added to what I knew with this exceptional teaching. Invaluable. Thank you Kristopher.”
Mark Boye, Eli Lilly and Company
“I attended this course because these models are complex and sifting through the literature is sometimes challenging given that there are multiple ways to test models and the recommended best approaches that are best learned from experts in the field. It is a real time saver to have Kris Preacher show you all these things and consult with him than it is to figure out on your own.”
Francisco Palermo, University of Missouri
“As a senior scholar, I wanted to learn about the latest, most sophisticated analytic tools. This 2-day workshop was a great opportunity to immerse myself in analyses. Can’t wait to use these tools with my data. Preacher is a wonderful teacher. I highly recommend the course.”
Rachel Pruchno, Rowan University
“This course gave me the resources and support I needed to feel confident in my analysis. It was perfectly suited for the intermediate multilevel researcher who uses Mplus.”
Sharon Sheridan, University of North Dakota
“Kris made complex concepts very accessible. He is an exceptionally clear and thoughtful instructor.”
Robert Stawski, Oregon State University
“I enjoyed this course. Kristopher is a very good instructor – very experienced and knowledgeable at managing our intake of the course and any questions that arose. Highly recommended!”
Shuang Ren, Deakin University