A 2-Day Seminar Taught by Tenko Raykov, Ph.D.
To see a sample of the course materials, click here.
Researchers in the behavioral, social, biomedical, business and economic disciplines often collect data that have a hierarchical structure. Patients are nested (clustered) within treatment centers, employees are nested within firms, respondents are nested within cities, students are nested within schools, and so on. As a consequence of this nesting, the observations in the data set are not statistically independent, thus violating a basic assumption of standard methods of analysis. Ignoring the nesting effect and proceeding with conventional, single-level methods of analysis (like linear regression) can yield highly misleading results. That’s because traditional methods produce standard errors that are typically too small, leading to confidence intervals that are too short and p-values that are too low.
This two-day seminar provides a thorough introduction to multilevel modeling, a statistical framework that accounts for the nesting effect and avoids these problems, as well as those associated with earlier methods of aggregation and disaggregation. Throughout the seminar, many empirical examples are drawn from the behavioral, clinical, educational and economic disciplines. The popular software package Stata is used for all the examples, along with a detailed discussion of the command syntax and interpretation of the output.
Participants in this seminar can expect to come away with:
1. A nuanced understanding of the conceptual foundations and basic mathematical formulation of the multilevel model.
2. The ability to understand, interpret and explain the output from multilevel modeling software.
3. An appreciation of the advantages and disadvantages of multilevel modeling as compared with other approaches to nested data.
4. Practical tools and strategies for developing and testing multilevel models.
5. The ability to extend the multilevel model to dichotomous outcomes.
6. A clear understanding of the differences between fixed and random effects.
This is a hands-on course with at least one hour per day devoted to exercises designed and supervised by the instructor.
To participate in the hands-on exercises, you are strongly encouraged to bring a laptop computer with Stata installed (Stata 15, but version 14 would suffice as well). However, no previous experience with Stata is assumed. A power outlet and wireless access will be available at each seat.
Who should attend?
To benefit from this seminar, you should have the equivalent of two semesters of statistics: a good introductory course with some treatment of probability and random variables, and a course on linear models. Some knowledge of logistic regression will also be helpful but not essential.
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 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 $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 SH0405 or click here. For guaranteed rate and availability, you must reserve your room no later than Monday, March 5, 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.
1. Resources for the seminar.
2. Why do we need multilevel modeling (MLM), and why are aggregation and disaggregation unsatisfactory?
3. The beginnings of MLM.
– Why what we already know about regression analysis is so useful.
– Centering of predictor variables.
4. The intra-class correlation coefficient – Do we really need a multilevel model?
5. How many levels? Proportions of third-level variance and at intermediate level, and how to evaluate them in three-level settings.
6. Random intercept models and model adequacy assessment.
7. Robust modeling of lower-level variable relationships in the presence of clustering effects.
8. What are mixed models, what are they made of, and why are they useful?
9. Random regression models – a general class of mixed/multilevel models of great utility great utility.
– Getting started and restricted maximum likelihood (REML) estimation.
– Random regression models.
– Multiple random slopes.
– Fixed effects, random effects, and total effects.
– Numerical issues and possible problems.
– Nested levels (higher-order nesting).
10. Mixed models with discrete response variables – what to do when the outcome is not continuous?
– Why do we need another modeling approach?
– Random intercept model with discrete outcome.
– Random regression model with discrete response.
– Model choice with discrete outcome.
11. Applications of multilevel modeling in complex design studies.
12. Conclusion and outlook.
“The class and lecture was very well planned and organized. It was easy to follow step-by-step and the instructor was clear, patient, and knowledgeable.”
Joseph Marchia, Stony Brook University
“Professor Tenko Raykov is phenomenal. He is extremely knowledgeable and leads the course excellently. I would highly recommend taking a future course with him as I found it very informative and thorough. Good coverage of the topic for research needs.”
“Helpful to understand the principles behind developing and choosing a model.”
Kathryn Anderson, McGovern Medical School, University of Texas
“Professor Raykov was great at putting everything in terms that would help you understand the concepts. I have not taken statistics in years but I found myself nodding along and really understood.”
Carla Lewandowski, Rowan University
“I had some prior experience with MLM, but there were some gaps in my knowledge. This course was fantastic and it filled in those gaps and gave me a much deeper understanding of MLM. I feel more confident about using MLM in my research as a result of this course.”
Peter Rivera, Pennsylvania State University
“This course was extremely well organized, implemented and taught. The instructor was able to break down complex issues to a very understandable level. I would strongly recommend this class.”
Kimberly Houser, Rowan University
“This course on MLM taught by Dr. Raykov was great. My work will be greatly benefited from the knowledge I gained. I feel so much more comfortable running these models now that I understand what errors to look out for.”
Jessica Rast, Drexel University
“I signed up for MLM because nobody in my department performs MLM in Stats. I personally tried to teach myself the methods but find an instructive classroom-like setting to be invaluable to the learning process. The amount of information disseminated and ability to get step-by-step instructions, explanation, and assistance over a 2-day period has been much more productive than my own 2+ months attempts to teach myself. Statistical Horizons offers knowledgeable qualified instructors who delve into the “how” and “why” to go through the method. Worth It!”
Jessica Kim, Stony Brook University
“I was intimidated by this complex statistics method prior to the course. After taking this course, I feel that I have a much better understanding of MLM. The instructor did a great job explaining the notion of MLM. Raykov makes it easy to understand for those who are not statisticians. He also addresses practical issues of using this analysis method. The examples he uses are easy to understand as well. Also, I liked the handbook; it is written in a way that I can recall what the instructor said during the course. This course is well worth it. I would highly recommend it.”
Ying-Ling Jao, Pennsylvania State University