Basics of multilevel modeling: handling grouped data in research and evaluation
October 29 @ 10:00 am - 4:00 pm$250
A 6-hour workshop taught by Paul D. Umbach, Ph.D.
Regression workshop series discount!
Register for this along with our workshops on Refresher on multiple regression for the applied researcher (September 25) and Logistic Regression: Analyzing binary outcomes (October 23) for only $500 (would normally cost $700 for all three). If you are interested in the workshop series, email us at firstname.lastname@example.org for discount codes.Register
This workshop provides the basics of multilevel modeling, focusing on practical applications rather than statistical theory. Researchers and evaluators from a range of disciplines often collect data that have a hierarchical structure. Students are nested within schools (and/or classrooms), employees are nested within firms, and multiple test scores are nested within students. One consequence of this nested structure is that observations are not statistically independent, thus violating a basic assumption of standard analytical techniques. Ignoring the nesting effect and proceeding with conventional, single-level methods of analysis (like linear regression) can yield misleading results. This one-day seminar provides an introduction to multilevel models (sometimes called hierarchical linear models or general linear models), a statistical approach that accounts for the nesting effect and avoids these problems, as well as those associated with aggregation and disaggregation.
By the end of this workshop, participants should know the following
- The conceptual foundations of multilevel models.
- An appreciation of the advantages and disadvantages of multilevel modeling as compared with other approaches to nested data.
- Modeling slopes and intercepts as outcomes.
- How to build two-level models using HLM 7.
- Approach to building growth models in a multilevel context.
- How to interpret and explain the output from multilevel modeling software.
- Practical tools and strategies for developing and testing multilevel models.
- A clear understanding of the differences between fixed and random effects.
Pricing and schedule
Time: Thursday, October 29, 10:00AM to 4:00PM (EST)
Register for this along with our workshops on Refresher on multiple regression for the applied researcher (September 25) and Logistic Regression: Analyzing binary outcomes (October 23) for only $500 (would normally cost $700 for all three). If you are interested in the workshop series, email us at email@example.com for discount codes.
We offer $50 discounts for graduate students and $25 discounts for multiple workshop enrollments. Find out about our discounts here.
Time permitting, at the end of the session Dr. Umbach will also answer questions about participants’ specific research projects. Participants can ask questions via chat, microphone, or telephone. In order to allow sufficient time for questions, the number of workshop participants is limited to 30.
Who should attend?
The target audience for this workshop is a range of educational researchers, including institutional researchers, evaluators, policy analysts, student affairs professionals, assessment professionals, graduate students, and faculty. Participants must have a strong working knowledge of ordinary least squares regression. If you do not feel comfortable with regression, consider enrolling in our regression refresher workshop.
- Why we need multilevel modeling and why other approaches are inadequate
- Brief discussion of conceptual underpinnings of multilevel modeling
- Extending regression with random intercepts and slopes as outcomes
- Centering the variables we include in our models
- Building the two level model: Null model, random intercept model, and full model
- Using real data and HLM 7 to build models
- Interpreting two-level model output
- Using multilevel model to study growth
- Interpreting growth model output
- Introduction to extensions of the two-level model: multilevel models with categorical outcomes, three level models, and cross classified models.
We will be using HLM 7 for class demonstrations. You can download the HLM 7 student version at the SSI Scientific Software website. Please note that HLM is only compatible with Windows. I will also provide output, data, and syntax for SPSS, Stata, and SAS.Register