Multilevel Modeling

Our workshop is designed for graduate students, post-doctoral fellows, faculty, and research scientists from the behavioral, social, and health sciences. Although it is recommended that participants have a working knowledge of the general regression model, these core concepts will be reviewed at the beginning of the workshop.

Software demonstrations will be provided in R, SAS, SPSS, and Stata. While it is helpful to have some familiarity with one of these programs, this is not necessary. The lectures which constitute the majority of the workshop are software-independent

Our motivating goal is to provide an intense yet enjoyable instructional experience that focuses on a large number of both introductory and advanced topics in multilevel modeling. We strive to strike an equal balance between core concepts of the underlying statistical model along with the practical application and interpretation of multilevel models fitted to real empirical data. Our workshop is designed to provide participants with the materials and instruction needed to both develop a real understanding of the multilevel model and to be able to thoughtfully apply a variety of basic and advanced multilevel models to their own data.

In an effort to continually improve our instruction we obtain student evaluations with each course offering. Here is a sample of reviews from our 2016 Multilevel Modeling workshop:

Thank you for such an amazing workshop! I learned so much and cannot wait to dig into my dissertation data!

The concepts and practical applications are hard for me, but in this class I really felt like I’ve had some Eureka moments. It really came together for me. I think that speaks to both of your teaching abilities, the format of the class, and the organization of the materials. I love the dynamic between you two and your teaching styles complement each other well. I had a blast, a really good time, and loved it.

Everything is a strength -- balance between lecture/practical, the pace at which the material is delivered, the course material/resources, etc. Really just everything.

The workshop does a great job of explaining MLM on a conceptual level and provides useful examples to better understand the modeling. The instructors were great at teaching the material and were very open to answering questions.

This workshop strikes a perfect balance between theory and practical applications. I particularly appreciated this as my MLM grad class was several years ago, so this was a refresher and an extension now that I have the data to actually analyze.

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Dan Bauer and Patrick Curran co-teach the workshop and alternate lecturing throughout the day. Participants will receive approximately 35 hours of total lecture time as well as PDF copies of the course notes and the computer demonstrations as well as data and code for all examples. The PDFs are not time-limited and may be retained indefinitely but should not be distributed to others without obtaining prior permission.

Please see sample copies of the lecture notes and SAS software notes (parallel versions exist in R, SPSS, and Stata).

Learn more about what makes our training unique...

Participants can ask text-based questions using a Zoom function that will be monitored by CBA staff and either answered by text or conveyed to the instructor and answered live. Participants can also use the "Raise Hand" function to ask a question verbally.

Because participants are receiving the stream from Zoom and not broadcasting video images back, the connectivity requirements are minimal. A minimum of 150kbps (kilobytes per second) is required to participate in a video webinar, and this can be wired or wireless. Given typical home internet connections or personal WiFi access, these requirements are quite low. For example, it is recommended that a 3000kbps (or 3mbps, megabytes per second) connection be used to stream a movie on Netflix. A typical WiFi hotspot on a typical cell phone is 20-30mbps, thus any standard internet connection should allow for uninterrupted participation in the webinar. See https://www.speedtest.net/ to evaluate your own connection speed. Note that a typical source of connectivity problems in the home is linking the device to the WiFi broadcast unit, so be certain your device is has uninterrupted lines of site to the wireless modem; see, e.g., https://www.familyhandyman.com/smart-homeowner/9-simple-tips-for-faster-wi-fi/

Live-stream lectures will begin at 9:00 a.m. EST and finish at 4:00 p.m. EST each day. On Monday through Thursday, computer demonstrations will be provided from 4:00 p.m. EST to 5:00 p.m. EST, with separate breakout meetings each day for each software program.

There will be 15 minute morning and afternoon breaks and a one hour lunch break, the exact times of which are determined during lecture. Local time zones within which the participant is connected must be adjusted to correspond to Eastern Standard Time.

The live-stream does not have DVR-like controls and thus cannot be paused or rewound during the session itself. However, full recordings of the live-stream will be available to all participants for seven days following the completion of the workshop. The recordings cannot be saved by the participant and will not be available after the seven day period.

CBA is not able to provide technical support for end-user issues. As such, participants are fully responsible for connectivity that supports the live-stream of audio and video. Information will be provided about the minimum required bandwidth and methods for testing connectivity. However, in the low probability that a participant is not able to connect, there will be access to the recorded sessions for seven days following the completion of the workshop.

We offer reduced-price registrations for graduate students who are actively enrolled in a recognized masters or doctoral training program. No application is necessary to qualify for the student tuition rates; simply choose the student rate when beginning the registration process at the top of the page. Confirmation of student status may be requested at a later time.

We are fortunate to have the opportunity to work in collaboration with the Society of Multivariate Experimental Psychology (SMEP) to provide a limited number of financial awards to students from under-represented groups to attend methodological workshops. These awards are made to qualifying students and post doctoral fellows with available funds of up to $1000 per student. Please see Support for Students from Underrepresented Groups to Attend Methodological Workshops for full details on both of these sources of support.

Curran-Bauer Analytics will fully refund registration fees, minus transaction fees, for cancellations made with one week or more notice prior to the event. Registration fees are non-refundable if a cancellation is made less than one week before the event.

Chapter 1. General Introduction
1.1 Nested Data Structures
1.2 Why Conventional Statistics are Inappropriate for Nested Data
1.3 Methods for Analyzing Nested Data

Chapter 2. Basic Multilevel Models
2.1 The Random-Effects ANOVA Model
2.2 Incorporating Lower-Level Predictors
2.3 Incorporating Upper-Level Predictors

Chapter 3. Decomposing Between- and Within-Group Effects
3.1 Total, Between-Group and Within-Group Effects
3.2 Centering Level 1 and Level 2 Predictors
3.3 Example: Effects of Centering
3.4 Frequently Asked Questions

Chapter 4. Random Slopes and Cross-Level Interactions
4.1 Including Random Slopes for Level-1 Predictors
4.2 Example: Random Slopes
4.3 Modeling Cross-Level Interactions
4.4 Example: Cross-Level Interaction

Chapter 5. Model Assumptions and Model Evaluation
5.1 Model Assumptions
5.2 Model Assessment and Diagnostics
5.3 Maximum Likelihood Estimation

Chapter 6. The Analysis of Repeated Measures: Part 1
6.1 Growth Curves Within the Multilevel Framework
6.2 Alternative Metrics of Time
6.3 Alternative Level-1 Error Structures
6.4 Modeling Non-Linear Change

Chapter 7. The Analysis of Repeated Measures: Part 2
7.1 Time-Invariant Predictors of Growth
7.2 Multiple Groups Models
7.3 Time-Varying Covariates

Chapter 8. Multilevel Models for Intensive Longitudinal Data
8.1 Unique Features of Intensive Longitudinal Data
8.2 Modeling Time Trends
8.3 Serially Correlated Residuals
8.4 Within- and Between-Person Effect Decomposition

Chapter 9. The Analysis of Three-Level Data
9.1 Three-Level Models for Hierarchical Data
9.2 Three-Level Models for Longitudinal Data

Chapter 10. Multilevel Models for Discrete Outcomes
10.1 Discrete Dependent Variables
10.2 Generalized Linear Models
10.3 Multilevel Generalized Linear Models
10.4 Additional Considerations

Appendix A. Multilevel Mediation

Appendix B. Multivariate Multilevel Models

Appendix C. Cross-Classified Models