Introduction to 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. Note that R can be downloaded for free. 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 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 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 prior 2020 online offering of Multilevel Modeling:

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

Both instructors are SO knowledgeable. I have taken courses on MLM before and you still offered a lot more. I also learned some tricks (e.g., entering the group means when centering) that were so helpful and clarified materials I have struggled with.

Dan Bauer and Patrick Curran co-teach the workshop via Zoom and will alternate lecturing throughout the day. Participants will receive complete 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).

Participants can ask text-based questions using a Zoom function that will be monitored by CBA staff and conveyed to the instructor. If a question cannot be answered during lecture, a text response will be provided at a later time.

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. and finish at approximately 3:30 p.m. Eastern Time (US) each day. Computer demonstrations will be provided from approximately 3:30 p.m. to 5:00 p.m. Eastern Time (US), with separate breakout meetings for SAS, SPSS, R, and Stata.

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 livestream 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 6 months following the completion of the workshop. The recordings cannot be saved by the participant and will not be available after the 6 month 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 6 months 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
2.4 Model Estimation

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 Frequently Asked Questions

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

Chapter 5: The Analysis of Repeated Measures
5.1 Growth Curves Within the Multilevel Framework
5.2 Alternative Metrics of Time & Level‐1 Error Structures
5.3 Modeling Non‐Linear Change
5.4 Time‐Invariant and Time-Varying Predictors