Introduction to Multilevel Modeling

December 2-4, 2020
Online Webinar via Zoom
Instructors: Dan Bauer and Patrick Curran
Software Demonstrations: R, SAS, SPSS, and Stata

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Course Description

Introduction to Multilevel Modeling is a three-day workshop focused on the application and interpretation of multilevel models, also known as hierarchical linear models and mixed models, for the analysis of nested data structures. Nesting can arise from hierarchical data structures (e.g., siblings nested within family; patients nested within therapist), longitudinal data structures (repeated measures nested within individual), or both (repeated measures nested within patient and patient nested within therapist). It is well known that the analysis of nested data structures using traditional general linear models (e.g., ANOVA or regression) is flawed, oftentimes substantially so. Not only are tests of significance likely biased, but many important within-group and between-group relations cannot be evaluated. All of these limitations can be addressed within the multilevel model. In this workshop we provide a comprehensive exploration of multilevel models, including estimation of main effects and interactions, decomposing within- and between-group effects, and the analysis of repeated measures data.

Course Schedule

Start date: December 02, 2020

End date: December 04, 2020

Start time: 09:00 a.m. EDT

End time: 05:00 p.m. EDT

Venue: Zoom Webinar

Email: info@curranbauer.org

Instructors

Dan BauerDaniel J. Bauer, Ph.D.

Dan Bauer is a Professor and the Director of the L.L. Thurstone Psychometric Laboratory in the Department of Psychology and Neuroscience at the University of North Carolina. He teaches primarily graduate-level courses in statistical methods, for which he has won teaching awards from the University of North Carolina and from the American Psychological Association.  Endeavoring to make advanced statistical techniques more accessible, Dan has spent the last 15 years developing and teaching workshops on a variety of topics in both the United States and abroad, including multilevel modeling, mixture modeling, longitudinal data analysis, structural equation modeling, latent curve analysis, missing data analysis, measurement, and integrative data analysis. His research interests lie at the intersection of quantitative and developmental psychology, particularly the development of problem and health-related behaviors over childhood and adolescence. He has published over 65 scientific papers, served as Associate Editor for Psychological Methods, currently serves on the editorial boards of several journals, and has reviewed grants for the National Science Foundation, National Institutes of Health, and the Institute of Educational Sciences. He received an early career award from the American Psychological Association in 2009. For more details, see his academic web page.

Patrick CurranPatrick J. Curran, Ph.D.

Patrick Curran is a Professor in the L.L. Thurstone Psychometric Laboratory in the Department of Psychology and Neuroscience at the University of North Carolina at Chapel Hill. Patrick has dedicated much of his career to the teaching and dissemination of advanced quantitative methods and has won teaching awards from UNC and from the American Psychological Association. Over the past 20 years Patrick has taught over 50 national and international workshops on structural equation modeling, multilevel modeling, latent curve analysis, longitudinal data analysis, and general linear modeling. He draws on experiences from his own program of research on high risk child development to guide and inform his quantitative teaching. Patrick’s program of research is primarily focused on the development and evaluation of statistical models of change over time, particularly as applied to studies of adolescent substance use. He has published over 70 scientific papers and chapters and has co-authored a text book on latent curve modeling with Ken Bollen. Patrick has served as Associate Editor for Psychological Methods and currently serves on the editorial boards of seven scientific journals. For more details, see his academic web page.

Course Details

Who Should Attend and Software Considerations

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.

The Goals of the Workshop

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.

Reviews from Past Participants

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!

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.

I appreciate that both Dan and Patrick explained everything in a very understandable way, regardless of your statistical knowledge and background. The examples provided throughout the lectures were extremely helpful too.

The instructors are highly skilled in clearly conveying complex concepts in very accessible manner. Coverage of the material on the topic was extremely thorough and useful. The SAS afternoon sessions were fantastic.

Clarity! Excellent instructors were able to scaffold the material in a helpful, comprehensible manner. I was very impressed with how well everything worked via Zoom, particularly since this was the first entirely-online season.

Such an excellent training opportunity! After taking this course, I feel much more confident in my ability to develop models for my dissertation.

The ability of both presenters to communicate important statistical and research concepts in clear, understandable language. After enduring a number of undergraduate and graduate level statistics courses this one was the first that felt accessible.

Really, really helpful!! I am a graduate student and managed to convince my department to let me use my travel funds this year to enroll in this class. The lectures and demos are so refreshingly clear, I would have paid out of pocket!

This was a fantastic workshop. I am a second-year graduate student and have only taken one semester of statistics so far, and found that Patrick and Dan presented this challenging material in an accessible, fun and engaging way.

The applied, practical focus and many real-world examples were key strengths. Also, the notes for the syntax were phenomenal! I really appreciate the time taken to annotate the code so well.

This training was the best thing to happen to me during my dissertation - a life saver! I am infinitely grateful.

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.

What is Provided

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).

Interactions with Instructors

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.

Connectivity Requirements

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/

Daily Schedule

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.

Availability of Recordings

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 14 days following the completion of the workshop. The recordings cannot be saved by the participant and will not be available after the 14-day period.

Tuition Reduction Opportunities

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.

Support for Junior Scholars from Under-Represented Groups

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.

Cancellation Policy

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.

Syllabus

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

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