The Center for Integrated Latent Variable Research (CILVR) presents Modern Longitudinal Data Analysis 2019, taught by Jeffrey R. Harring.
This three-day short course is intended as both a theoretical and practical introduction to modern statistical techniques for longitudinal data analysis as it pertains to methods regularly used in educational, behavioral, and social science research. An understanding of modern longitudinal data analytic methods will be developed by relating it to participants’ existing knowledge of traditional statistical methods, particularly multiple linear regression. A participant’s experience in this workshop will be enhanced by additional prior coursework or knowledge of advanced statistical techniques such as multilevel modeling.
An outline of topics covered during the three-day short course follow sound principles of any data analytic endeavor. The course begins with discussion of longitudinal designs, data management, and exploratory data analysis. Linear mixed-effects models focusing on specifying a model for individuals, means, variances, and covariances will be presented. Maximum likelihood and Bayesian estimation approaches will be discussed as a precursor to drawing inferences through the hypothesis testing paradigm. On the third day, nonlinear longitudinal models will be presented and many extensions to basic models will be discussed.
Examples used in this short course draw primarily from social and behavioral science research, including the fields of education and psychology. Datasets will be made available to participants as well as R scripts to run the examples and annotated output. Throughout the course, participants will be able to practice exercises using R statistical software and because it is freely available, participants are encouraged to bring their own laptops with R pre-installed to perform these exercises. Participants will be instructed on how to download R prior to the course.
Participants should have a foundational knowledge up through multiple regression. Prior experience with multilevel or mixed-effects modeling is a plus, but not required. Although prior experience with R software is not required, a basic knowledge of R will enhance the participant's experience.
Models and hands-on exercises for this workshop will be done using R statistical software. Participants are welcome to bring the software loaded on their own computer, although this is not required.
Check-in: 8:30 AM
Continental Breakfast*: 8:30 AM - 9:00 AM
Morning Session: 9:00 AM - 12:15 PM
Lunch (on your own): 12:15 PM - 1:15 PM
Afternoon Session: 1:15 PM - 4:30 PM
* Participants who have special dietary needs or preferences are welcome to bring their own food as well.
Course Fees: $895 for all three days; $595 for full-time students; (free for registered HDQM Department faculty and students, although you must register; admission will depend on if space is available).
This course offers an online option as well.Register today using the Short-Course Registration button! For students not in the HDQM department, please fill out the Modern Longitudinal Data Analysis Workshop Registration Form and upload it when prompted within the online registration system: