Intensive Longitudinal Methods
A 2-Day Seminar Taught by Donald Hedeker, Ph.D.
To see a sample of the course materials, click here.
Innovative methods of data collection often produce large numbers of repeated measurements for each individual. Variously known as ecological momentary assessments (EMA), experience sampling method (ESM), and daily diary (DD), these methods have been developed to record the momentary events and experiences of subjects in daily life. They usually involve self-reports from individuals, dyads, families, or other small groups over the course of hours, days, and weeks. Data produced by these methods are commonly referred to as intensive longitudinal data.
Although there is much to be learned from such data, conventional methods of analysis are often unsuited to the task. In this seminar you will learn how to analyze intensive longitudinal data by way of mixed models, also known as multilevel or hierarchical linear models. The course begins with the basic 2- and 3-level model, and then proceeds to more extended uses of these models.
One of those extensions is to model the variances. In the standard mixed model, the error variance and the variance of the random effects are assumed to be constant across individuals. When there are many observations per individual, it becomes practical to allow those variances to vary randomly across individuals, as well as to depend on other covariates including time itself. Besides making the models more realistic, additional substantive insights can be gleaned by modeling both means and variances.
Here are some of the other topics covered in the seminar:
- Random intercepts 2- and 3-level mixed models with observations nested within days and days within subjects, or observations within waves and waves within subjects.
- Estimating descriptive statistics for time-varying variables in situations where the number of observations per subject can be quite varied across subjects.
- The treatment of occasion-varying covariates, and the decomposition of the within-subjects (WS) and between-subjects (BS) effects of such covariates.
- Modeling random subject intercept and slope heterogeneity in terms of covariates.
- Modeling WS and BS variance in terms of covariates using mixed location-scale models that allow subject heterogeneity in both a subject’s mean and variance.
- Modeling ordinal outcomes using an extension of the mixed location-scale model.
- Item Response Theory (IRT) models for the timing of event reports.
Computer application using SAS, Stata, and the freeware MixRegLS & MixWILD programs will be described and illustrated.
In all cases, methods will be illustrated using software, with SAS, Stata, and MixRegLS & MixWILD examples and syntax. Some familiarity with reading in data and performing basic statistical analyses in either SAS or Stata is recommended.
This is a hands-on course with at least one hour each day devoted to carefully structured and supervised assignments. To do the exercises, you will need to bring your own laptop computer with a recent version of SAS or Stata and the free programs MixRegLS & MixWILD installed.
Who should attend
If you plan on analyzing EMA or other forms of intensive longitudinal data, this course is for you. Participants should be thoroughly familiar with multiple linear regression, and some knowledge of mixed models (i.e. multilevel/HLM) is helpful, but not assumed.
LOCATION, Format, and MATERIALS
The class will meet from 9 am to 5 pm each day with a 1-hour lunch break at Temple University Center City, 1515 Market Street, Philadelphia, PA 19103.
Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking.
Registration and lodging
The fee of $995.00 includes all seminar materials.
If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).
Lodging Reservation Instructions
A block of guest rooms has been reserved at the Club Quarters Hotel, 1628 Chestnut Street, Philadelphia, PA at a special rate of $177 per night. This location is about a 5-minute walk to the seminar location. In order to make reservations, call 203-905-2100 during business hours and identify yourself by using group code SH1114 or click here. For guaranteed rate and availability, you must reserve your room no later than Monday, October 14, 2019.
If you need to make reservations after the cut-off date, you may call Club Quarters directly and ask for the “Statistical Horizons” rate (do not use the code or mention a room block) and they will try to accommodate your request.
Hedeker D, Mermelstein RJ, & Flay BR (2006). Application of item response theory models for intensive longitudinal data. In TA Walls & JL Schafer (Eds.), Models for Intensive Longitudinal Data (pp. 84-108). Oxford University Press, New York. pdf file supplemental materials
Hedeker, D. & Mermelstein, R.J. (2007). Mixed-effects regression models with heterogeneous variance: Analyzing ecological momentary assessment data of smoking. In T.D. Little, J.A. Bovaird, & N.A. Card (Eds.), Modeling Contextual Effects in Longitudinal Studies. Erlbaum: Mahwah, NJ. pdf file SAS code
Hedeker, D., Mermelstein, R.J., & Demirtas, H. (2008). An application of a mixed-effects location scale model for analysis of Ecological Momentary Assessment (EMA) data. Biometrics, 64, 627-634. pdf file supplemental materials
Hedeker, D., Mermelstein, R.J., Berbaum, M.L., & Campbell, R.T. (2009). Modeling mood variation associated with smoking: An application of a heterogeneous mixed-effects model for analysis of Ecological Momentary Assessment (EMA) data. Addiction, 104, 297-307. pdf file supplemental materials
Hedeker, D., Demirtas, H., & Mermelstein, R.J. (2009). A mixed ordinal location scale model for analysis of Ecological Momentary Assessment (EMA) data. Statistics and its Interface, 2, 391-401. pdf file
Hedeker, D., Mermelstein, R.J., & Demirtas, H. (2012). Modeling between- and within-subject variance in Ecological Momentary Assessment (EMA) data using mixed-effects location scale models. Statistics in Medicine. pdf file
“Dr. Hedeker is an exceptional teacher. This course covers how to handle EMA and other dense longitudinal data sets. He gives instruction on how to implement the different models in SAS, Stata, and MixWILD (a free program). He was able to teach the class and answer questions from students of varied backgrounds, from statisticians to clinicians. As wearables and data collection through mobile devices gets more common, this course gives you the tools to handle the data sets correctly.”
Elizabeth Avery-Mamer, Rush University Medical Center
“This course builds upon the traditional content taught in graduate classrooms. It allows you to think deeper and consider different approaches in answering interesting questions in EMA data. The interaction with Dr. Hedeker is fantastic. He has the ability to transfer complex statistical concepts to the level of the audience.”
Knar Sagherian, University of Tennessee, Knoxville
“This course was extremely valuable for learning to analyze EMA/ESM data. I learned how to do techniques that I’ve read about but have been unable to do on my own. Dr. Hedeker is an excellent teacher who takes the time to answer questions and help everyone fully understand the material. Strongly recommend this course!”
Margaret Kerr, University of Wisconsin, Madison
“I really enjoyed this course. The content and instructor were great. It answered several analytic questions I had and inspired me to ask some new ones!”
Amanda Roy, University of Illinois at Chicago
“The instructor for this course was excellent in transmitting knowledge (statistical concepts) across the board. Every query was very adequately answered and explained. For those who are in need of using EMA or traditional longitudinal models, this is a very enlightening course.”
“The presenter was very engaging and used interesting examples to highlight statistical techniques that are quite dense. The ability to practice implementing the techniques on our own computers also was very important and useful. Would recommend!”
Jon Stange, University of Illinois at Chicago
“Don was an incredible instructor. He made the course accessible and interesting to all participants, and I feel like I’m walking away with a clear and usable skillset.”
Stephanie Kerrigan, Rush University Medical Center
“This course was a very helpful intro to dealing with EMA data. It included coding and interpretations for the results which was super helpful.”
“This class was extremely informative and helpful in learning advanced statistical techniques for modeling EMA data. Don is an amazing instructor and very knowledgeable on the topic. Enjoyed the live demonstrations and examples that were provided using multiple statistical packages (i.e. R, Stata, SAS, etc.). I would definitely recommend the course!”
Katie L. Burkhouse, University of Illinois at Chicago
“If you have a research project in mind using EMA data, this course is invaluable to help you come up with new possible ways of looking at it and giving you the tools to carry it out.”
Sandahl Nelson, University of California, San Diego