Intensive Longitudinal Methods
A 2-Day Seminar Taught by Donald Hedeker, Ph.D.
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 program will be described and illustrated.
In all cases, methods will be illustrated using software, with SAS, Stata, and MixRegLS 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 program MixRegLS 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 DePaul Center, Loop Campus, 1 East Jackson Boulevard, Chicago, IL 60604.
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 includes all course materials. The early registration fee of $895.00 is available until October 10.
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 Congress Plaza Hotel, 520 South Michigan Avenue, Chicago, IL 60605. In order to make reservations, call 312-427-3800 Ext. 5025 and identify yourself as part of the Statistical Horizons group or click here for a special rate of $119. For guaranteed rate and availability, you must reserve your room no later than Tuesday, October 10.
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
RECENT Comments From Hedeker’s seminar on Multilevel Modeling of Non-Normal Data
“This class offers an in-depth discussion of the theory, fitting, and interpretation of multilevel modeling for non-normal data that does not seem to exist elsewhere. Don puts together and delivers arguably the best short course I’ve ever taken. This course is well worth the time and money.”
Amy Hughes, University of Texas, Health Science Center at Houston
“I found Don’s course very helpful for advanced as well as intermediate analysis of categorical data in clustered structures. There were many tips, tricks, nuances, and insights communicated from Don’s many years of experience with categorical data problems. I am taking away many helpful strategies for approaching my ongoing projects.”
Andrea Howard, Carleton University
“Don did a great job on the topic of multilevel modeling of non-normal data. I found it particularly useful as a refresher of what I learned in graduate school a few years ago. I really liked how he prepared all the real world examples for different models and spent a great amount of time interpreting the results. He also showed us how the results can be different when the assumptions change.”
Amy Yang, Northwestern University
“Multilevel Modeling of Non-Normal Data taught by Don Hedeker was well worth the time and cost. This course is applicable to people with intermediate to advanced knowledge. Don’s knowledge and expertise are vast but he’s able to explain concepts in layman’s term. Great course!”
Kristen Lwin, University of Toronto
“Excellent instructor, well organized materials, great examples and exercises”
Amy Watson, University of Illinois at Chicago, Jane Addams College of Social Work