Intensive Longitudinal Methods Fall 2017

Event Phone: 1-610-715-0115

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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.

Venue:  

Venue Phone: 1-312-362-8000

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Address:
1 East Jackson Boulevard, Chicago, Illinois, 60604, United States