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In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 4-Day Livestream 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 the extended uses of these models 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.

In the seminar, you will learn to:

• Apply and interpret the results of 2- and 3-level mixed (i.e., multilevel) models with observations nested within days and days within subjects, or observations within waves and waves within subjects.
• Estimate descriptive statistics for time-varying variables in situations where the number of observations per subject can be quite varied across subjects.
• Include occasion-varying covariates in your analyses, and distinguish the within-subjects (WS) and between-subjects (BS) effects of such covariates.
• Model day of week and time of day effects.
• Model random subject intercept and slope heterogeneity in terms of covariates to learn what explains such heterogeneity.
• Extend your modeling of the mean response, by also modeling WS and BS variances in terms of covariates. This extended approach uses mixed location-scale (MELS) models that allow subject heterogeneity in both a subject’s mean and variance.
• Apply these methods using SAS, Stata, and the freeware MixRegLS & MixWILD computer software programs.