In this week’s Brief, we look at hierarchical and mixed models. Our course spotlight is
- April 10 – May 8: Generalized Linear Models
- April 24 – May 22: Mixed and Hierarchical Linear Models
See you in class!
Mixed Model – When to Use?
In 1861, the British Royal Astronomer George Airy gathered a set of telescopic observations on multiple nights, with multiple observations each night. This is considered by some to be the first antecedent of a key analytical tool in most research […]
Word of the Week
Factor
The term “factor” has different meanings in statistics that can be confusing because they conflict. In statistical programming languages like R, factor acts as an […]
Problem of the Week
Notify or Don’t Notify?
Our problem of the week is an ethical dilemma, posed by the New England Journal of Medicine to its readers 10 days ago. Volunteers contributed DNA samples to investigators building a genetic database for study, on condition the data would be deidentified and kept confidential and that they themselves would not learn results. Should participants at significant genetic risk for cancer now be notified? […]
Course Spotlight
Our course spotlight is on two courses that deal with linear models:
April 10 – May 8: Generalized Linear Models (GLM)
The GLM course covers the derivation of the generalized linear model, starts off with the familiar case of linear regression with continuous responses (outcomes), and discusses how to handle over-dispersion in the data. In addition, you will learn how to
- Fit binomial models like logit and probit
- Fit count models like Poisson and negative binomial
- Use appropriate metrics to assess model fit
April 24 – May 22: Mixed and Hierarchical Linear Models (HLM)
The HLM course extends the linear modeling structure to deal with the complexities introduced when data are clustered, repeated at the same time (repeated measures), or repeated sequentially over time (longitudinal). You will learn how to
- Specify a linear mixed model
- Identify the associated marginal model
- Estimate the covariance and fixed effect parameters
- Conduct hypothesis tests on models
Your instructor in both courses is James Hardin, Associate Dean of Faculty Affairs and Curriculum Professor at the University of South Carolina, and co-author (with Joseph Hilbe) of Generalized Estimating Equations.
See you in class!
