Introduction to Hierarchical Linear Models
Did you catch the webinar of an Introduction to Hierarchical Linear Models Using PROC MIXED? You can view it on-demand if not.
Here's an overview of what the webinar covers:
Hierarchical linear models are used to analyze hierarchical data structures where multiple micro-level units are sampled for each macro-level unit. A common example of hierarchically structured data comes from the education field where students are nested within classrooms. Another example is from medical research where patients are nested within physician. The advantages of hierarchical linear models are that they are parsimonious, they take into account the dependence in the data, they enable you to make inferences to the population of groups, they conform to the sampling design, and they enable you to examine the effects of individual-level and group-level influences simultaneously. Hierarchical linear models are fit using the MIXED procedure in SAS/STAT.
Watch the webinar to learn more!
