Using PROC NLMIXED and PROC GLMMIX to Analyze Dyadic Data With a Dichotomous Dependent Variable

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Using PROC NLMIXED and PROC GLMMIX to Analyze Dyadic Data With a Dichotomous Dependent Variable

Source: SAS Institute

Date: January 2008

Type: White Paper

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Overview: The most commonly used statistical techniques (e.g. the general linear model) assume that the data are independent. For data that come from individuals, this often makes sense - what one subject says is often unrelated to what other subjects say. However, many data sets are hierarchical, that is, the data are nested. Two common kinds of hierarchy are temporal and spatial clustering; one commonly cited example of spatially clustered data is students in classrooms in schools; temporal clustering usually involves repeated measures on a subject.

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