The objective of this thesis is to examine random effect models applied to binary
data. I will use classical and Bayesian inference to fit generalized linear mixed models
to a specific data set. The data analyzed in this thesis comes from a study examining
the injection practices of needle exchange clientele in Victoria, B.C. focusing on their
risk networks. First, I will examine the application of social network analysis to the
study of injection drug use, focusing on issues of gender, norms, and the problem of
hidden populations. Next the focus will be on random effect models, where I will
provide some background and a few examples pertaining to generalized linear mixed
models (GLMMs). After GLMMs, I will discuss the nature of the injection drug use
study and the data which will then be analyzed using a GLMM. Lastly, I will provide
a discussion about my results of the GLMM analysis along with a summary of the
injection practices of the needle exchange clientele. / Graduate / 0463
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/4899 |
| Date | 03 September 2013 |
| Creators | Stone, Ryan Alexander |
| Contributors | Cowen, Laura Louise Elizabeth |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web |
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