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dc.contributor.authorSpiller, Michaelen_US
dc.date.accessioned2009-08-19T16:40:58Z
dc.date.available2014-08-19T06:20:23Z
dc.date.issued2009-08-19T16:40:58Z
dc.identifier.otherbibid: 6681406
dc.identifier.urihttps://hdl.handle.net/1813/13551
dc.description.abstractRespondent-Driven Sampling (RDS) is a snowball-type sampling method used to survey hidden populations. To date, analyses of RDS data have primarily consisted of estimating population proportions and their variance because of the special complexities RDS data pose for regression analysis. This paper discusses those complications, focusing on the role of homophily (differential affiliation) in the recruitment process and respondent clustering at multiple potential levels of aggregation. It proposes two techniques for confronting these problems: entering recruiter characteristics directly into recruit-level regression models and estimating fixed- or random-effects models at the levels where significant clustering is observed. An empirical example demonstrates the modeling process, and a six-step procedure for regression modeling of RDS data is presented.en_US
dc.language.isoen_USen_US
dc.titleRegression Modeling Of Data Collected Using Respondentdriven Samplingen_US
dc.typedissertation or thesisen_US


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