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dc.creatorWilk, Katarzyna
dc.date.accessioned2015-06-01T19:56:32Z
dc.date.available2015-06-01T19:56:32Z
dc.date.issued2007
dc.identifier.citationAsk: Research and Methods. Volume 16, Issue 1 (2007), pp. 55-66en_US
dc.identifier.issn1234-9224
dc.identifier.urihttp://hdl.handle.net/1811/69551
dc.description.abstractEmpirical analyses of hierarchical data are important in various disciplines, but are most common to the social sciences. Until the 1980’s, when the method of multilevel modeling was introduced, researchers dealt with the problem of nested data in a variety of ways, none of which was completely effective or accurate. The method of hierarchical modeling, and softwares such as HLM or MLwiN, provide the most appropriate available tools for dealing with the nested data. This article intends to introduce this strategy, as well as provide an empirical example to illustrate the relative advantages of using it to perform analysis.en_US
dc.language.isoenen_US
dc.publisherIFiS Publishersen_US
dc.rightsThis item may be protected by copyright, and is made available here for research and educational purposes. The user is responsible for making a final determination of copyright status. If copyright protection applies, permission must be obtained from the copyright holder to reuse, publish, or reproduce the object beyond the bounds of Fair Use or other exemptions to the law. We are eager to hear from any copyright holders who are not properly identified.en_US
dc.subjecthierarchical modelingen_US
dc.subjecthierarchical modeling softwareen_US
dc.subjectmultilevel dataen_US
dc.subjectnested dataen_US
dc.subjectnon-independent units of observationen_US
dc.titleHierarchical Modeling: Properties and Applicationen_US
dc.typeArticleen_US


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