@article{
 title = {Understanding hierarchical linear models: applications in nursing research},
 type = {article},
 year = {2007},
 identifiers = {[object Object]},
 pages = {S40-6},
 volume = {56},
 websites = {http://dx.doi.org/10.1097/01.NNR.0000280634.71278.a0},
 id = {95a6d136-7fe4-36e5-96d4-ef4b2fd796a5},
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 accessed = {2014-01-29},
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 profile_id = {369acd69-1fe7-313d-821e-cb7bbe1ddab2},
 last_modified = {2017-03-25T14:39:59.517Z},
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 abstract = {Nurses practice within hierarchical organizations and occupational structures. Hence, data emanating from nursing environments are structured, often inherently, hierarchically. From the perspective of ordinary regression, such structuring constitutes a statistical problem because this violates the assumption that we have observed independent and identical cases. A preferable approach is to employ analytical methods that mesh with the kinds of natural aggregations present in nursing environments. Consequently, there has been increasing interest in applying hierarchical, or multilevel, linear models to nursing contexts because this powerful analytical tool recognizes and accommodates naturally hierarchical data structures. The purpose of this article is to foster an understanding of both the strengths and limitations of hierarchical models. A hypothetical nursing example is progressively extended from the most basic hierarchical linear model toward a full two-level model. The structural similarities between two-level and three-level models are pointed out while focusing on the hierarchical nature of models rather than statistical technicalities. The limitations of hierarchical models are discussed also.},
 bibtype = {article},
 author = {Adewale, Adeniyi J and Hayduk, Leslie A and Estabrooks, Carole A and Cummings, Greta G and Midodzi, William K and Derksen, Linda},
 journal = {Nursing Research},
 number = {4 Suppl}
}

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