Adewale Adeniyi J, Hayduk Leslie, Estabrooks Carole A, Cummings Greta G, Midodzi William K, Derksen Linda
Department of Public Health Sciences, University of Alberta, Edmonton, Canada.
Nurs Res. 2007 Jul-Aug;56(4 Suppl):S40-6. doi: 10.1097/01.NNR.0000280634.71278.a0.
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.
护士在层级分明的组织和职业结构中开展工作。因此，源自护理环境的数据通常在本质上具有层级结构。从普通回归的角度来看，这种结构构成了一个统计问题，因为这违反了我们所观察到的是独立且相同案例的假设。一种更可取的方法是采用与护理环境中存在的自然聚合类型相契合的分析方法。因此，将分层或多级线性模型应用于护理情境的兴趣与日俱增，因为这种强大的分析工具能够识别并适应自然的层级数据结构。本文的目的是增进对分层模型的优势和局限性的理解。一个假设的护理示例从最基本的分层线性模型逐步扩展到完整的两级模型。在关注模型的层级性质而非统计技术细节的同时，指出了两级模型和三级模型之间的结构相似性。还讨论了分层模型的局限性。
