多级模型
分层数据库模型
结构化
计算机科学
透视图(图形)
层级组织
线性模型
统计模型
数据科学
管理科学
数据挖掘
人工智能
机器学习
工程类
经济
管理
财务
作者
Adeniyi J. Adewale,Leslie A. Hayduk,Carole A. Estabrooks,Greta G. Cummings,William K. Midodzi,Linda Derksen
出处
期刊:Nursing Research
[Ovid Technologies (Wolters Kluwer)]
日期:2007-07-01
卷期号:56 (4): S40-S46
被引量:34
标识
DOI:10.1097/01.nnr.0000280634.71278.a0
摘要
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.
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