Diagnosing and dealing with multicollinearity.
Diagnosing and dealing with multicollinearity.
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DOI：
10.1177/019394599001200204
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发表时间：
1990-04-01
影响因子：
1.8
通讯作者：
Schroeder, M A
中科院分区：
文献类型：
--
作者：
Schroeder, M A
本文的目的是提高护理研究者对共线数据在发展护理实践理论模型中的作用的认识。共线数据扭曲了由普通最小二乘分析产生的估计值的真实值。然而，为提供护理实践基础而开发的理论模型不需要被放弃，因为它们无法在重复应用中产生一致的估计。认识到多重共线性是一个数据问题，而不是与理论模型的错误说明有关的问题，这一点也很重要。研究者必须首先意识到这个问题，然后才有可能根据多重共线性的程度、理论考虑以及与可选的、有偏差的、最小二乘回归技术相关的误差来源，制定一个有根据的解决方案。基于理论和统计考虑的决策将进一步发展基于理论的护理实践。
The purpose of this article was to increase nurse researchers' awareness of the effects of collinear data in developing theoretical models for nursing practice. Collinear data distort the true value of the estimates generated from ordinary least-squares analysis. Theoretical models developed to provide the underpinnings of nursing practice need not be abandoned, however, because they fail to produce consistent estimates over repeated applications. It is also important to realize that multicollinearity is a data problem, not a problem associated with misspecification of a theorectical model. An investigator must first be aware of the problem, and then it is possible to develop an educated solution based on the degree of multicollinearity, theoretical considerations, and sources of error associated with alternative, biased, least-square regression techniques. Decisions based on theoretical and statistical considerations will further the development of theory-based nursing practice.