Kwak Chanyeong, Clayton-Matthews Alan
College of Nursing, University of Rhode Island, 2 Heathman Road, Kingston, RI 02881-2021, USA.
Nurs Res. 2002 Nov-Dec;51(6):404-10. doi: 10.1097/00006199-200211000-00009.
When the dependent variable consists of several categories that are not ordinal (i.e., they have no natural ordering), the ordinary least square estimator cannot be used. Instead, a maximum likelihood estimator like multinomial logit or probit should be used.
The purpose of this article is to understand the multinomial logit model (MLM) that uses maximum likelihood estimator and its application in nursing research.
The research on "Racial differences in use of long-term care received by the elderly" (Kwak, 2001) is used to illustrate the multinomial logit model approach. This method assumes that the data satisfy a critical assumption called the "independence of irrelevant alternatives." A diagnostic developed by Hausman is used to test the independence of irrelevant alternatives assumption. Models in which the dependent variable consists of several unordered categories can be estimated with the multinomial logit model, and these models can be easily interpreted.
This method can handle situations with several categories. There is no need to limit the analysis to pairs of categories, or to collapse the categories into two mutually exclusive groups so that the (more familiar) logit model can be used. Indeed, any strategy that eliminates observations or combines categories only leads to less efficient estimates.
当因变量由几个非有序类别（即它们没有自然顺序）组成时，不能使用普通最小二乘估计器。相反，应使用多项逻辑回归或概率单位等最大似然估计器。
本文旨在了解使用最大似然估计器的多项逻辑回归模型（MLM）及其在护理研究中的应用。
以“老年人接受长期护理的种族差异”（Kwak，2001年）的研究为例来说明多项逻辑回归模型方法。该方法假定数据满足一个称为“无关选项独立性”的关键假设。使用豪斯曼开发的一种诊断方法来检验无关选项独立性假设。因变量由几个无序类别组成的模型可以用多项逻辑回归模型进行估计，并且这些模型易于解释。
该方法可以处理有多个类别的情况。无需将分析局限于类别对，或将类别合并为两个相互排斥的组以便使用（更熟悉的）逻辑回归模型。实际上，任何消除观测值或合并类别的策略只会导致估计效率降低。
