STATISTICAL-MODEL FOR ANALYSIS OF ORDINAL LEVEL DEPENDENT VARIABLES
STATISTICAL-MODEL FOR ANALYSIS OF ORDINAL LEVEL DEPENDENT VARIABLES
复制标题
DOI：
10.1080/0022250x.1975.9989847
复制
发表时间：
1975-01-01
影响因子：
1
通讯作者：
ZAVOINA, W
中科院分区：
文献类型：
--
作者：
MCKELVEY, RD;ZAVOINA, W
本文开发了一个模型，与线性模型的假设类似，当观察到的因变量是有序的。该模型是二分概率单位模型的扩展，并假设观察到的因变量的有序性是由于收集数据的方法限制，这迫使研究人员将（否则）区间水平变量的各个部分合并在一起并识别。该模型假设每个自变量的线性影响以及因变量类别之间的一系列断点。最大似然估计发现这些参数，沿着与他们的渐近抽样分布，和模拟的R2（回归分析中的决定系数）被定义为衡量拟合优度。通过对1965年医疗保险法案的国会投票的分析来说明该模型的使用。
This paper develops a model, with assumptions similar to those of the linear model, for use when the observed dependent variable is ordinal. This model is an extension of the dichotomous probit model, and assumes that the ordinal nature of the observed dependent variable is due to methodological limitations in collecting the data, which force the researcher to lump together and identify various portions of an (otherwise) interval level variable. The model assumes a linear eflect of each independent variable as well as a series of break points between categories for the dependent variable. Maximum likelihood estimators are found for these parameters, along with their asymptotic sampling distributions, and an analogue ofR2(the coefficient of determination in regression analysis) is defined to measure goodness of fit. The use of the model is illustrated with an analysis of Congressional voting on the 1965 Medicare Bill.