Correlations and Nonlinear Probability Models
Correlations and Nonlinear Probability Models
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DOI：
10.1177/0049124114544224
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发表时间：
2014-11-01
影响因子：
6.3
通讯作者：
Karlson, Kristian Bernt
中科院分区：
文献类型：
--
作者：
Breen, Richard;Holm, Anders;Karlson, Kristian Bernt
虽然Logit、Probit和其他非线性概率模型(NLPM)的参数通常被解释为与潜在线性潜变量模型的回归系数有关，但我们认为，根据潜在变量模型的因变量与其预测变量之间的相关性也可以有效地解释它们。我们展示了如何从NLPM的参数中推导出这种相关性，对导出的相关性的统计意义进行了检验，并说明了它在两个应用中的有效性。在我们解释的某些情况下，导出的相关性提供了一种克服NLPM参数的交叉样本比较所固有的问题的方法。
Although the parameters of logit and probit and other nonlinear probability models (NLPMs) are often explained and interpreted in relation to the regression coefficients of an underlying linear latent variable model, we argue that they may also be usefully interpreted in terms of the correlations between the dependent variable of the latent variable model and its predictor variables. We show how this correlation can be derived from the parameters of NLPMs, develop tests for the statistical significance of the derived correlation, and illustrate its usefulness in two applications. Under certain circumstances, which we explain, the derived correlation provides a way of overcoming the problems inherent in cross-sample comparisons of the parameters of NLPMs.