STATISTICAL-METHODS FOR COMPARING REGRESSION-COEFFICIENTS BETWEEN MODELS
STATISTICAL-METHODS FOR COMPARING REGRESSION-COEFFICIENTS BETWEEN MODELS
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DOI：
10.1086/230638
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发表时间：
1995-03-01
影响因子：
4.4
通讯作者：
HARITOU, A
中科院分区：
文献类型：
--
作者：
CLOGG, CC;PETKOVA, E;HARITOU, A
统计方法被开发用于在其中一个模型嵌套在另一个模型中的设置中比较模型之间的回归系数。当一个给定现象的两种解释被指定为线性模型时，这种比较是有意义的。在这种情况下，研究人员应该问，当其他预测因子或协变量作为对照加入时，与给定的一组预测因子相关的系数是否会发生显著变化。基于回归分析例程提供的数量的简单计算可用于获得标准误差和所需的其他统计数据。结果也给出了广义线性模型类（例如，逻辑回归、对数线性模型等）。我们建议从根本上改变社会研究中模型比较的策略，以及对回归或回归型模型结果的呈现方式进行修改。
Statistical methods are developed for comparing regression coefficients between models in the setting where one of the models is nested in the other. Comparisons of this kind are of interest whenever two explanations of a given phenomenon are specified as linear models. In this case, researchers should ask whether the coefficients associated with a given set of predictors change in a significant way when other predictors or covariates are added as controls. Simple calculations based on quantities provided by routines for regression analysis can be used to obtain the standard errors and other statistics that are required. Results are also given for the class of generalized linear models (e.g., logistic regression, log-linear models, etc.). We recommend fundamental change in strategies for model comparisons in social research as well as modifications in the presentation of results from regression or regression-type models.