Testing for Interaction in Binary Logit and Probit Models: Is a Product Term Essential?
Testing for Interaction in Binary Logit and Probit Models: Is a Product Term Essential?
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DOI：
10.1111/j.1540-5907.2009.00429.x
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发表时间：
2010-01-01
影响因子：
4.2
通讯作者：
Esarey, Justin
中科院分区：
文献类型：
--
作者：
Berry, William D.;DeMeritt, Jacqueline H. R.;Esarey, Justin
提出二元因变量（BDV）模型的政治学家经常假设变量相互作用影响事件的概率Pr（Y）。目前检验这类假设的典型方法是：（1）用乘积项估计logit或probit模型，（2）通过确定该项的系数是否具有统计学显著性来检验假设，（3）通过描述一个变量对Pr（Y）的估计影响如何随另一个变量的值变化来表征检测到的任何相互作用的性质。这种方法需要一个统计上显著的乘积项来支持交互作用假设。我们表明，一个统计上显着的产品项是既不必要也不充分的变量相互作用有意义的影响Pr（Y）。事实上，即使logit或probit模型不包含乘积项，一个变量对Pr（Y）的影响也可能与另一个变量的值密切相关。我们提出了一个策略，用于测试BDV模型中的相互作用，包括指导何时包括产品术语。
Political scientists presenting binary dependent variable (BDV) models often hypothesize that variables interact to influence the probability of an event, Pr(Y). The current typical approach to testing such hypotheses is (1) estimate a logit or probit model with a product term, (2) test the hypothesis by determining whether the coefficient for this term is statistically significant, and (3) characterize the nature of any interaction detected by describing how the estimated effect of one variable on Pr(Y) varies with the value of another. This approach makes a statistically significant product term necessary to support the interaction hypothesis. We show that a statistically significant product term is neither necessary nor sufficient for variables to interact meaningfully in influencing Pr(Y). Indeed, even when a logit or probit model contains no product term, the effect of one variable on Pr(Y) may be strongly related to the value of another. We present a strategy for testing for interaction in a BDV model, including guidance on when to include a product term.