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首页> 外文期刊>Educational and Psychological Measurement >Loss of power in logistic, ordinal logistic, and probit regression when an outcome variable is coarsely categorized
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Loss of power in logistic, ordinal logistic, and probit regression when an outcome variable is coarsely categorized

机译：当对结果变量进行粗略分类时，逻辑，序数逻辑和概率回归中的动力丧失

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摘要

Variables that have been coarsely categorized into a small number of ordered categories are often modeled as outcome variables in psychological research. The authors employ a Monte Carlo study to investigate the effects of this coarse categorization of dependent variables on power to detect true effects using three classes of regression models: ordinary least squares (OLS) regression, ordinal logistic regression, and ordinal probit regression. Both the loss of power and the increase in required sample size to regain the lost power are estimated. The loss of power and required sample size increase were substantial under conditions in which the coarsely categorized variable is highly skewed, has few categories (e.g., 2, 3), or both. Ordinal logistic and ordinal probit regression protect marginally better against power loss than does OLS regression.
机译：在心理学研究中，通常将已粗略分类为少量有序类别的变量建模为结果变量。作者采用蒙特卡洛研究，使用三类回归模型来研究因变量的这种粗分类对检测真实效果的功效的影响：普通最小二乘（OLS）回归，有序逻辑回归和有序概率回归。功率损耗和重新获得功率损耗所需的样本量都会增加。在粗略分类的变量高度偏斜，类别很少（例如2、3）或两者兼有的条件下，功率损失和所需的样本量增加是相当大的。与OLS回归相比，序数逻辑和序数概率回归可以更好地防止功耗。

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