.png){kind=icon}
返回
Robust Methods for Moderation Analysis with a Two-Level Regression Model
.png){kind=icon}
PRE
AI
DOI：10.1080/00273171.2016.1235965.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Moderation analysis has many applications in social sciences. Most widely used estimation methods for moderation analysis assume that errors are normally distributed and homoscedastic. When these assumptions are not met, the results from a classical moderation analysis can be misleading. For more reliable moderation analysis, this article proposes two robust methods with a two-level regression model when the predictors do not contain measurement error. One method is based on maximum likelihood with Student's t distribution and the other is based on M-estimators with Huber-type weights. An algorithm for obtaining the robust estimators is developed. Consistent estimates of standard errors of the robust estimators are provided. The robust approaches are compared against normal-distribution-based maximum likelihood (NML) with respect to power and accuracy of parameter estimates through a simulation study. Results show that the robust approaches outperform NML under various distributional conditions. Application of the robust methods is illustrated through a real data example. An R program is developed and documented to facilitate the application of the robust methods.
Keyword:
Complete moderation
partial moderation
robust methods
sandwich-type standard errors
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
M
IF:
3.5
论文数:
1.8K
被引数:
9.4K
机构
引用论文
STATISTICAL DIFFICULTIES OF DETECTING INTERACTIONS AND MODERATOR EFFECTS检测相互作用和主持人效应的统计困难.png)
PSYCHOLOGICAL BULLETIN
PSYCHOLOGICAL BULLETINIF19.8
PREAI
分享收藏
Away from a Unisex Psychology: Individual Differences in Visual Sensory and Perceptual ProcessesPerception
PerceptionIF0
PREAI
分享收藏
查看更多内容