.png){kind=icon}
返回
Understanding Mixed-Effects Models Through Data Simulation
.png){kind=icon}
OA
AI
DOI：10.1177/2515245920965119.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Experimental designs that sample both subjects and stimuli from a larger population need to account for random effects of both subjects and stimuli using mixed-effects models. However, much of this research is analyzed using analysis of variance on aggregated responses because researchers are not confident specifying and interpreting mixed-effects models. This Tutorial explains how to simulate data with random-effects structure and analyze the data using linear mixed-effects regression (with the lme4 R package), with a focus on interpreting the output in light of the simulated parameters. Data simulation not only can enhance understanding of how these models work, but also enables researchers to perform power calculations for complex designs. All materials associated with this article can be accessed at https://osf.io/3cz2e/.
Keyword:
simulation
mixed-effects models
power
lme4
R
open materials
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
A
IF:
13.4
论文数:
322
被引数:
3.7K
机构
引用论文
.png){kind=icon}
Statistical Power and Optimal Design in Experiments in Which Samples of Participants Respond to Samples of Stimuli参与者样本对刺激样本做出反应的实验中的统计能力和最佳设计
PREAI
分享收藏Failure analysis of composite laminates subjected to hydrostatic stresses: A multicontinuum approach
PREAI
分享收藏NESmapper: Accurate Prediction of Leucine-Rich Nuclear Export Signals Using Activity-Based ProfilesOAAI
OAAI分享收藏
查看更多内容