集合(抽象数据类型)
会话(web分析)
事件(粒子物理)
计算机科学
多级模型
事件相关电位
数据集
过程(计算)
数据科学
心理学
认知心理学
数据挖掘
机器学习
人工智能
脑电图
神经科学
物理
量子力学
万维网
程序设计语言
操作系统
作者
Hannah I. Volpert‐Esmond,Elizabeth Page‐Gould,Bruce D. Bartholow
标识
DOI:10.1016/j.ijpsycho.2021.02.006
摘要
Multilevel modeling (MLM) is becoming increasingly accessible and popular in the analysis of event-related potentials (ERPs). In this article, we review the benefits of MLM for analyzing psychophysiological data, which often contains repeated observations within participants, and introduce some of the decision-making points in the analytic process, including how to set up the data set, specify the model, conduct hypothesis tests, and visualize the model estimates. We highlight how the use of MLM can extend the types of theoretical questions that can be answered using ERPs, including investigations of how ERPs vary meaningfully across trials within a testing session. We also address reporting practices and provide tools to calculate effect sizes and simulate power curves. Ultimately, we hope this review contributes to emerging best practices for the use of MLM with psychophysiological data.
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