Analyzing 'visual world' eyetracking data using multilevel logistic regression
Analyzing 'visual world' eyetracking data using multilevel logistic regression
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DOI：
10.1016/j.jml.2007.09.002
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发表时间：
2008-11-01
影响因子：
4.3
通讯作者：
Barr, Dale J.
中科院分区：
文献类型：
--
作者：
Barr, Dale J.
提供了一个新的框架，该框架使用多级逻辑回归（MLR）来分析心理学研究中使用的“视觉世界”眼球追踪实验的数据。 MLR框架克服了常规分析中的一些问题，使得可以将时间纳入连续变量和凝视位置作为分类因变量。多级方法最小化数据聚合的需求，因此提供了一种更统计的强大方法。使用MLR，研究人员建立了整体响应曲线的数学模型，将响应分为不同的时间成分。研究人员可以通过检查自变量及其对这些组件的相互作用的影响来检验假设。提供了一个使用MLR的示例。 （c）2007 Elsevier Inc.保留所有权利。
A new framework is offered that uses multilevel logistic regression (MLR) to analyze data from 'visual world' eye-tracking experiments used in psycholinguistic research. The MLR framework overcomes some of the problems with conventional analyses, making it possible to incorporate time as a continuous variable and gaze location as a categorical dependent variable. The multilevel approach minimizes the need for data aggregation and thus provides a more statistically powerful approach. With MLR, the researcher builds a mathematical model of the overall response curve that separates the response into different temporal components. The researcher can test hypotheses by examining the impact of independent variables and their interactions on these components. A worked example using MLR is provided. (C) 2007 Elsevier Inc. All rights reserved.