Examples of mixed-effects modeling with crossed random effects and with binomial data
Examples of mixed-effects modeling with crossed random effects and with binomial data
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DOI：
10.1016/j.jml.2008.02.002
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发表时间：
2008-11-01
影响因子：
4.3
通讯作者：
van den Bergh, Huub
中科院分区：
文献类型：
--
作者：
Quene, Hugo;van den Bergh, Huub
心理语言学数据经常被重复测量方差分析（ANOVA），但本文认为，混合效应（多层次）模型提供了一个更好的替代方法。首先，讨论了两个随机因素（被试和项目）交叉而非嵌套的模型，并对模拟数据和真实的数据，将传统的方差分析与这些交叉混合效应模型进行了比较。结果表明，混合效应方法具有较低的机会资本化风险（I型错误）。其次，讨论了Logistic回归的混合效应模型（广义线性混合模型，GLMM），并用模拟二项数据进行了证明。混合效应模型有效地解决了“语言作为固定效应谬误”，并具有其他几个优点。总之，混合效应模型为心理语言学数据的分析提供了一种上级方法。(C)2008年爱思唯尔公司版权所有© 2016
Psycholinguistic data are often analyzed with repeated-measures analyses of variance (ANOVA), but this paper argues that mixed-effects (multilevel) models provide a better alternative method. First, models are discussed in which the two random factors of participants and items are crossed, and not nested, Traditional ANOVAs are compared against these crossed mixed-effects models, for simulated and real data. Results indicate that the mixed-effects method has a lower risk of capitalization on chance (Type I error). Second, mixed-effects models of logistic regression (generalized linear mixed models, GLMM) are discussed and demonstrated with simulated binomial data. Mixed-effects models effectively solve the "language-as-fixed-effect-fallacy", and have several other advantages. In conclusion, mixed-effects models provide a superior method for analyzing psycholinguistic data. (C) 2008 Elsevier Inc. All rights reserved,