2008
DOI: 10.1016/j.jml.2007.12.005
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Mixed-effects modeling with crossed random effects for subjects and items

Abstract: This paper provides an introduction to mixed-effects models for the analysis of repeated measurement data with subjects and items as crossed random effects. A worked-out example of how to use recent software for mixed-effects modeling is provided. Simulation studies illustrate the advantages offered by mixed-effects analyses compared to traditional analyses based on quasi-F tests, by-subjects analyses, combined by-subjects and by-items analyses, and random regression. Applications and possibilities across a ra… Show more

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Cited by 6,934 publications
(5,985 citation statements)
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References 46 publications
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“…This amounts to 1,508 trials (14.5%) removed in total. RTs were analyzed with linear mixed effects models (Baayen, Davidson, & Bates, 2008) implemented in RStudio (Version 0.98.945, 2009(Version 0.98.945, -2013; RStudio, Inc.) using the lme4 package (Bates, Maechler, Bolker, & Walker, 2014). The full model contained word type and visual condition as well as the interaction between the two as fixed effects and by-participants random slopes and intercepts for both fixed effects and by-items slopes and intercepts for the effect of visual condition.…”
Section: Methodsmentioning
confidence: 99%
“…This amounts to 1,508 trials (14.5%) removed in total. RTs were analyzed with linear mixed effects models (Baayen, Davidson, & Bates, 2008) implemented in RStudio (Version 0.98.945, 2009(Version 0.98.945, -2013; RStudio, Inc.) using the lme4 package (Bates, Maechler, Bolker, & Walker, 2014). The full model contained word type and visual condition as well as the interaction between the two as fixed effects and by-participants random slopes and intercepts for both fixed effects and by-items slopes and intercepts for the effect of visual condition.…”
Section: Methodsmentioning
confidence: 99%
“…This approach to analysis allows for greater confidence that significant results will generalize beyond the specific materials used and participants recruited (Baayen, Davidson & Bates, 2008;Gelman & Hill, 2007;Jaeger, 2008). This statistical approach was implemented using the lme4 package in R (Bates, Maechler, Bolker & Walker 2015).…”
Section: Data Analysis Approachmentioning
confidence: 99%
“…The measures were response time, the distance travelled by the mouse cursor (scaled so that a straight line from the start point to the response corresponds to 1 unit), the number of times the cursor was moved during a trial (with movements defined as windows of 100 msec or more in motion, separated by 100 msec or more not moving), and the closest proximity achieved between the cursor and the non-chosen option (closest proximity to the heuristic response option on trials where the correct option was chosen, and vice versa). These measures were compared using linear mixed models, with crossed random intercepts for each participant, and each problem (see Baayen, Davidson, & Bates, 2008). Response latencies, and the distance travelled by the mouse cursor were logtransformed to normalize their distributions, and a generalized mixed model with a Poisson log link was used to model the number of movements.…”
Section: By-trial Analysismentioning
confidence: 99%