Analyzing Reaction Times

By R. Harald Baayen, Petar Milin. 2010

1,011 citations recorded by the source corpus.

Reaction times (RTs) are an important source of information in experimental psychology. Classical methodological considerations pertaining to the statistical analysis of RT data are optimized for analyses of aggregated data, based on subject or item means (c.f., Forster & Dickinson, 1976). Mixed-effects modeling (see, e.g., Baayen, Davidson, & Bates, 2008) does not require prior aggregation and allows the researcher the more ambitious goal of predicting individual responses. Mixed-modeling calls for a reconsideration of the classical methodological strategies for analysing rts. In this study, we argue for empirical exibility with respect to the choice of transformation for the RTs. We advocate minimal a-priori data trimming, combined with model criticism. We also show how trial-to-trial, longitudinal dependencies between individual observations can be brought into the statistical model. These strategies are illustrated for a large dataset with a non-trivial random-effects structure. Special attention is paid to the evaluation of interactions involving fixed-effect factors that partition the levels sampled by random-effect factors.

Subfields: Applied Psychology, Management Science and Operations Research, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/4eb363cb4dfed1c8da470f281c761b5484678af3

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Citations in the corpus

Best practice guidance for linear mixed-effects models in psychological science (2019, neutral): “…the literature on LMMs is fairly consistent in terms of recommendations for best practice but there has been some diversity in the guidance available to researchers (e.g., compare Barr et al., 2013; Bates, Kliegl, Vasishth & Baayen, 2015; Matuschek, Kliegl, Vasishth, Baayen, & Bates, 2017).”
The cave of Shadows. Addressing the human factor with generalized additive mixed models (2015, methodology): “5 Including the previous reaction time as covariate in order to reduce the autocorrelation in the error, as suggested by Baayen and Milin (2010), has many disadvantages compared to including an ar(1) process in the errors, and is not recommended.”
Homophony and morphology: The acoustics of word-final S in English 1 (2015, methodology): “Following standard procedures (e.g. Crawley 2002, Baayen & Milin 2010), we removed outliers (defined as items with standardized residuals exceeding −2.5 or +2.5) and refitted the model (the removal of outliers resulted in the loss of 1.7% of the observations).”
Argument Structure in Usage-Based Construction Grammar: Experimental and corpus-based perspectives (2015, background): “As argued by Baayen & Milin (2010), mixed models are particularly well suited to the analysis of reaction time data, notably because variation related to random factors, such as subjects (i.e., some subjects may be faster or slower responders than others, regardless of the stimulus) or items (i.e.,…”