Analyzing Reaction Times. Baayen, R., H. and Milin, P.
Analyzing Reaction Times [pdf]Paper  abstract   bibtex   
Reaction times (rts) are an important source of information in experimental psychology. Classical methodological considerations pertaining to the sta-tistical 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 re-quire 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 flexibility with respect to the choice of transforma-tion for the rts. We advocate minimal a-priori data trimming, combined with model criticism. We also show how trial-to-trial, longitudinal depen-dencies 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 in-teractions involving fixed-effect factors that partition the levels sampled by random-effect factors.
@article{
 title = {Analyzing Reaction Times},
 type = {article},
 keywords = {distributions,linear mixed-effects modeling,outliers,reaction times,tempo-ral dependencies,transformations},
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 last_modified = {2017-09-01T15:53:49.633Z},
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 abstract = {Reaction times (rts) are an important source of information in experimental psychology. Classical methodological considerations pertaining to the sta-tistical 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 re-quire 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 flexibility with respect to the choice of transforma-tion for the rts. We advocate minimal a-priori data trimming, combined with model criticism. We also show how trial-to-trial, longitudinal depen-dencies 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 in-teractions involving fixed-effect factors that partition the levels sampled by random-effect factors.},
 bibtype = {article},
 author = {Baayen, R Harald and Milin, Petar}
}
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