Linear mixed modelling for data from a double mixed factorial design with covariates: a case-study on semantic categorization response times

Jorge González B., Paul De Boeck and Francis Tuerlinckx

Journal of the Royal Statistical Society Series C, 2014, vol. 63, issue 2, 289-302

Abstract: type="main" xml:id="rssc12031-abs-0001">

Linear mixed modelling is a useful approach for double mixed factorial designs with covariates. It is explained how these designs are appropriate for the study of human behaviour as a function of characteristics of individuals and situations and stimuli in the situations. The behaviour of subjects nested in types of individual responding to stimuli nested in types of stimuli defines a mixed factorial design. The inclusion of additional covariates of the observational units can help to explain the behaviour under study further. A linear mixed modelling approach for such designs allows a combined focus on fixed effects (general effects) and individual and stimulus differences in these effects. This combination has the potential to advance the integration of two different subdisciplines of psychology, general psychology and differential psychology, so that they can borrow strength from each other. An application is presented with semantic categorization response time data from a factorial design with age groups by word types and with age of acquisition as an additional covariate of the words. The results throw light on the processes underlying the effect of age of acquisition and on individual differences and word differences.

Date: 2014
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