USING MULTILEVEL MODELLING IN THE ANALYSIS OF EXPERIMENTAL DATA: CUMULATIVE EFFECTS OF STRUCTURAL PRIMING IN CHILDREN

Multilevel modelling is a flexible alternative to the traditional factorial ANOVA approach in the analysis of experimental data with repeated measures. This article describes a psycholinguistic experiment and provides a detailed account of the data analysis, demonstrating the use of multilevel models to include a continuous predictor and complex assumptions about error variance. The experiment investigated the effects of structural priming on reaction times in a word monitoring task. Pairs of sentences with identical or different syntactic structures were presented to 4- and 5-year-old children, whose task was to respond to a word presented in the second sentence. Multilevel modelling analysis revealed an interaction between the experimental condition and position of the trial within the experiment: the reaction times in the same-structure condition decreased over the course of the experiment, while they increased in the different-structure condition. The analysis demonstrates how can be used multilevel models to detect change in responses over the course of an experimental session.