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.
Financed by the National Centre for Research and Development under grant No. SP/I/1/77065/10 by the strategic scientific research and experimental development program:
SYNAT - “Interdisciplinary System for Interactive Scientific and Scientific-Technical Information”.