Academy of Marketing Conference edition:42 location:Bournemouth date:8-10 July 2014
Although Repeated Measures ANOVA is often used to analyze experimental designs, this method does not suffice to describe all variance in a crossed effects experiment. Responses are generated from the same subjects and simultaneously those responses will be collected for the same stimuli, exposing the independence of the results. We address this methodological concern by fitting a mixed-effects model to reanalyze the outcomes of an experiment. In this experiment, a RM ANOVA was used to analyze the impact of a condition and a treatment factor on the recall of products displayed for a short time on a computer screen and where the within-subject variance was a random effect (Janssens et al., 2011). Although there was no major impact on the fixed effects, the interaction between the experimental condition and the treatment remained significant, the mixed-effects model with two random effect terms outweighs a RM ANOVA with only one random effect term for subject. It significantly reduces the overall variance and significantly improves the predictive power of the model, measured by the index of concordance. Additionally, the intra-class correlation reveals that the random effect term for the stimuli explains 49.14% of the variance compared to only 7.93% for the subjects.