Abstract
Data from repeated measures experiments are usually analyzed with conventional ANOVA. Three well-known problems with ANOVA are the sphericity assumption, the design effect (sampling hierarchy), and the requirement for complete designs and data sets. This tutorial explains and demonstrates multi-level modeling (MLM) as an alternative analysis tool for repeated measures data. MLM allows us to estimate variance and covariance components explicitly. MLM does not require sphericity, it takes the sampling hierarchy into account, and it is capable of analyzing incomplete data. A fictitious data set is analyzed with MLM and ANOVA, and analysis results are compared. Moreover, existing data from a repeated measures design are re-analyzed with MLM, to demonstrate its advantages. Monte Carlo simulations suggest that MLM yields higher power than ANOVA, in particular under realistic circumstances. Although technically complex, MLM is recommended as a useful tool for analyzing repeated measures data from speech research.
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