Use of univariate repeated-measures ANOVA is commonplace in ERP research, but has several shortcomings limiting its applicability (see Vasey & Thayer, 1987). Psychophysiological data frequently violates the assumption of sphericity (i.e., that the variances of differences between factor levels are equal; Jennings, Wood, & Lawrence, 1976), and corrections (e.g., Greenhouse-Geisser or Huynh-Feldt p-value adjustments) result in loss of statistical power. Inter-individual variability in both baseline and stimulus-elicited EEG activity often is greater than variability attributable to manipulated factors of interest (see Gratton, 2000), contributing to inflated error variance estimates in ANOVA that also reduce power. Scholars have therefore advised the use of multivariate approaches for psychophysiological data (see Gratton, 2000; Vasey & Thayer, 1987), such as multilevel modeling. Advantages of multilevel modeling include relaxed assumptions concerning sphericity, the ability to simultaneously estimate both within-participant and between-participants effects (see Bryk & Raudenbush, 1992), and the ability to specify separate error terms at each level of nesting. Multilevel modeling is robust to missing observations, whereas repeated-measures ANOVA requires that missing values be interpolated or aggregated across, or that the subject’s data be
