Copyright 2012 Aileen M. Echiverri-Cohen - University of Washington

demonstrated appropriate skewness given the expected skewed nature of the U-shaped curve of
the AB effect. In contrast, PPI showed significant positive skewness and was corrected using
natural log transformations, consistent with other PPI studies (Ornitz et al., 1989). To reduce
outlier status, univariate and multivariate outliers were examined separately at pre- and post-
treatment to isolate the correlated observations that often result in consecutive measurements in
longitudinal studies (Tasca & Gallop, 2009). Univariate outliers were evaluated by running
scatterplots of standardized residuals of measures, in addition to the inspection of z-scores where
cases with standardized residual values exceeding 3.29 in absolute value were identified as
potential outliers (Tabachnick & Fidell, 2001). The outliers for the repeated measures, were
determined by getting the absolute value of the correlation of the observed values and the
subject-specific predicted values. The lower 5 th percentile of the correlation coefficients were
linked back to the subjects corresponding to potential multivariate outliers. Where there were
potential outliers, variable transformation or changing scores on the variable for the outlier so
that it is less deviant, were considered to minimize variability within groups (Tabachnick &
Fidell, 2001). Missing data was addressed using restricted maximum likelihood (REML) within a
random effects modeling and mixed model regressions analytic approach assuming that the
missing data is independent of outcome variables (Gallop & Tasca, 2009).
Evaluating Possible Covariates
A bivariate correlation matrix was conducted across all variables to evaluate possible
covariates. Covariates were included in the analysis if it was theoretically correlated with the
outcome variables (Tabachnick et al., 2001). Age and education are demographic variables
known to be related to executive functioning (Hasher & Zacks, 1988; Posner & Snyder, 1975;
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