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

Spinella & Miley, 2004) were examined as possible covariates to help and reduce error variance
and obtain a more powerful test (Tabachnick & Fidell, 2001). Based on the small correlations
between the potential covariates and inhibition measures, no covariates were included in the
main analyses. However for the exploratory analyses examining pre-treatment inhibition as a
predictor of changes in psychopathology, age showed significant correlations with pre-treatment
inhibition and was therefore included as a covariate for AB.
Power Analysis
Power analyses were based on a general linear model framework based on the observed
effects seen with each of the four specific aims. The general linear model framework provides
slightly more conservative power estimates compared to longitudinal model frameworks such as
mixed effects modeling. The increase in power for mixed effects models depends on with
magnitude of the magnitude of the within subject correlation such that power ranges between
the estimates derived in the general linear model framework to 100% power.
Power in the present study was most constrained by the first hypothesis, comparing two
treatment modalities (PE vs SER) on AB and PPI from pre-to post-treatment, and the third
hypothesis, examining the ability of pre-treatment inhibition (AB: L1, L2, L3, L4, L5; PPI: 30
ms, 60 ms, 120 ms) to predict changes from pre- to post-treatment and follow-up on
psychopathology and functioning.
For the first hypothesis, with respect to the treatment modality x time interaction, the
sample size of two groups (SER: 29 and PE: 20) yielded 77.1% power to detect a significant
interaction when the interaction is a disordinal effect. A disordinal effect means the treatment
effect is in opposite directions between the two treatments with effect sizes of 0.8 for each
treatment contrast and the two levels of the predictor.
29