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

For the third hypothesis of estimating predictors of trauma-related outcomes, with a
maximum of five predictor variables (L1, L2, L3, L4, L5), a sample of 49 participants provided a
minimum 80% power to detect a large effect (Cohen's d = 0.8).
effects.
Main Analyses
Thus, power was adequate across main analyses to detect large, clinically relevant
Random Effects Modeling. Random effects modeling is a widely-accepted data analytic
technique in longitudinal research designs because it affords modeling individual change and
variances (Tasca & Gallop, 2009). This approach allows for examination of both within-subject
(level 1) and between-subject (level 2) factors in a participant’s response. The combination of the
level 1 and level 2 factors is translated into a mixed linear model with fixed and random
coefficients with person-specific parameters corresponding to a random intercept and a random
slope. Random effects modeling is appropriate for data that have a nested or hierarchical
structure by modeling the within-subject correlation using random effects, and missing data that
are inherent in longitudinal treatment data (Gibbons et al., 1993). Random effects modeling is
superior to other data analytic techniques that are typically used in repeated measurement studies.
Unlike analysis of variance techniques that violate the assumption of sphericity (i.e., equivalent
error variances and correlations between two measurements across time) resulting in inflated
Type 1 error rates, ignore within subject correlation for hierarchical data resulting in
understestimating the variance in the model, and rely on group averages and group variance,
random effects modeling does not (Tasca & Gallop, 2009).
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