The traditional approach to analyzing data from the proposed research design is a univariate, repeated measures analysis of variance (ANOVA). Increasingly, random effects or RE models are being employed with data from repeated measures designs (Hedeker & Gibbons, 1997 and Singer and Willett, 2003). RE models offer two important advantages over ANOVA, (Raudenbush and Bryk, 2002). First, unequal samples sizes such as occurs with missing data do not present the difficulties they impose for ANOVA. Second, it is easy to specify complex models that include multiple random effects. The proposed research model is straightforward and there are only two time periods where data will be collected. In such instances, a third alternative is available: a multivariate repeated measures ANOVA (Tabachnick & Fidell, 2006). Like RE models, the multivariate approach is robust to violations of the sphericity assumption (Stevens, 2002). Unlike RE models, the analysis is straight forward and results are interpreted according to traditional ANOVA conventions, a time effect, a group effect, and a group by time interaction, which makes them understandable to a wider audience (Maxwell and Delaney 2004). Consequently, data for the proposed study will be analyzed using the multivariate repeated measures ANOVA.