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7). In the present study, we investigated the bias of squared [http://www.selleckchem.com/products/GDC-0449.html Selleckchem Vismodegib] regression structure coefficients and determined if a formula, that has been used to correct for bias in coefficients of determination and Pearson r2, could be used to correct for bias in squared regression structure coefficients. Squared regression structure coefficients with less bias will be more true to the population parameters and more accurately describe how much of  the regression effect can be attributed to a given predictor. In the remainder of this section, we review the general linear model (GLM) as a rubric for regression interpretation followed by the squared regression structure coefficient, squared multiple correlation coefficient, Pearson r2, and sample sizes in  published literature before presenting the purpose of the study. General linear model (GLM) as a rubric for regression interpretation Multiple regression analyses are part of the GLM. Furthermore, all analytic methods that are part of the GLM are correlational and have the capability of producing variance-accounted-for effect sizes such as R2, ��2, ��2, which are analogs to r2 (see Thompson, 2000, 2006; Zientek and Thompson, 2009). As Graham (2008) further explained, The vast majority of parametric statistical procedures in common use are part of (a single analytic family called) the GLM, including the t-test, analysis of variance (ANOVA), multiple regression, descriptive discriminant analysis (DDA), multivariate analysis of variance (MANOVA), canonical correlation analysis (CCA), and structural equation modeling  (SEM). Moreover, these procedures are hierarchical (italics added), in that some procedures are special cases of others. (p. 485). The hierarchical structure of the GLM has been demonstrated by the work of several researchers. First, Cohen (1968) showed that all univariate parametric analyses such as t-tests, ANOVAs, and Pearson r are subsumed as special cases of multiple regression analysis. Next, Knapp (1978) showed that all of the common univariate and multivariate analyses conducted in research are special cases of canonical correlation analysis. Finally, Bagozzi et al. (1981) and later Graham (2008) showed that SEM can be categorized as an even more general case of the GLM (see Fan, 1997 for more detail). The importance of interpreting structure coefficients and analogs of regression weights for statistical analyses within the GLM permeates the literature. For example, within the GLM for the exploratory factor analysis case, Gorsuch (1983) argued that the interpretation of factors is contingent on the factor structure. Graham et al.