Multifaceted personality scales assess multiple related facets or dimensions and, as such, they are typically made up of correlated subscales. In some cases, the degree of correlation among subscales can be so high as to render the use of standard procedures for evaluating a subscale's relative importance (e.g., beta weights or bivariate correlations) dubious. In such cases of high predictor multicollinearity, researchers are faced with few viable options and, in response, many turn to multiple regression when examining predictor-criterion associations (for example, interpreting semipartial correlations and incremental variance estimates). In an effort to broaden researchers' options and thereby allow for greater interpretive clarity, z tests for comparing dependent zero-order correlations and R. G. Malgady's (1987) methods for comparing two dependent semipartial correlations and for comparing dependent semipartial and zero-order correlations are proposed as additional techniques for analyzing predictor (or subscale) criterion associations in the context of predictor collinearity. Worked examples of both techniques are provided, using a dataset on sense of coherence and depression. Finally, relevant computer programs for implementing the aforementioned techniques are noted.