多元迴歸的自變數比較與多元共線性之影響：效果量、優勢性與相對權數指標的估計與應用
摘要
Regression analysis is frequently used in social sciences. However, regression analyses often rely heavily on hypothesis testing and interpretations of regression coefficients. As a result, the effect sizes of regression models as well as the qualities of individual predictors have long been ignored. This paper reviews several indices that can be used to evaluate the effect size and relative importance of predictors, the relative weight analysis (RWA), and the dominance analysis (DA) in multiple regressions. A simulated dataset is used to examine the impacts of mutlicollinearity, including the enhancement, suppression, and redundancy effects, on the evaluation of the effect size and relative importance of predictors. A sample of 2,325 Taiwanese adults selected from the 2011 Panel Study of Family Dynamics (PSFD) are used to demonstrate the use of those indices in predicting the salary differences. Results suggest that the indices based on RWA and DA are recommended for evaluating the relative importance of predictors. In particular, DA has the advantage of flexible procedures for evaluating the different facets of the dominance of predictors. The properties of the recommended index were summarized in the end of the paper.