The Practical Utility of Importance Measures in Assessing the Relative Importance of Work-Related Perceptions and Organizational Characteristics on Work-Related Outcomes

Abstract

The utility of dominance analysis and other importance indices is the subject of much debate in determining the relative importance of predictors in multiple regression. The goal in conducting this research was to bring an applied perspective to this issue by comparing the conclusions one would draw regarding predictors’ relative importance, when using various indices of importance with real-world data sets. The overall results indicate that researchers would reach only minor differences in their conclusions when using dominance analysis or other importance indices as compared to simply examining the traditional standardized beta weights or squared semipartial correlations. The lack of differences in conclusions drawn is particularly apparent when comparing the rank ordering of the predictor importance produced by the different indices.

A Test of Two Models of Value Creation in Virtual Communities

“…Baltes et al [4] provide evidence that examining the standardized beta weights can be a good way to assess the relative importance of predictors. Following their recommendation, in our study, the mean of the beta weights for all the effort variables is greater than the mean beta weights for the information variables, providing evidence in support of the dominant role of efforts of the sponsor over the MGI variables.…”

Examining the effects of trust in leaders: A bases-and-foci approach

“…Lastly, to address the issue of multicollinearity among the four trust variants in multiple regression analyses, we computed relative importance indexes rather than relying only on regression coefficients to determine the trust variants' explanatory contributions. Nevertheless, given that relative importance indexes cannot resolve inherent problems with correlated predictors (Baltes, Parker, Young, Huff, & Altmann, 2004), caution should be exercised when interpreting relative importance results.…”

FIRE: an SPSS program for variable selection in multiple linear regression analysis via the relative importance of predictors

“…To overcome this problem, several methods have been developed for assessing the RI of the predictors, among which are dominance analysis (Azen & Budescu, 2003;Budescu, 1993;Chevan & Sutherland, 1991) and Johnson's epsilon (ε; J. W. Johnson, 2000), initially proposed by Fabbris (1980). These RI indices are generally used to help determine importance when a researcher has no theoretical ordering of predictor variables (Baltes, Parker, Young, Huff, & Altmann, 2004). However, they can also be used in a more confirmatory way to assess whether some "a priori" or theoretically derived ordering is supported by empirical data.…”