Johnson, J. W. (2000). A heuristic method for estimating the relative weight of predictor variables in multiple regression. Multivariate Behavioral Research, 35, 1-19.
Johnson, J. W. (2004). Factors affecting relative weights: The influence of sampling and measurement error. Organizational Research Methods, 7, 283-299.
Krasikova, D., LeBreton, J. M., & Tonidandel, S. (2011). Estimating the Relative Importance of Variables in Multiple Regression Models. In G. P. Hodgkinson & J.K. Ford (Eds.), International review of industrial and organizational psychology. Indianapolis, IN: Wiley.
LeBreton, J. M. & Tonidandel, S. (2008). Multivariate relative importance: Extending relative weight analysis to multivariate criterion spaces. Journal of Applied Psychology, 93, 329-345.
Tonidandel, S. & LeBreton, J. M. (2010). Determining the Relative Importance of Predictors in Logistic Regression: An Extension of Relative Weights Analysis. Organizational Research Methods, 13, 767-781.
Tonidandel, S., LeBreton, J. M., & Johnson, J. W. (2009). Determining the statistical significance of relative weights. Psychological Methods, 14, 387-399.
Dalal, R. S., Baysinger, M., Brummel, B. J., & LeBreton, J. M. (2012). The relative importance of employee engagement, other job attitudes, and trait affect as predictors of job performance. Journal of Applied Social Psychology, 42, 295-325.
--Example of a paper comparing univariate and multivariate relative weights. The authors found univariate weights did not always yield the same conclusions as multivariate weights.
LeBreton, J. M., Baysinger, M., Abbey, A., & Jacques-Tiura, A. J. (2013). The relative importance of psychopathy-related traits in predicting impersonal sex and hostile masculinity. Personality and Individual Differences, 55, 817-822.
--A paper that expands the application of univariate relative weight analysis to topics related to clinical and social psychology.
LeBreton, J. M., Binning, J. F., Adorno, A. J., & Melcher, K. M. (2004). Importance of personality and job-specific affect for predicting job attitudes and withdrawal behavior. Organizational Research Methods, 7, 300-325.
--Early example of a paper comparing relative weights with general dominance weights. The authors found that the estimates were virtually identical to one another, but deviated substantially from standardized regression weights.
LeBreton, J. M., Hargis, M. B., Griepentrog, B., Oswald, F. L., & Ployhart, R. E. (2007). A multidimensional approach for evaluating variables in organizational research and practice. Personnel Psychology, 60, 475-498. 37.
--A paper that compares and contrasts incremental importance (incremental validity) with relative importance. The authors recommend an integrative strategy that combines elements of both types of importance analysis.
LeBreton, J. M., Tonidandel, S., & Krasikova, D. (2013). Residualized relative importance analysis: A technique for the comprehensive decomposition of variance in higher-order regression models. Organizational Research Methods, 16, 449-473. 38.
--A paper that explains how to use relative weight analysis in models testing interaction effects and other high-order effects (e.g., quadratic, cubic).
LeBreton, J. M., Ployhart, R. E., & Ladd, R. T. (2004). A Monte Carlo comparison of relative importance methodologies. Organizational Research Methods, 7, 258-282.
--A paper that reports the results of a comprehensive simulation study comparing different estimates of importance. Results indicated that general dominance weights and relative weights yielded virtually identical conclusions, but that regression weights, correlations, and the product measure all deviated substantially from these newer statistics. The authors concluded by recommending either relative weights or general dominance weights.
Tonidandel, S., & LeBreton, J. M. (2013). Beyond step down analysis: A new test for decomposing the importance of dependent variables in MANOVA. Journal of Applied Psychology, 98, 469-477.
--A paper that extends the logic of relative weight analysis to multivariate analysis of variance (MANOVA). The authors discuss how the problems of determining predictor relative importance in regression mirror the problems of determining dependent variable relative importance in MANOVA. The authors introduce a new technique that can be used to estimate variable importance in MANOVA and show that it is superior to traditional approaches (e.g., univariate ANOVAs; step-down analysis; etc.).