A Monte Carlo Comparison of Relative Importance Methodologies

By James M. LeBreton, Robert E. Ployhart, R. Ladd. 2004

192 citations recorded by the source corpus.

Subfields: Food Science, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/02472171b3ef41c083f99d569247e89e0f5712d1

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Citations in the corpus

Estimators of Relative Importance in Linear Regression Based on Variance Decomposition (2007, background): “Budescu (1993) and Azen and Budescu (2003) introduced dominance analysis that—among other things—assigns exactly the LMG contribution to each regressor, and Azen (2003) provided a SAS macro for the relevant calculations.”
Moments, not minutes: The nature-wellbeing relationship (2021, methodology): “Several authors have discussed the limitations of relying on simple bivariate correlations or standardized linear regression coefficients as measures of predictive or relative importance, and have argued for using a variety of techniques to augment correlational and regression analyses (Darlington &”
Identifying the most important predictors of support for climate policy in the United States (2020, background): “…may not accurately gauge the relative importance of each predictor because they are highly sensitive to other variables in the model and therefore do not provide the best estimate of effect size – an issue that is exacerbated by strong correlations between predictors (LeBreton et al., 2004).”
Interpreting Multiple Linear Regression: A Guidebook of Variable Importance (2012, methodology): “Common effects are measures of total effect (LeBreton et al., 2004), as they quantify the contribution to the regression effect that each variable shares with every other variable set.”
Understanding the Results of Multiple Linear Regression (2013, methodology): “The similarity between the general dominance (GenDom) and relative weights (RLW) is in keeping with research that indicates that these coefficients typically yield almost identical values, despite different definitions and computational strategies (see Johnson, 2000; LeBreton et al., 2004).”
Relative Importance Analysis: A Useful Supplement to Regression Analysis (2011, background): “…comes from some large scale simulation studies were the average difference between the dominance weights and relative weights was often in the third decimal place when collapsing across thousands of simulation runs (LeBreton et al. 2004; LeBreton and Tonidandel 2008; Tonidandel and LeBreton 2010).”
RWA Web: A Free, Comprehensive, Web-Based, and User-Friendly Tool for Relative Weight Analyses (2014, background): “Simulation research clearly shows that these measures of importance perform quite well across a variety of conditions and are recommended for this purpose (LeBreton et al. 2004b).”
Occupational Future Time Perspective: A Meta-Analysis of Antecedents and Outcomes (2018, unknown): “When predictors are correlated, the relative contribution of each to the model R 2 cannot be determined by examining the partial regression weights alone (LeBreton, Ployhart, & Ladd, 2004).”
Determining the Relative Importance of Predictors in Logistic Regression: An Extension of Relative Weight Analysis (2010, background): “…continually demonstrated that both procedures are superior to traditional approaches for determining importance (e.g., p values of regression coefficients, b weights; Azen & Budescu, 2003; Budescu, 1993; Budescu & Azen, 2004; Johnson, 2000, 2001; Johnson & LeBreton, 2004; LeBreton et al., 2004).”
Personality and Cognitive Ability as Predictors of Effective Performance at Work (2014, background): “In evaluating multiple predictors, a small body of research has promoted the use of relative weight analyses (LeBreton et al. 2004).”
Thriving at work: A meta‐analysis (2019, methodology): “The relative contribution of correlated predictors to the model R 2 cannot be determined solely by examining the partial regression weights (Lebreton, Ployhart, & Ladd, 2004).”