The Practical Utility of Importance Measures in Assessing the Relative Importance of Work-Related Perceptions and Organizational Characteristics on Work-Related Outcomes
Restricted accessResearch articleFirst published online July, 2004
The Practical Utility of Importance Measures in Assessing the Relative Importance of Work-Related Perceptions and Organizational Characteristics on Work-Related Outcomes
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.
Azen, R., & Budescu, D. V. (2003). The dominance analysis approach for comparing predictors in multiple regression. Psychological Methods, 8, 129-148.
2.
Bajdo, L. M., & Dickson, M. W. (2002). Perceptions of organizational culture and women’s advancement in organizations: A cross-cultural examination. Sex Roles, 45, 399-414.
3.
Baltes, B. B., Lacost, H., Parker, C. P., Altmann, R., Huff, J., & Young, S. (1999, April). A multitrait-multimethod examination of hierarchical models of psychological climate. Paper presented at the 14th annual meeting of the Society for Industrial and Organizational Psychology, Atlanta, GA.
4.
Beguin, A. A., & Glas, C. A. W. (2001). MCMC estimation and some model-fit analysis of multidimensional IRT models. Psychometrika, 66, 541-561.
5.
Blum, T. C., Fields, D. L., & Goodman, J. S. (1994). Organization-level determinants of women in management. Academy of Management Journal, 37, 241-268.
6.
Bolt, D. M. (2002). A Monte Carlo comparison of parametric and nonparametric polytomous DIF detection methods. Applied Measurement in Education, 1(5), 113-141.
7.
Bolt, D. M., Cohen, A. S., & Wollack, J. A. (2001). A mixture item response model for multiple-choice data. Journal of Educational and Behavioral Statistics, 26, 381-409.
8.
Budescu, D. V. (1993). Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression. Psychological Bulletin, 114, 542-551.
9.
Chan, W., Ho, R. M., & Leung, K. (1999). An alternative method for evaluating congruence coefficients with procrustes rotation: A bootstrap procedure. Psychological Methods, 4, 378-402.
10.
Darlington, R. B. (1968). Multiple regression in psychological research and practice. Psychological Bulletin, 69, 161-182.
11.
Ford, K. J., Quiñones, M. A., Sego, D. J., & Sorra, J. S. (1992). Factors affecting the opportunity to perform trained tasks on the job. Personnel Psychology, 45, 511-528.
12.
Gibson, W. A. (1962). Orthogonal predictors: A possible resolution of the Hoffman-Ward controversy. Psychological Reports, 11, 32-34.
13.
Greenhaus, J. H., Parasuraman, S., & Wormley, W. M. (1990). Effects of race on organizational experiences, job performance evaluations, and career outcomes. Academy of Management Journal, 33, 64-86.
14.
Healy, M. J. R. (1990). Measuring importance. Statistics in Medicine, 9, 633-637.
15.
Hoffman, P. J. (1960). The paramorphic representation of clinical judgment. Psychological Bulletin, 57, 116-131.
16.
Hoffman, P. J. (1962). Assessment of the independent contributions of predictors. Psychological Bulletin, 59, 77-80.
17.
Johnson, J. W. (2000). A heuristic method for estimating the relative weight of predictor variables in multiple regression. Multivariate Behavioral Research, 35, 1-19.
18.
Jones, A. P., & James, L. R. (1979). Psychological climate: Dimensions and relationships of individual and aggregated work environment perceptions. Organizational Behavior and Human Performance, 23, 201-250.
19.
LeBreton, J. M., Ployhart, R. E., & Ladd, R. T. (2001, April). Determining relative importance via dominance analysis: A Monte Carlo simulation. In J. M. LeBreton & J. W. Johnson (Eds.), Use of relative importance methodologies in organizational research. Symposium conducted at the annual meeting of the Society for Industrial and Organizational Psychology, San Diego, CA.
20.
LeBreton, J. M., Ployhart, R. E., & Ladd, R. T. (2004). A Monte Carlo comparison of relative importance methodologies. Organizational Research Methods, 7(3), 258-282.
21.
Mitchell, T. W., & Klimoski, R. J. (1986). Estimating the validity of cross-validity estimation. Journal of Applied Psychology, 71, 311-317.
22.
Parker, C. P., Dipboye, R. L., & Jackson, S. L. (1995). Perceptions of organizational politics: An investigation of antecedents and consequences. Journal of Management, 21, 891-912.
23.
Song, X., Lee, S., & Zhu, H. (2001). Model selection in structural equation models with continuous and polytomous data. Structural Equation Modeling, 8, 378-396.
24.
Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th ed.). New York: HarperCollins College.
25.
Theil, H. (1987). How many bits of information does an independent variable yield in a multiple regression?Statistics and Probability Letters, 6, 107-108.
