Assessing the Relative Importance of Predictors in Latent Regression Models

References

• Azen, R., & Budescu, D. (2003). The dominance analysis approach for comparing predictors in multiple regression. Psychological Methods, 8, 129–148.
   PubMed Web of Science ®Google Scholar
• Azen, R., & Traxel, N. (2009). Using dominance analysis to determine predictor importance in logistic regression. Journal of Educational and Behavioral Statistics, 34, 319–347.
   Web of Science ®Google Scholar
• Bollen, K. (1989). Structural Equations with Latent Variables. John Wiley and Sons.
   Google Scholar
• Braeken, J., Mulder, J., & Wood, S. (2015). Relative effects at work: Bayes factors for order hypotheses. Journal of Management, 41, 544–573.
   Web of Science ®Google Scholar
• Braun, M. T., Converse, P. D., & Oswald, F. L. (2019). The accuracy of dominance analysis as a metric to assess relative importance: The joint impact of sampling error variance and measurement unreliability. The Journal of Applied Psychology, 104, 593–602.
   PubMedGoogle Scholar
• 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.
   Web of Science ®Google Scholar
• de Jong, P. (1999). Hierarchical regression analysis in structural equation modeling. Structural Equation Modeling, 6, 198–211.
   Web of Science ®Google Scholar
• Efron, B. (1987). Better bootstrap confidence intervals. Journal of the American Statistical Association, 82, 171–185.
   Web of Science ®Google Scholar
• Gu, X. (2021). Evaluating predictors’ relative importance using Bayes factors in regression models. Psychological Methods, 2021, 431.
   Google Scholar
• Gu, X., Hoijtink, H., & Mulder, J. (2020). Bayesian one-sided variable selection. Multivariate Behavioral Research, 2020, 1–15.
   Google Scholar
• Gu, X., Hoijtink, H., Mulder, J., & Lissa, C. J. V. (2019). bain: Bayes factors for informative hypotheses. Retrieved from https://CRAN.R-project.org/package=bain (R package version 0.2.1)
   Google Scholar
• Gu, X., Mulder, J., & Hoijtink, H. (2018). Approximated adjusted fraction Bays factors: A general method for testing informative hypotheses. British Journal of Mathematical and Statistical Psychology, 71, 229–261.
   PubMed Web of Science ®Google Scholar
• Gu, X., Mulder, J., Deković, M., & Hoijtink, H. (2014). Bayesian evaluation of inequality constrained hypotheses. Psychological Methods, 19, 511–527.
   PubMed Web of Science ®Google Scholar
• Hayes, T. (2021). R-squared change in structural equation models with latent variables and missing data. Behavior Research Methods, 53, 2127–2157.
   PubMed Web of Science ®Google Scholar
• Hoijtink, H., Mulder, J., van Lissa, C. J., & Gu, X. (2019). A tutorial on testing hypotheses using the Bayes factor. Psychological Methods, 24, 539–556.
   PubMed Web of Science ®Google Scholar
• Johnson, J. W. (2000). A heuristic method for estimating the relative weight of predictor variables in multiple regression. Multivariate Behavioral Research, 35, 1–19.
   PubMed Web of Science ®Google Scholar
• Johnson, J. W., & LeBreton, J. (2004). History and use of relative importance indices in organizational research. Organizational Research Methods, 7, 238–257.
   Web of Science ®Google Scholar
• Klugkist, I., Laudy, O., & Hoijtink, H. (2005). Inequality constrained analysis of variance: A Baysian approach. Psychological Methods, 10, 477–493.
   PubMed Web of Science ®Google Scholar
• Lindeman, R. H., Merenda, P. F., & Gold, R. Z. (1980). Introduction to bivariate and multivariate analysis. Scott Foresman.
   Google Scholar
• Luo, W., & Azen, R. (2013). Determining predictor importance in hierarchical linear models using dominance analysis. Journal of Educational and Behavioral Statistics, 38, 3–31.
   Web of Science ®Google Scholar
• Mulder, J., Wagenmakers, E. J., & Marsman, M. (2020). A generalization of the Savage Dickey density ratio for testing equality and order constrained hypotheses. The American Statistician, 2020, 1–8.
   Google Scholar
• Muthén, B., & Asparouhov, T. (2012). Bayesian structural equation modeling: A more flexible representation of substantive theory. Psychological Methods, 17, 313–335.
   PubMed Web of Science ®Google Scholar
• Muthén, L., & Muthén, B. (2010). Mplus User’s Guide (6th ed.). Muthén & Muthén.
   Google Scholar
• Nimon, K., & Oswald, F. (2013). Understanding the results of multiple linear regression: Beyond standardized coefficients. Organizational Research Methods, 16, 650–674.
   Web of Science ®Google Scholar
• Preacher, K. J. (2006). Testing complex correlational hypotheses with structural equation models. Structural Equation Modeling, 13, 520–543.
   Web of Science ®Google Scholar
• Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48, 1–36.
   Web of Science ®Google Scholar
• Tonidandel, S., LeBreton, J. M., & Johnson, J. (2009). Determining the statistical significance of relative weights. Psychological Methods, 14, 387–399.
   PubMed Web of Science ®Google Scholar
• Van Lissa, C. J., Gu, X., Mulder, J., Rosseel, Y., Van Zundert, C., & Hoijtink, H. (2021). Teacher’s corner: Evaluating informative hypotheses using the bayes factor in structural equation models. Structural Equation Modeling, 28, 292–301.
   Web of Science ®Google Scholar
• Warren, R. D., White, J. K., & Fuller, W. A. (1974). An errors-in-variables analysis of managerial role performance. Journal of the American Statistical Association, 69, 886–893.
   Web of Science ®Google Scholar
• Wetzels, R., Grasman, R. P. P. P., & Wagenmakers, E. J. (2010). An encompassing prior generalization of the Savage-Dickey density ratio test. Computational Statistics & Data Analysis, 38, 666–690.
   Google Scholar