The Dominance Analysis Approach for Comparing Predictors in Multiple Regression (original) (raw)
Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression
David Budescu
Psychological Bulletin, 1993
View PDFchevron_right
Measures of predictor variable importance in multiple regression: An additional suggestion
fabbris Luigi
Quality and Quantity, 1980
View PDFchevron_right
Calculating the relative importance of multiple regression predictor variables using dominance analysis and random forests
Atsushi Mizumoto
2022
View PDFchevron_right
FIRE: an SPSS program for variable selection in multiple linear regression analysis via the relative importance of predictors
Urbano Lorenzo-Seva
Behavior Research Methods, 2011
View PDFchevron_right
Determining Predictor Importance In Multiple Regression Under Varied Correlational And Distributional Conditions
Rachel Fouladi
Journal of Modern Applied Statistical Methods, 2002
View PDFchevron_right
Interpreting Multiple Linear Regression: A Guidebook of Variable Importance
Kim Nimon
View PDFchevron_right
Relative Importance Analysis With Multicategory Dependent Variables: An Extension and Review of Best Practices
Joe Luchman
Organizational Research Methods, 2014
View PDFchevron_right
Quantifying the Relative Importance of Predictors in Multiple Linear Regression Analyses for Public Health Studies
Leena Nylander-French
Journal of Occupational and Environmental Hygiene, 2008
View PDFchevron_right
A Heuristic Method for Estimating the Relative Weight of Predictor Variables in Multiple Regression
jeff sa-ad
Multivariate Behavioral Research, 2000
View PDFchevron_right
Use of the multiple lens approach to multiple regression findings with a national dataset
Laura Nathans
View PDFchevron_right
History and Use of Relative Importance Indices In Organizational Research
James M LeBreton
Organizational Research Methods, 2004
View PDFchevron_right
Relative Importance of Predictors in Multilevel Modeling
Bruno Zumbo
Journal of Modern Applied Statistical Methods, 2014
View PDFchevron_right
Regular Articles: Relative Importance of Predictors in Multilevel Modeling
Bruno Zumbo
2014
View PDFchevron_right
Relative Importance Analysis for Psychological Research
madona wijaya
Jurnal Pengukuran Psikologi dan Pendidikan Indonesia (JP3I), 2021
View PDFchevron_right
Multivariate relative importance: Extending relative weight analysis to multivariate criterion spaces
James M LeBreton, Scott Tonidandel
Journal of Applied Psychology, 2008
View PDFchevron_right
On Variable Importance in Linear Regression
Bruno Zumbo
1998
View PDFchevron_right
Bayesian Inference of Predictors Relative Importance in Linear Regression Model Using Dominance Hierarchies
philippe duverger
International Journal of Pure and Apllied Mathematics, 2013
View PDFchevron_right
Two SPSS programs for interpreting multiple regression results
Urbano Lorenzo-Seva
Behavior Research Methods, 2010
View PDFchevron_right
The Importance of Variable Importance
Charles D Coleman
arXiv (Cornell University), 2022
View PDFchevron_right
The Importance of Structure Coefficients in Multiple Regression: A Review with Examples from Published Literature
Thomas K Burdenski
2000
View PDFchevron_right
Using commonality analysis in multiple regressions:a tool to decompose regression effects in the face of multicollinearity
Shomen Mukherjee
Methods in Ecology and Evolution, 2014
View PDFchevron_right
Tools to support interpreting multiple regression in the face of multicollinearity
Kim Nimon
Frontiers in psychology, 2012
View PDFchevron_right
Regression Commonality Analysis: Demonstration of an SPSS Solution
Kim Nimon
Multiple Linear Regression Viewpoints, 2010
View PDFchevron_right
Dominance Analysis Utilizing the Bayesian Framework in Linear and Logistic Regression Models
Chenxin Xue
View PDFchevron_right
Effect Size and Power in Assessing Moderating Effects of Categorical Variables Using Multiple Regression: A 30-Year Review
Herman Aguinis, Robert Boik
Journal of Applied Psychology, 2005
View PDFchevron_right
Direct Effects Testing: a Two-Stage Procedure to Test for Effect Size and Variable Importance for Correlated Binary Predictors and a Binary Response.
Matthew Sperrin
Statistics in Medicine
View PDFchevron_right
Investigating the Performance of Alternate Regression Weights by Studying All Possible Criteria in Regression Models with a Fixed Set of Predictors
Jeff Jones, Niels Waller
Psychometrika, 2011
View PDFchevron_right
Information importance of predictors: Concept, measures, Bayesian inference, and applications
Joseph Retzer
Computational Statistics & Data Analysis, 2009
View PDFchevron_right
A Step-Down Hierarchical Multiple Regression Analysis for Examining Hypotheses About Test Bias in Prediction
Jorge L Mendoza
Applied Psychological Measurement, 1986
View PDFchevron_right
Regression as the Univariate General Linear Model: Examining Test Statistics, p values, Effect Sizes, and Descriptive Statistics Using R
Mandolen Mull
General Linear Model Journal
View PDFchevron_right
On Measuring the Relative Importance of Explanatory Variables in a Logistic Regression
Bruno Zumbo
Journal of Modern Applied Statistical Methods, 2008
View PDFchevron_right