RELATIVE IMPORTANCE ANALYSIS WITH MULTIVARIATE MODELS: SHIFTING THE FOCUS FROM INDEPENDENT VARIABLES TO PARAMETER ESTIMATES

Conclusions regarding the relative importance of different independent variables in a statistical model have meaningful implications for theory and practice. However, methods for determining relative importance have yet to extend beyond statistical models with a single dependent variable and a limi...

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Main Authors: Joseph N. Luchman, Xue Lei, Seth Kaplan
Format: Article
Language:English
Published: Sarawak Research Society (SRS) 2020-06-01
Series:Journal of Applied Structural Equation Modeling
Subjects:
Online Access:https://jasemjournal.com/wp-content/uploads/2020/07/Luchman-et-al-2020-Vol4Issue2.pdf
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author Joseph N. Luchman
Xue Lei
Seth Kaplan
author_facet Joseph N. Luchman
Xue Lei
Seth Kaplan
author_sort Joseph N. Luchman
collection DOAJ
description Conclusions regarding the relative importance of different independent variables in a statistical model have meaningful implications for theory and practice. However, methods for determining relative importance have yet to extend beyond statistical models with a single dependent variable and a limited set of multivariate models. To accommodate multivariate models, the current work proposes shifting away from the concept of independent variable relative importance toward that of parameter estimate relative importance (PERI). This paper illustrates the PERI approach by comparing it to the evaluation of regression slopes and independent variable relative importance (IVRI) statistics to show the interpretive and methodological advantages of the new concept and associated methods. PERI’s advantages above standardized slopes stem from the same fit metric that is used to compute PERI statistics; this makes them more comparable to one another than standardized slopes. PERI’s advantages over IVRI stem from situations where independent variables do not predict all dependent variables; hence, PERI permits importance determination in situations where independent variables are nested in dependent variables they predict. We also provide recommendations for implementing PERI using dominance analysis with statistical models that can be estimated with maximum likelihood estimation combined with a series of model constraints using two examples.
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spelling doaj.art-e3c35c2a8f034bf5b4e8223068ac47262022-12-21T17:59:02ZengSarawak Research Society (SRS)Journal of Applied Structural Equation Modeling2590-42212020-06-0142120RELATIVE IMPORTANCE ANALYSIS WITH MULTIVARIATE MODELS: SHIFTING THE FOCUS FROM INDEPENDENT VARIABLES TO PARAMETER ESTIMATESJoseph N. Luchman0Xue Lei1Seth Kaplan2Fors Marsh Group, Arlington, VirginiaDepartment of Psychology, George Mason University, Fairfax, VirginiaDepartment of Psychology, George Mason University, Fairfax, VirginiaConclusions regarding the relative importance of different independent variables in a statistical model have meaningful implications for theory and practice. However, methods for determining relative importance have yet to extend beyond statistical models with a single dependent variable and a limited set of multivariate models. To accommodate multivariate models, the current work proposes shifting away from the concept of independent variable relative importance toward that of parameter estimate relative importance (PERI). This paper illustrates the PERI approach by comparing it to the evaluation of regression slopes and independent variable relative importance (IVRI) statistics to show the interpretive and methodological advantages of the new concept and associated methods. PERI’s advantages above standardized slopes stem from the same fit metric that is used to compute PERI statistics; this makes them more comparable to one another than standardized slopes. PERI’s advantages over IVRI stem from situations where independent variables do not predict all dependent variables; hence, PERI permits importance determination in situations where independent variables are nested in dependent variables they predict. We also provide recommendations for implementing PERI using dominance analysis with statistical models that can be estimated with maximum likelihood estimation combined with a series of model constraints using two examples.https://jasemjournal.com/wp-content/uploads/2020/07/Luchman-et-al-2020-Vol4Issue2.pdfrelative importancedominance analysispath analysiszero-inflated poisson regressionmultivariate model
spellingShingle Joseph N. Luchman
Xue Lei
Seth Kaplan
RELATIVE IMPORTANCE ANALYSIS WITH MULTIVARIATE MODELS: SHIFTING THE FOCUS FROM INDEPENDENT VARIABLES TO PARAMETER ESTIMATES
Journal of Applied Structural Equation Modeling
relative importance
dominance analysis
path analysis
zero-inflated poisson regression
multivariate model
title RELATIVE IMPORTANCE ANALYSIS WITH MULTIVARIATE MODELS: SHIFTING THE FOCUS FROM INDEPENDENT VARIABLES TO PARAMETER ESTIMATES
title_full RELATIVE IMPORTANCE ANALYSIS WITH MULTIVARIATE MODELS: SHIFTING THE FOCUS FROM INDEPENDENT VARIABLES TO PARAMETER ESTIMATES
title_fullStr RELATIVE IMPORTANCE ANALYSIS WITH MULTIVARIATE MODELS: SHIFTING THE FOCUS FROM INDEPENDENT VARIABLES TO PARAMETER ESTIMATES
title_full_unstemmed RELATIVE IMPORTANCE ANALYSIS WITH MULTIVARIATE MODELS: SHIFTING THE FOCUS FROM INDEPENDENT VARIABLES TO PARAMETER ESTIMATES
title_short RELATIVE IMPORTANCE ANALYSIS WITH MULTIVARIATE MODELS: SHIFTING THE FOCUS FROM INDEPENDENT VARIABLES TO PARAMETER ESTIMATES
title_sort relative importance analysis with multivariate models shifting the focus from independent variables to parameter estimates
topic relative importance
dominance analysis
path analysis
zero-inflated poisson regression
multivariate model
url https://jasemjournal.com/wp-content/uploads/2020/07/Luchman-et-al-2020-Vol4Issue2.pdf
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