Comparing Coefficients of Nonlinear Multivariate Regression Models Between Equations

The present study discusses the usage of non-linear constraints in regression models with multiple categorical outcomes. With this approach, effect differences between equations are made accessible to statistical tests while potential differences in residual variation are explicitly taken into accou...

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Main Authors: Christoph Kern, Petra Stein
Format: Article
Language:English
Published: European Survey Research Association 2015-12-01
Series:Survey Research Methods
Subjects:
Online Access:https://ojs.ub.uni-konstanz.de/srm/article/view/6211
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author Christoph Kern
Petra Stein
author_facet Christoph Kern
Petra Stein
author_sort Christoph Kern
collection DOAJ
description The present study discusses the usage of non-linear constraints in regression models with multiple categorical outcomes. With this approach, effect differences between equations are made accessible to statistical tests while potential differences in residual variation are explicitly taken into account. In this context, it can be shown that the techniques reviewed by Williams (2010) are conjointly equivalent to the specification of non-linear constraints in multivariate regression models. However, the application of non-linear constraints extends these approaches into a structural equation modeling framework, which allows the researcher to address a broader range of research questions.
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spelling doaj.art-4d1494cc189b4e4bb5d4d8798adef0412022-12-22T02:46:11ZengEuropean Survey Research AssociationSurvey Research Methods1864-33612015-12-019310.18148/srm/2015.v9i3.62115735Comparing Coefficients of Nonlinear Multivariate Regression Models Between EquationsChristoph Kern0Petra Stein1University of Duisburg-EssenUniversity of Duisburg-EssenThe present study discusses the usage of non-linear constraints in regression models with multiple categorical outcomes. With this approach, effect differences between equations are made accessible to statistical tests while potential differences in residual variation are explicitly taken into account. In this context, it can be shown that the techniques reviewed by Williams (2010) are conjointly equivalent to the specification of non-linear constraints in multivariate regression models. However, the application of non-linear constraints extends these approaches into a structural equation modeling framework, which allows the researcher to address a broader range of research questions.https://ojs.ub.uni-konstanz.de/srm/article/view/6211non-linear constraintsstructural equation modelingeffect comparison
spellingShingle Christoph Kern
Petra Stein
Comparing Coefficients of Nonlinear Multivariate Regression Models Between Equations
Survey Research Methods
non-linear constraints
structural equation modeling
effect comparison
title Comparing Coefficients of Nonlinear Multivariate Regression Models Between Equations
title_full Comparing Coefficients of Nonlinear Multivariate Regression Models Between Equations
title_fullStr Comparing Coefficients of Nonlinear Multivariate Regression Models Between Equations
title_full_unstemmed Comparing Coefficients of Nonlinear Multivariate Regression Models Between Equations
title_short Comparing Coefficients of Nonlinear Multivariate Regression Models Between Equations
title_sort comparing coefficients of nonlinear multivariate regression models between equations
topic non-linear constraints
structural equation modeling
effect comparison
url https://ojs.ub.uni-konstanz.de/srm/article/view/6211
work_keys_str_mv AT christophkern comparingcoefficientsofnonlinearmultivariateregressionmodelsbetweenequations
AT petrastein comparingcoefficientsofnonlinearmultivariateregressionmodelsbetweenequations