Exploring Nonlinearities in Path Models

Exploring Nonlinearities in Path Models In causal analysis, path models are an appropriate tool for studying relationships between social phenomena. However, they assume linear linkages between variables, and hence they are not always suitable for describing the complexity and richness of relationships in social phenomena. The aim of this work is to propose an exploratory graphical method to evaluate if the phenomena under analysis are actually characterized by non-linear linkages. In particular, the method is well suited to discovering interactions between the observed variables in path models. The proposed approach, which does not depend on any hypothesis on the error distribution, is based on a series of plots that can be easily interpreted and drawn using standard statistical software. As an additional feature, the plots – which we call joint effect plots – support qualitative interpretation of the non-linear linkages after the path model has been specified. Finally, the proposed method is applied within a case study. Non-linearities are explored in a casual model aiming to find the determinants of remittances of a group of Tunisian migrants in Italy. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Exploring Nonlinearities in Path Models

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Publisher
Springer Netherlands
Copyright
Copyright © 2006 by Springer
Subject
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-006-9022-x
Publisher site
See Article on Publisher Site

Abstract

In causal analysis, path models are an appropriate tool for studying relationships between social phenomena. However, they assume linear linkages between variables, and hence they are not always suitable for describing the complexity and richness of relationships in social phenomena. The aim of this work is to propose an exploratory graphical method to evaluate if the phenomena under analysis are actually characterized by non-linear linkages. In particular, the method is well suited to discovering interactions between the observed variables in path models. The proposed approach, which does not depend on any hypothesis on the error distribution, is based on a series of plots that can be easily interpreted and drawn using standard statistical software. As an additional feature, the plots – which we call joint effect plots – support qualitative interpretation of the non-linear linkages after the path model has been specified. Finally, the proposed method is applied within a case study. Non-linearities are explored in a casual model aiming to find the determinants of remittances of a group of Tunisian migrants in Italy.

Journal

Quality & QuantitySpringer Journals

Published: Sep 15, 2006

References

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