Confounding and collinearity in regression analysis: a cautionary tale and an alternative procedure, illustrated by studies of British voting behaviour

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Many ecological- and individual-level analyses of voting behaviour use multiple regressions with a considerable number of independent variables but few discussions of their results pay any attention to the potential impact of inter-relationships among those independent variables—do they confound the regression parameters and hence their interpretation? Three empirical examples are deployed to address that question, with results which suggest considerable problems. Inter-relationships between variables, even if not approaching high collinearity, can have a substantial impact on regression model results and how they are interpreted in the light of prior expectations. Confounded relationships could be the norm and interpretations open to doubt, unless considerable care is applied in the analyses and an extended principal components method for doing that is introduced and exemplified.

Related collections

Most cited references 31

Record: found
Abstract: found
Article: not found

The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations.

Reuben M. Baron, David A Kenny (1986)

In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptually and strategically, the many ways in which moderators and mediators differ. We then go beyond this largely pedagogical function and delineate the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena, including control and stress, attitudes, and personality traits. We also provide a specific compendium of analytic procedures appropriate for making the most effective use of the moderator and mediator distinction, both separately and in terms of a broader causal system that includes both moderators and mediators.

0 comments Cited 4191 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

A general approach to causal mediation analysis.

Kosuke Imai, Luke Keele, Dustin Tingley (2010)

Traditionally in the social sciences, causal mediation analysis has been formulated, understood, and implemented within the framework of linear structural equation models. We argue and demonstrate that this is problematic for 3 reasons: the lack of a general definition of causal mediation effects independent of a particular statistical model, the inability to specify the key identification assumption, and the difficulty of extending the framework to nonlinear models. In this article, we propose an alternative approach that overcomes these limitations. Our approach is general because it offers the definition, identification, estimation, and sensitivity analysis of causal mediation effects without reference to any specific statistical model. Further, our approach explicitly links these 4 elements closely together within a single framework. As a result, the proposed framework can accommodate linear and nonlinear relationships, parametric and nonparametric models, continuous and discrete mediators, and various types of outcome variables. The general definition and identification result also allow us to develop sensitivity analysis in the context of commonly used models, which enables applied researchers to formally assess the robustness of their empirical conclusions to violations of the key assumption. We illustrate our approach by applying it to the Job Search Intervention Study. We also offer easy-to-use software that implements all our proposed methods. PsycINFO Database Record (c) 2010 APA, all rights reserved.

0 comments Cited 496 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: not found
Book: not found

Regression Diagnostics

John D. Fox (1991)

0 comments Cited 197 times – based on 0 reviews

Bookmark

All references

Author and article information

Contributors

Ron Johnston: R.Johnston@bristol.ac.uk

Journal

Journal ID (nlm-ta): Qual Quant

Journal ID (iso-abbrev): Qual Quant

Title: Quality & Quantity

Publisher: Springer Netherlands (Dordrecht )

ISSN (Print): 0033-5177

Publication date (Electronic): 13 November 2017

Publication date PMC-release: 13 November 2017

Publication date (Print): 2018

Volume: 52

Issue: 4

Pages: 1957-1976

Affiliations

ISNI 0000 0004 1936 7603, GRID grid.5337.2, School of Geographical Sciences, , University of Bristol, ; Bristol, BS8 1SS UK

Article

Publisher ID: 584

DOI: 10.1007/s11135-017-0584-6

PMC ID: 5993839

PubMed ID: 29937587

SO-VID: 5120d38b-c6e1-49d1-9257-3acc68c4f8ea

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.