2017
DOI: 10.1007/s11135-017-0584-6
|View full text |Cite
|
Sign up to set email alerts
|

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

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… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

5
425
3
3

Year Published

2018
2018
2024
2024

Publication Types

Select...
8
1

Relationship

1
8

Authors

Journals

citations
Cited by 764 publications
(436 citation statements)
references
References 26 publications
(23 reference statements)
5
425
3
3
Order By: Relevance
“…We employed a linear regression to check for the existence of potential confounders and to verify whether they were not confusing the association between dependent and independent variables (Johnston et al 2018). First, because the independent variables included in the model could highly correlate with each other, we conducted an index of tolerance and the measure of variance inflation factors (VIF) to detect collinearity.…”
Section: Resultsmentioning
confidence: 99%
“…We employed a linear regression to check for the existence of potential confounders and to verify whether they were not confusing the association between dependent and independent variables (Johnston et al 2018). First, because the independent variables included in the model could highly correlate with each other, we conducted an index of tolerance and the measure of variance inflation factors (VIF) to detect collinearity.…”
Section: Resultsmentioning
confidence: 99%
“…Equation 1 showed the linear model specification with dummy variables for the demand function. Equation 2 showed the double log model specification derived from the Cobb-Douglas function (exponential) (Gujarati and Porter 2009) with a logarithm transformation, including dummy variables. !…”
Section: Model Specificationmentioning
confidence: 99%
“…The correlation was then conducted to determine which explanatory variables are correlated. A correction was made by omitting one of the closely correlated variables (Gujarati and Porter 2009).…”
Section: Specification Testingmentioning
confidence: 99%
“…Multicollinearity test for selected individual-level predictors was done to ensure in ated standard errors due to many predictors measuring the same characteristics are controlled. In this research, the parameters for variance in ation factor (VIF) ≤ 2.5 and tolerance ≥ 0.4 were set as recommended by Johnston et al [41]for the logistic regression model to identify potentially redundant variables due to collinearity.…”
Section: Multilevel Regression Modelmentioning
confidence: 99%