2005
DOI: 10.1038/sj.bdj.4812743
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Problems of correlations between explanatory variables in multiple regression analyses in the dental literature

Abstract: Multivariable analysis is a widely used statistical methodology for investigating associations amongst clinical variables. However, the problems of collinearity and multicollinearity, which can give rise to spurious results, have in the past frequently been disregarded in dental research. This article illustrates and explains the problems which may be encountered, in the hope of increasing awareness and understanding of these issues, thereby improving the quality of the statistical analyses undertaken in denta… Show more

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Cited by 136 publications
(92 citation statements)
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“…Bivariate correlations among all covariates were examined to assess potential problems due to collinearity, as such collinearity might confound the analyses (Tu et al, 2001(Tu et al, , 2005. Where strong correlations were observed between time (year of diagnosis) and the Calman -Hine implementation scores, analyses were undertaken across two time periods -1995 to 2000 and 1996 to 1998, separately -in an attempt to limit the effect of collinearity.…”
Section: Methodsmentioning
confidence: 99%
“…Bivariate correlations among all covariates were examined to assess potential problems due to collinearity, as such collinearity might confound the analyses (Tu et al, 2001(Tu et al, , 2005. Where strong correlations were observed between time (year of diagnosis) and the Calman -Hine implementation scores, analyses were undertaken across two time periods -1995 to 2000 and 1996 to 1998, separately -in an attempt to limit the effect of collinearity.…”
Section: Methodsmentioning
confidence: 99%
“…Generally, our seasonal models have the optimal performance with three auxiliary variables, and less or more variables than three would bring about instability or redundancy of the GWR models. Adding extra auxiliary variables shows little improvement in the GWR model but even reduces the model performance and aggravates the overfitting, due to the increasing risk of intra-correlations among variables [41]. We further analyzed the auxiliary variables in four seasonal model "AOD + 3" and guessed that they coincided with three meteorological effects in PM 2.5 concentrations.…”
Section: Discussionmentioning
confidence: 99%
“…We also determined the synergism between peer smoking and perceived accessibility, that is, the degree to which peer smoking enhances the effects of perceived accessibility on smoking rates, as has been previously described by Hallqvist et al 40 and Andersson et al 41 We used the variance infl ation factor and correlation coeffi cient to assess for collinearity or multicollinearity in the multivariable models. 42 After consideration of multicollinearity, we retained the following covariates for potential inclusion in the multivariable models: age; sex; race-ethnicity; number of siblings; knowledge of product affi liation of the Joe Camel character; having a favorite cigarette advertisement; parental permissiveness of watching R-rated movies; parental smoking; perceived parental or peer approval of smoking; belief that smoking makes a person popular, look cool, or fi t in; involvement in community activities or Boy or Girl Scouts; impulsivity; concerns about weight; anger coping; …”
mentioning
confidence: 99%