Farrar D, Glauber R: Multicollinearity in regression analysis: the problem revisited. Rev Econ Stat. 1967, 49 (1): 92-107. 10.2307/1937887. Article Google Scholar Margolis AD, Joseph H, Belcher L, Hirshfield S, Chiasson MA: 'Never testing for HIV' among men who have sex with men recr...
Variance Inflation Factor (VIF) values (VIF = 1.006 < 10 (or less than 3) of both variables obtained from Coefficients table of our multiple linear regression model demonstrated that the predictor variables were not highly correlated with each other and Multicollinearity assumption was als...
Over recent years, there has been increasing scientific interest in understanding the potential benefits of reading fiction for a range of psychological outcomes. Whilst the majority of empirical research has tested the idea that reading fiction promotes social cognitive abilities including theory of mind...
applied. Variance inflation factor (VIF) is used to detect the severity of multicollinearity in our regression analyses. The VIF is below 3 in all cases. Two two-stage models were constructed, with the individual and contextual variable blocks entered in different orders. This procedure generates ...
This is likely due to multicollinearity among the predictors used in this study, which suggests that stigma does not uniquely explain HIV testing intentions when more powerful predictors are taken into consideration. Figure 1. Social Cognitive Theory (SCT) Applied to HIV Testing. There were ...
The multicollinearity test among independent variables was examined as tolerance and variance inflation factors, and the interdependence among residuals, was examined using the Durbin–Watson statistic. All analyses were performed using the SPSS Statistics 20 software (IBM, Armonk, NY, USA)....
In this sense, [13] indicated that “MARS is better suited to model situations that include a high number of variables, non-linearity, multicollinearity and/or a high degree of interaction among predictors.” In addition, works such as [14] and [15] have praised the rigorous statistical ...