Therefore, in our enhanced multiple regression guide, we show you: (a) how to use SPSS Statistics to detect for multicollinearity through an inspection of correlation coefficients and Tolerance/VIF values; and (b) how to interpret these correlation coefficients and Tolerance/VIF values so that you...
Multicollinearity test Excessive correlation of variables can cause problems such as inaccurate regression coefficients and variance inflation, which may affect the explanatory power and predictive ability of the model. The multicollinearity test is used to detect the degree of correlation between variables....
Learn about factor analysis - a simple way to condense the data in many variables into a just a few variables.
In the second step, before the analysis, multicollinearity was examined, because this can lead to unstable and unreliable estimates of the regression coefficients and can make it difficult to determine the unique contribution of each independent variable to the outcome. To detect multicollinearity, ...
The coefficient of determination (R2), and predictors’ effect size (f2) were calculated to assess the predictive ability of the structural model. The variance inflation factor (VIF) value was examined to determine multicollinearity. Finally, the hypotheses of the study were tested, and the ...
Main St., St. Albans, VT 05478, United States. Tel.: +1 802 524 1500. 6 To test for multicollinearity, an analysis of the variance inflation factors (VIFs) was conducted. No collinearity was detected within the model; all of the initial variables were included in the final model....
(humble leadership, LMX, positive emotion, improvisation, and power distance orientation). Fourth, we used regression analysis in SPSS22.0, and we used the bootstrapping approach to estimate the intervals of mediation and moderated mediation in Mplus 8. To alleviate the multicollinearity problem, ...
We tested the continuous predictor variables a priori to verify that there was no violation of the assumption of the linearity of the logit and multicollinearity. The tidy LPA package [42] working in the RStudio 2022.07.2 (R 4.1.2) environment was used to conduct the LPA, while SPSS 28 ...
checkforheteroskedasticity,normality,autocorrelation,andmulticollinearityaswellaploretechniquesfor variableselectionanddevelopspeciallyconstructedvariables. 1.Introduction Thispaperdiscussesthedevelopmentofamultivariateregressionmodeltopredicttheretailpriceof2005 GeneralMotor(GM)cars.Statisticaltextbookstypicallyoffermanysmalldataset...
First, we examined the relationships between values and food consumption by including each value variable separately as a predictor in logistic regression analysis to avoid multicollinearity. We conducted analyses for six different outcome variables: consumption of red meat, dairy, legumes, plant-based ...