Learn about factor analysis - a simple way to condense the data in many variables into a just a few variables.
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....
To avoid multicollinearity in the regression analyses, any independent variables that were strongly correlated to other predictors were not included in regression models; specific information on this aspect is included below. Results Table 1, which summarises the descriptive statistics for the use of ...
Why is endogeneity a problem for inference in regressions? Which of the following is not a reason why multicollinearity a problem in regression? a. It limits the size of r. b. It makes it difficult to assess the importance of individual predictors. c. ...
Therefore, in our enhanced moderator analysis 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...
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 ...
Resilience has been found to have positive impacts on college students’ well-being and mental health. However, we still lack knowledge on how and und
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, ...
Multicollinearity was assessed by inspection of the variance inflation factors and tolerance statistics [32]. In addition to basic descriptive statistics, correlation analysis and t-tests to compare means, stepwise hierarchical regression analysis was performed to examine the predictive values of the inde...
The two predictors were scale-centered to reduce multicollinearity and facilitate interpretation of the coefficients on the x–y plane, where the origin of the x-axis and y-axis is located (Edwards, 1994). Then, we created three new variables per OC dimension: the square of the centered ...