Determining whether there is multicollinearity is an important step in multinomial logistic regression. Unfortunately, this is an exhaustive process in SPSS Statistics that requires you to create any dummy variables that are needed and run multiple linear regression procedures. Assumption #5: There needs...
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...
it tends to diminish multicollinearity, especially between the interaction effect and its constituent main effects; it may render our b-coefficients more easily interpretable.We'll cover an entire regression analysis with a moderation interaction in a subsequent tutorial. For now, we'll focus on...
Factor analysis doesn’t operate in isolation – it’s often used as a stepping stone for further analysis. For example, once you’ve identified key factors through factor analysis, you might then proceed to acluster analysis– a method that groups your customers based on their responses to th...
Feed in the collected data in a statistical program to come up with fewer factors that are a representation of all the important attributes enlisted above. SPSS is the most commonly deployed software for factor analysis. Further utilize these factors for building perceptual maps, product positioning...
line plot. However, when you have more than one independent variable, you can’t use a fitted line plot and you’ll need torely on residual plots to check the regression assumptions. For our data, the residual plots display the nonrandom patterns very clearly. You want to see random...
The variance inflation factor indices of both quality and structural parameters were below 1.5 (Table 1), indicating that the analysis was not affected by the multicollinearity. The least squares regression analysis revealed that both mineral density and AGEs content were important quality parameters acc...
Related post:How High Does R-squared Need to Be? If you can find legitimate predictors, that can work in some cases. However, for every study area there is an inherent amount of unexplainable variability. For instance, studies that attempt to predict human behavior generally have R-squared va...
If the correlations are low, you might be better off running separate one-way ANOVAs, and if the correlation(s) are too high (greater than 0.9), you could have a multicollinearity problem. This is problematic for MANOVA and needs to be screened out....
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