You have heard about multicollinearity, but don’t understand the meaning and its processes. In this post, let us understand multicollinearity and its methodology. Moreover, multicollinearity test SPSS will be tackled, too. You can also check manova spss output interpretation or spss test for linearity if you need.
What Is Multicollinearity?
If two or more predictor variables are interrelated in a multiple regression, that is multicollinearity. Multicollinearity is considered a phenomenon, which linearly foreseen from the rest with a nontrivial point of accuracy. Changes in the multiple regressions’ coefficient estimates are erratic, responding to minimal changes in the data or model.
The multicollinearity’s reliability or predictive power is not reduced as a whole but within the data set sample. Instead, the individual predictor calculations are affected.
In simple term, multicollinearity is redundancy. This is one of the complex statistical processes that everyone should interest in.
Multicollinearity Detection
Multicollinearity can be presented in a model. A linearity test SPSS can also be conducted. Below are indicators to determine if multicollinearity is allowed for a model.

Estimated regression coefficients have large changes when added or deleted with a predictor variable.

Affected variables of insignificant regression coefficients in the multiple regressions, but a joint hypothesis rejection that coefficients are all zero.

Multivariable regression’s multicollinearity when an insignificant coefficient is found, but simple linear regression shows a difference from zero.

Farrar Glauber test

Condition number test
Multicollinearity Example
With the complexity of the term multicollinearity, providing samples are better way to understanding. One of the simplest and useful examples is having both weight and height as a regression model predictors. These values may present diverse aspects but are both measurement and related. How? Smaller people usually weigh less than taller people do. This is a case to case scenario, but fashion models aside; this results in a problem when input together in a model.
Multicollinearity Consequences
In statistics, multicollinearity has two fold problems. Determining the consequences are useful in identifying the solution for the loopholes.

Your model’s individual predictor in the estimate of effects losses reliability.

Nonsense, misleading, and strange findings.
With the above issues, you should care.
Below are warning signs of multicollinearity to take care of:
 Variable correlated with Y is insignificant with the regression coefficient
 Changes in the regression coefficients when adding or deleting an X variable
 Negative regression coefficient instead of increasing
 Positive regression coefficient instead of decreasing
 High pairwise correlations for X variables
Dealing with Multicollinearity
Defining multicollinearity is a good jumpstart, but identifying what it is a problem is critical. With the complexity of the term multicollinearity, providing samples are better way to understanding. One of the simplest and useful examples is having both weight and height as a regression model predictors.
These values may present diverse aspects but are both measurement and related. How? Smaller people usually weigh less than taller people do. This is a case to case scenario, but fashion models aside; this results in a problem when input together in a model.
Dealing with multicollinearity problems are simple, especially if the VIF factor is above or near 5:

Eliminate from the model highly correlated predictors. Remove one VIF from the model because they provide redundant information. This may not reduce the Rsquared, so consider using best subsets regression, stepwise regression, or specialized knowledge of the data.

Use Principal Components Analysis or Partial Least Squares regression (PLS) that reduces the predictor numbers to a minimal set of uncorrelated components.