i also need to ask if 17 predictors which came out to be significant in univariate analysis plus those who are theoretically hypothesized, are enough to enter into MV analysis for the sample size of 216. Meaning i will have 12 (216/17) cases for each predictor as i intend to use Backward elimination method to create my model. ???
You can diagnose multicollinearity based on Tolerance or Variance Inflation Factor.
The regression procedures for categorical dependent variables do not have collinearity diagnostics. However, you can use the linear Regression procedure for this purpose. Collinearity statistics in regression concern the relationships among the predictors, ignoring the dependent variable. So, you can run REGRESSION with the same list of predictors and dependent variable as you wish to use in LOGISTIC REGRESSION (for example) and request the collinearity diagnostics. Run Logistic Regression to get the proper coefficients, predicted probabilities, etc. after you've made any necessary decisions (dropping predictors, etc.) that result from the collinearity analysis.
If you have categorical predictors in your model, you will need to transform these to sets of dummy variables to run collinearity analysis in REGRESSION, titled "Recoding a categorical SPSS variable into indicator (dummy) variables", discusses how to do this.
You can diagnose multicollinearity based on Tolerance or Variance Inflation Factor.
The regression procedures for categorical dependent variables do not have collinearity diagnostics. However, you can use the linear Regression procedure for this purpose. Collinearity statistics in regression concern the relationships among the predictors, ignoring the dependent variable. So, you can run REGRESSION with the same list of predictors and dependent variable as you wish to use in LOGISTIC REGRESSION (for example) and request the collinearity diagnostics. Run Logistic Regression to get the proper coefficients, predicted probabilities, etc. after you've made any necessary decisions (dropping predictors, etc.) that result from the collinearity analysis.
If you have categorical predictors in your model, you will need to transform these to sets of dummy variables to run collinearity analysis in REGRESSION, titled "Recoding a categorical SPSS variable into indicator (dummy) variables", discusses how to do this.
Thank you Muayyad Ahmad. Actually i am using SPSS for this purpose, and you are saying i need to go to linear regression and use my categorical DV in it and list all the categorical and continuous predictors of interest as IV and run "collinearity diagnostics" ? Correct me if i am wrong.
Afterwards, the value of VIF for each predictor will be obtained so, how to interpret this now and choose the best predictors for MV analysis? Please clarify, thanks.
Tolerance Measures of collinearity
Proportion of variance in a variable that is not accounted for by the other independent variables
Each independent variable is regressed on the other independent variables
High multiple correlation indicates variable is highly related to other independent variables
Tolerance equals 1 - Rsquared
Tolerance of 0 (1-1) would indicate perfect collinearity
Small tolerances (
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