I have a research project with seven predictor variables that have relatively high correlations with each other. Also, the actual multicollinearity statistics are very poor.
Unfortunately, qualitative analyses led us to these quantitive tests, so removing variables is not ideal. Please let me know if you know of any methods to negate this issue.
Dear Daniele,
I don't think that you should be hoping to eliminate this issue. These variables are important, so why are you letting the limitations of statistical technique drive your question?
I will support the recommendation of Rimantas in that many researchers will map these predictors onto a latent variable and work from there. The difference between this and simply averaging together the scores depends more or less on the structure of covariance between these predictors; you only work this out by trying it both ways.
Given that these variables are arising from quantitative analyses you may also decide to simply dichotomize them and build a predictor which is the number of these indicators which is present. This might be more honest then the latent variable approach because the latter requires (but this is rarely done) some verification about what is actually common between a set of variables.
Dear Daniele, let me ask you - what is the final objective of your project? Will you focus on outcome (prediction) or on process itself (for example studying impact of individual variables)? Because for prediction, multicollinearity does not have to be such big issue. A nice comment has been recently done in this study - https://www.toi.no/getfile.php/Publikasjoner/T%C3%98I%20rapporter/2015/1403-2015/1403-2015-elektronisk.pdf (chapter 13 - Don’t worry about multicollinearity).
I read your question one week ago. I was writing a paper related to your questions. I finish to wright a paper and spend many hours to look for your questions again. RG should offer the function to find by key-word.
Below is the conclusion. This paper will be upload next March. But, you can download about CPD data after 2014. I compare three approach such to analyze the original data, analyze after PCA (approach 1) and delete some variables relate to collinearity (approach 2). The best result is approach 2 and the worst one is approach 1. See my papers about CPD data after 2014.
**********************************************************
5. Conclusion
In this research, we discuss the new method and model selection procedure of the discriminant analysis using the CPD data with collinearities. Our examination tells us that the second approach (to exclude the variables relate to collinearities) is the best, and the first approach (bundle of PCA and regression analysis) is the worst. The common consensus that we had better regressed the data with collinearity using new variables by PCA may be wrong. We must examine this fact in other cases if we can access other data with collinearities.
**************************************:
**********************:
- Badges
- Science method
- Similar topics
- Mathematics
- Statistics
- Statistical Techniques
- Structural Equation Modeling