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How to get rid of Multicolinearity Issues?

Research in any aspect can prove out to be significant if it is carried out based on the analysis of real world data and information. If the superior quality journal and conference papers in the field of management research are being looked at, then it can be seen that almost all of those papers are empirical in nature. Devoid of a detailed and rigorous empirical analysis, it is not possible to come up with a quality research paper, which can be implemented in a policy implication purpose. There are more than a few ways to carry out an empirical analysis on a particular research topic. However, researchers are sometimes being encountered with several issues regarding the data to be used in the analysis, and devoid of cleaning the data with a view to getting rid of those issues it is not possible to carry out the empirical analysis, or to come up with any significant results. One of these issues is the multicolinearity among the dependent or explanatory variables. This issue occurs, when the explanatory variables are highly correlated to each other.

There are majorly two ways to get rid of the issues regarding multicolinearity in the data. Following are those two ways mostly used by the researchers across the globe:

  • Most of the researchers try to go for principal component analysis or factor analysis, where they try to combine the mostly correlated explanatory variables to form one or two variables, so that those newly formed variables or factors can explain the variances to the best manner possible.
  • Next transformation technique being used by the researchers is orthogonal transformation, in which the entire matrix of explanatory variables is orthogonally transformed, so that entire presence of correlations can be removed.

For more information about reasons behind the occurrence of issues like multicolinearity and the possible solutions about those, kindly browse through the pages of www.thesishelpindia.com.

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