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Test of Multicollinearity of Data in SPSS
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- čas přidán 17. 04. 2022
- In this video, I demonstrated how to perform Test of Multicollinearity of data in SPSS, and also described in details how to interpret the outcome. Honestly, it is important that you see this video to the end, because this video contains information you required to know about Multicollinearity and how the results are interpreted.
For the response variable of your multiple regression model to have valid output, it is important to ensure your data meets the Multicollinearity assumption, so as to be tenable, and to make that possible, you need to see this video.
A detailed preamble was first given and then the demonstration in SPSS was performed. Also, list of what you can do to your dataset should your data not be tenable are highlighted in this video.
If you wish to contact me directly for business purposes, such as SPSS Tutorial, or data analysis, please use this address:
asktitocan@gmail.com
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Thank you very good 💯
Thank you very much. Interesting
Fantastic teaching. Very clear and easy to understand. Thank you @titocan
In deed wonderful. I like the pace too
Which condition is best to test multicollinearity?if one condition is met is important to proceed with analysis?like in this case the condition index wasn't met but three others were met and at the end of the video u say we can proceed to analysis. Otherwise am learning alot here, u r really helping
@@jesterkalumba2660
Multicollinearity is just one of the assumptions that needs to be met, and it is on the basis of this topic that i said "you can proceed to perform the analysis if the multicollinearity assumption is met". Of course, all the other required assumptions must be satisfied before your data can become tenable for the analysis. I took out Multicollinearity to discuss just the way I spoke about normality in one of my videos. Thanks for your concern, and my apologies for the late response.
hi titocan mark, I did a multicollinearity check via the methods described and it showed up us:
during bivariate correlation testing, Pearson is 0.692 (p suggesting moderate to almost strong correlation
but during linear regression with correlation diagnostics check, it shows VIF of 1.92 and Tolerance of 0.52.
how shall i interpret the results - are the 2 variables correlated or not correlated?
also, does correlation refer to colinearity? thank you
how to know what variable to remove if there problem with 2 variable
It depends on what you are checking for and which variable comes up badly. However, for two variables, a correction may be preferred to removal of any variable. Otherwise, you might have to return to the field to gather the data again.
What are some of behaviours in SPSS that shows multicollinearity is present? Leave alone performing the test in the SPSS??
I have posted a separate video on how to test for Multicollinearity in SPSS. Please, go to my CZcams Channel to see the video and many more that may interested you.
My CZcams Channel is "Titocan Mark Solutions".
Thanks for your interest.