How to Test Data for Assumptions of Binary Logistic Regression in SPSS

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In this video, I demonstrated how to test data for assumptions of Binary Logistic Regression in SPSS.
To have a valid result of Binary Logistic Regression analysis, it is expected that your data satisfies all the parametric assumptions before you can perform the analysis. So, in this video, I demonstrated “How To Perform the Test”.
The links to the videos that I referred to during the demonstration are listed below:
1. Test of Multicollinearity of Data in SPSS: • Test of Multicollinear...
2. Identify Outliers using Tukey’s Hinges Approach in SPSS: • Identify Outliers usin...
3. Identify Outliers using Stem-and-Leaf and Box Plots in SPSS: • Identify Outliers usin...
4. Identify Outliers using Scattered Plots in SPSS: • Identify Outliers usin...
The content of this video is detailed, thorough and comprehensive, and the demonstration has been simplified to the grassroots. So, you are kindly advised to see or watch this video to the end. Please, kindly share the link of this video for others to see and also benefit.
For more details and help for data analysis, reach out to me on: asktitocan@gmail.com
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Опубликовано:

28 мар 2024

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Комментарии : 9

@plakendeKut 13 дней назад

Thank you! Would these assumptions all be the same as well for a penalized Firth logistic regression?

@TitocanMarkSolutions 13 дней назад

@@plakendeKut Thanks for your comment. Please note that every parametric test has specific assumptions associated with it. Some assumptions are common among the Parametric test. So, the assumptions of Binary Logistic Regression may have some common assumptions. But, not all.

@hzlivj 4 месяца назад

Hello! Thanks so much for uploading. What should I do if an assumption isn't followed? Should I completely exclude the variable from the model? Or if there are outliers, but less than 10 in a dataset with more than 20,000 responses, should I also exclude these outliers?

@TitocanMarkSolutions 4 месяца назад

The basic advice will be to exclude the affected variables from the dataset. However, this action may be wise to do if and only if there are sufficient variables (not respondents, factors or cases) in your dataset, otherwise transformation of the affected variables can be a solution if you do not have sufficient variables in your dataset. I think I should develop a tutorial video on what to do if your data violates the parametric assumptions. Thank you.

@hzlivj 4 месяца назад

@@TitocanMarkSolutions Hello! Thank you so much for your advise. Please develop a tutorial video on what to do if the data set violates the assumptions especially for the last 3 assumptions. I'm looking forward to it!

@hzlivj 3 месяца назад

@@TitocanMarkSolutions Hello, when will you have the new video regarding what to do when the data violates the assumptions?