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sasnewbie12
Obsidian | Level 7

 

Why is that when I categorize a variable in logistic regression by making it binary at the 75th percentile cutoff, it makes Variable 2 which was previously significant into non-significant. Then, when I change the categorization to binary while using an outlier number much greater than the 75th percentile as the cut off , Variable 2 then becomes significant again?

 

For example

 

1) model event1= variable 1(continuous), variable 2(categorical)

 - variable 1 is significant, variable 2 is significant

 

2) model event1= variable 1 (categorical at 75th percentile), variable 2(categorical)

   - variable 1 is significant, variable 2 becomes non-significant

 

3) model event1= variable1 (categorical at outlier point, much greater than 75th percentile), variable 2(categorical)

 - variable 1 is significant, variable 2 is again significant

1 ACCEPTED SOLUTION

Accepted Solutions
Reeza
Super User

You changed a variable and the model changed?

That’s to be expected. THis is almost a good example of why it’s not a good idea to categorize data. 

 

Categorizing a continuous variable suddenly means that 10 and 11 can be entirely separate categories where the weren’t previously.

 

I would do some cross tabs (Variable1*outcome) and variable2*outcome to see what happens with the outcome. Knowing your data will help to understand why this is happening. 

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Community_Guide
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ballardw
Super User

Code would tell us which options might have an effect.

You also might provide examples of the two sets. It may be interesting to see how you accomplish "making it binary at the 7th percentile cutoff".

 

But it sounds like you are surprised that you change the data or the model and get different results. That is generally not uncommon.

 

Reeza
Super User

You changed a variable and the model changed?

That’s to be expected. THis is almost a good example of why it’s not a good idea to categorize data. 

 

Categorizing a continuous variable suddenly means that 10 and 11 can be entirely separate categories where the weren’t previously.

 

I would do some cross tabs (Variable1*outcome) and variable2*outcome to see what happens with the outcome. Knowing your data will help to understand why this is happening. 

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