Analysing 2-factor 2-level design as if it were 4 different treatment design

gust

New Member
#1
Hello!

Just curious why we see different results in such cases.

Here we have a classic 2-factor 2-level factorial design which we can analyze using Nominal regression/logistic regression.



Analysis shows that only interaction is significant :



However, if we transform this data as if there were 1 factor 4-level A1, B1, C (A1*B1), D (A0*B0),

then we will see that main effects are significant.
1613500562935.png


How can we explain and interpret this?


PS
Datasets are in attachments. These results are from real experiment.
 

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