Hello!
Just curious why we see different results in such cases.
Here we have a classic 2factor 2level 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 4level A1, B1, C (A1*B1), D (A0*B0),
then we will see that main effects are significant.
How can we explain and interpret this?
PS
Datasets are in attachments. These results are from real experiment.
Just curious why we see different results in such cases.
Here we have a classic 2factor 2level 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 4level A1, B1, C (A1*B1), D (A0*B0),
then we will see that main effects are significant.
How can we explain and interpret this?
PS
Datasets are in attachments. These results are from real experiment.
Attachments

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