I have 4 factors: Organisation, colour, display size and accuracy. They all have two levels, resulting in 16 combinations.

There are equal numbers in every condition, except for the accuracy. There are always 60 trials in each combination of factors, of which a proportion were correct, and the others were wrong. This means, according to me, that the model can only come up with terms that interact with accuracy in order to explain the results.

The data looks like this:

Org Col Size Acc Freq

1 1 1 1 36

1 1 1 2 24

1 1 2 1 6

1 1 2 2 54

1 2 1 1 52

1 2 1 2 8

1 2 2 1 8

1 2 2 2 52

2 1 1 1 40

2 1 1 2 20

2 1 2 1 6

2 1 2 2 54

2 2 1 1 55

2 2 1 2 5

2 2 2 1 26

2 2 2 2 34

However, when I ran a hierarchical loglinear model on it in SPSS (v13.0 for windows), these are my results :

The highest order terms, according to my model, are :

size * correct

colour * size

org * correct

colour * correct

I keep getting an interaction between colour and size, which, according to me, simply cannot be there. How can I explain this? Can anyone run this through another statistical program and see if they come up with the same result?

Thanks in advance