I have noticed that the sum of squares in my models can change fairly radically with even the slightest adjustment to my models???? Is this normal???? I'm using SPSS 16, and both models presented below used the same data and variables with only one small change - categorizing one of the variables as either a 2 level or 3 level variable.

Details - using a 2 x 2 x 6 mixed model ANOVA with the 6 being the repeated measure i get the following in the between group analysis

------------------------------------------------------------
Source    | Type III SS |  df  |  MS       |  F      |  Sig
------------------------------------------------------------
intercept | 4086.46     |  1   | 4086.46   | 104.93  |  .000
X         |  224.61     |  1   |  224.61   |   5.77  |  .019
Y         |    2.60     |  1   |    2.60   |    .07  |  .80
X by Y    |   19.25     |  1   |   19.25   |    .49  |  .49
Error     | 2570.40     | 66   |   38.95   | 

Then, when I use the exact same data but a slightly different model in which variable Y has 3 levels instead of 2 levels I get the following

------------------------------------------------------------
Source    | Type III SS |  df  |  MS       |  F      |  Sig
------------------------------------------------------------
intercept | 3603.88     |  1   | 3603.88   |  90.89  |  .000
X         |  171.89     |  1   |  171.89   |   4.34  |  .041
Y         |   19.23     |  2   |    9.62   |    .24  |  .79
X by Y    |   17.90     |  2   |   17.90   |    .80  |  .80
Error     | 2537.76     | 64   |   39.65   | 

I don't understand why variable X would have a different sum of squares simply because variable Y gets devided up into 3 levels instead of 2. This is also the case in the within groups analysis too.

Please help me understand :D

Thank you in advance

Pat