I’d like to understand what accounts for the differences I see. I’m using v26.0.0.0 if that matters.
Consider these two models:
mixed behavior with wave txgroupn/fixed wave wave*wave txgroupn/
print solution/random intercept wave  subject(rid) covtype(un).
mixed behavior by txgroupnrev with wave/fixed wave wave*wave txgroupnrev/
print solution/random intercept wave  subject(rid) covtype(un).
The first model is the standard covariate setup. Txgroupn is a dichotomous variable coded 0, 1. Wave is the time coding for a growth curve.
The second model makes txgroupn a factor and so that the correct group is the reference, txgroupn is reversed coded as txgroupnrev.
My current understand says the results ought to be the same. But they’re not.
Type III tests fixed effect for model 1 then model 2
Type III Tests of Fixed Effects^{a}

Source

Numerator df

Denominator df

F

Sig.

Intercept

1

165.400

1006.201

.000

wave

1

87.621

8.105

.005

wave * wave

1

74.521

4.831

.031

txgroupn

1

82.545

.042

.838

a. Dependent Variable: Behavior.

Type III Tests of Fixed Effects^{a}

Source

Numerator df

Denominator df

F

Sig.

Intercept

1

138.703

1324.826

.000

wave

1

87.621

8.105

.005

wave * wave

1

74.521

4.831

.031

txgroupnrev

1

82.545

.042

.838

a. Dependent Variable: Behavior.

Now the estimates table for model 1 then model 2
Estimates of Fixed Effects^{a}

Parameter

Estimate

Std. Error

df

t

Sig.

95% Confidence Interval

Lower Bound

Upper Bound

Intercept

3.869160

.121976

165.400

31.721

.000

3.628329

4.109990

wave

.059161

.020781

87.621

2.847

.005

.017861

.100460

wave * wave

.002448

.001114

74.521

2.198

.031

.004667

.000229

txgroupn

.023950

.117075

82.545

.205

.838

.256826

.208926

a. Dependent Variable: Behavior.

Estimates of Fixed Effects^{a}

Parameter

Estimate

Std. Error

df

t

Sig.

95% Confidence Interval

Lower Bound

Upper Bound

Intercept

3.869160

.121976

165.400

31.721

.000

3.628329

4.109990

wave

.059161

.020781

87.621

2.847

.005

.017861

.100460

wave * wave

.002448

.001114

74.521

2.198

.031

.004667

.000229

[txgroupnrev=0]

.023950

.117075

82.545

.205

.838

.256826

.208926

[txgroupnrev=1]

0^{b}

0

.

.

.

.

.

a. Dependent Variable: Behavior.

b. This parameter is set to zero because it is redundant.

The intercept df for the F and the F itself differ but the dfs and Fs for the other effects don’t. I’d always understood that F = t*t. Well, 1006.201 is not quite 31.721 squared but squaring works for the other parameters.
But on a model with another binary predictor beside txgroupn, the intercept and wave dfs and their Fs differ even though the estimates tables don’t.
This could just be something I don’t understand, if so I’d like to understand better. If not my understanding, is this fixed in a patch?
Thanks, Gene Maguin
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