If you mean that all 4 categories were available for the first model, but the

4th category just was not used, fair enough. In that case, I think you can

solve your problem by using INTEGER mode on your CROSSTABS command.

* McNemar-Bowker test for marginal homogeneity with non-square table.

DATA LIST FREE / res1980 res1985 wt .

BEGIN DATA.

1 1 11607

1 2 100

1 3 366

1 4 124

2 1 87

2 2 13677

2 3 515

2 4 302

3 1 172

3 2 225

3 3 17819

3 4 270

4 1 63

4 2 176

4 3 286

4 4 10192

END DATA.

WEIGHT BY wt.

* Create the symmetry and quasi-symmetry indicator variables .

COMPUTE s1 = any(1,res1980,res1985).

COMPUTE s2 = any(2,res1980,res1985).

COMPUTE s3 = any(3,res1980,res1985).

COMPUTE qs12 = s1*s2 .

COMPUTE qs13 = s1*s3 .

COMPUTE qs14 = s1*any(4,res1980,res1985).

COMPUTE qs23 = s2*s3 .

COMPUTE qs24 = s2*any(4,res1980,res1985).

COMPUTE qs34 = s3*any(4,res1980,res1985).

FORMATS s1 to qs34 (F4).

* Symmetry model - reported in Agresti, page 357 .

GENLOG

res1980 res1985 WITH s1 s2 s3 qs12 qs13 qs14 qs23 qs24 qs34

/PRINT FREQ ESTIM

/PLOT NONE

/DESIGN qs12 qs13 qs14 qs23 qs24 qs34 s1 s2 s3 .

CROSSTABS RES1980 by RES1985 /STATISTICS=MCNEMAR.

* RECODE RES1980 so that all 4s become 3s.

RECODE RES1980 (4=3) (ELSE=COPY) into RES1980r.

* Now redo the CROSSTABS command--but note that we need

* to use INTEGER mode with a non-square table.

CROSSTABS VARIABLES = RES1980r (1,4) RES1985(1,4)

/TABLES = RES1980r by RES1985 /STATISTICS=MCNEMAR.

* References.

*

http://spssx-discussion.1045642.n5.nabble.com/MxN-variation-of-McNemar-Bowker-test-td5735446.html#a5735452*

http://www-01.ibm.com/support/docview.wss?uid=swg21477019.

rsijben wrote

> Thank you for your suggestions,

>

> Considering how each case is assigned to 1 category per model I created

> the

> following long format:

>

> 1 Column for case/subject number, each case listed twice as we're

> comparing

> 2 models.

> 1 Column listing model number 1 or 2, used as repeated measure.

> 1 Column listing the assigned category for each case by the respective

> model.

>

>

> Inputting this to the multinomial logistic under generalized linear grants

> the error: "Repeated measures analysis is not supported for the

> multinomial

> probability distribution".

>

>

> @Mr. Weaver,

>

> The first model assigns each case to 1 of 3 categories while the 2nd model

> assigns these cases to 4 categories. Model 1 could, for example, assign

> the

> data evenly over the conditions yielding a proportion of 0.33 for each

> category. Model 2, being more optimized, might recognize that half the

> cases

> in category 3 actually belong in a new category 4, creating the

> proportions

> 0.33, 0.33, 0.1565, 0.1565 for categories 1, 2, 3, and 4, respectively.

> The

> proportion of cases assigned to category 1 and 2 would remain identical.

>

>

>

>

>

> --

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