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Posted 09-10-2020 11:22 PM
(555 views)

What are the respective codes would be to get these results given below?

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What share of these students took both tests? And do you know the id's of those students. If there are a large number of them, you shold calculate the week4-week1 differences for each of those students. Then do an analog of this syntax with your sample data (using the pottery example data in my earlier post). Only in this case, the dependent variables would be the test-score * differences*, not the scores themselves.

```
proc glm data=pottery;
model Al Fe Mg Ca Na = ;
manova h=_all_ / printe printh;
run;
quit;
```

I believe this produces a manova test against the null hypothesis of no multivariate change in the 5 outcome measures. SAS labels it as

MANOVA Test Criteria and Exact F Statistics for the Hypothesis of

No Overall Intercept Effect

But if each of your students took only one test, then you can't do student-specific changes. Instead, you would need a CLASS statement for the "week-of-test" (or whatever you name it) variable, and make that variable the predictor in the model statement.

All this presumes you have a multivariate measure of anxiety.

And I guess if you have a substantial number of students who took both tests, and of students who took only one test, then you would have to run two manova's.

--------------------------

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3 REPLIES 3

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This is a multivariate anova run under PROC GLM. It uses the manova statement, which is documented in the MANOVA Statement section in the chapter on PROC GLM. If you click on the "details" tab, you'll get a link to the Multivariate Analysis of Variance page which lists the statistics you show in your example.

Go to the Examples tab, and you can play with this SAS sample code for manova. It's Example 50.6 which I've copied below:

```
title "Romano-British Pottery";
data pottery;
input Site $12. Al Fe Mg Ca Na;
datalines;
Llanederyn 14.4 7.00 4.30 0.15 0.51
Llanederyn 13.8 7.08 3.43 0.12 0.17
Llanederyn 14.6 7.09 3.88 0.13 0.20
Llanederyn 11.5 6.37 5.64 0.16 0.14
Llanederyn 13.8 7.06 5.34 0.20 0.20
Llanederyn 10.9 6.26 3.47 0.17 0.22
Llanederyn 10.1 4.26 4.26 0.20 0.18
Llanederyn 11.6 5.78 5.91 0.18 0.16
Llanederyn 11.1 5.49 4.52 0.29 0.30
Llanederyn 13.4 6.92 7.23 0.28 0.20
Llanederyn 12.4 6.13 5.69 0.22 0.54
Llanederyn 13.1 6.64 5.51 0.31 0.24
Llanederyn 12.7 6.69 4.45 0.20 0.22
Llanederyn 12.5 6.44 3.94 0.22 0.23
Caldicot 11.8 5.44 3.94 0.30 0.04
Caldicot 11.6 5.39 3.77 0.29 0.06
IslandThorns 18.3 1.28 0.67 0.03 0.03
IslandThorns 15.8 2.39 0.63 0.01 0.04
IslandThorns 18.0 1.50 0.67 0.01 0.06
IslandThorns 18.0 1.88 0.68 0.01 0.04
IslandThorns 20.8 1.51 0.72 0.07 0.10
AshleyRails 17.7 1.12 0.56 0.06 0.06
AshleyRails 18.3 1.14 0.67 0.06 0.05
AshleyRails 16.7 0.92 0.53 0.01 0.05
AshleyRails 14.8 2.74 0.67 0.03 0.05
AshleyRails 19.1 1.64 0.60 0.10 0.03
;
proc glm data=pottery;
class Site;
model Al Fe Mg Ca Na = Site;
contrast 'Llanederyn vs. the rest' Site 1 1 1 -3;
manova h=_all_ / printe printh;
run;
```

--------------------------

The hash OUTPUT method will overwrite a SAS data set, but not append. That can be costly. Consider voting for Add a HASH object method which would append a hash object to an existing SAS data set

Would enabling PROC SORT to simultaneously output multiple datasets be useful? Then vote for

Allow PROC SORT to output multiple datasets

--------------------------

The hash OUTPUT method will overwrite a SAS data set, but not append. That can be costly. Consider voting for Add a HASH object method which would append a hash object to an existing SAS data set

Would enabling PROC SORT to simultaneously output multiple datasets be useful? Then vote for

Allow PROC SORT to output multiple datasets

--------------------------

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If I wanted to see if students’ math anxiety decreased from the beginning of class to the end. 386 students were tested in week 1 and 342 students in week 4.

How do I tell if there is a change in anxiety from time 1 to time 2?

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What share of these students took both tests? And do you know the id's of those students. If there are a large number of them, you shold calculate the week4-week1 differences for each of those students. Then do an analog of this syntax with your sample data (using the pottery example data in my earlier post). Only in this case, the dependent variables would be the test-score * differences*, not the scores themselves.

```
proc glm data=pottery;
model Al Fe Mg Ca Na = ;
manova h=_all_ / printe printh;
run;
quit;
```

I believe this produces a manova test against the null hypothesis of no multivariate change in the 5 outcome measures. SAS labels it as

MANOVA Test Criteria and Exact F Statistics for the Hypothesis of

No Overall Intercept Effect

But if each of your students took only one test, then you can't do student-specific changes. Instead, you would need a CLASS statement for the "week-of-test" (or whatever you name it) variable, and make that variable the predictor in the model statement.

All this presumes you have a multivariate measure of anxiety.

And I guess if you have a substantial number of students who took both tests, and of students who took only one test, then you would have to run two manova's.

--------------------------

The hash OUTPUT method will overwrite a SAS data set, but not append. That can be costly. Consider voting for Add a HASH object method which would append a hash object to an existing SAS data set

Would enabling PROC SORT to simultaneously output multiple datasets be useful? Then vote for

Allow PROC SORT to output multiple datasets

--------------------------

The hash OUTPUT method will overwrite a SAS data set, but not append. That can be costly. Consider voting for Add a HASH object method which would append a hash object to an existing SAS data set

Would enabling PROC SORT to simultaneously output multiple datasets be useful? Then vote for

Allow PROC SORT to output multiple datasets

--------------------------

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