SPSSX Discussion

Value Labels with different Conditions

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Gad Chen

Value Labels with different Conditions

Hi, there,

I am trying to give label values with different conditions. But it is not
working. Can anyone give me any suggestion to solve this? Thanks

Gad

data list /CityID 1-3 NeighbourhoodID 5-8.

begin data

100 2000

100 2001

100 2002

200 2001

200 2002

end data.

do if (CityID=100).

value label

/NeighbourhoodID

2000 'C100 N2000'

2001 'C100 N2001'.

else.

value label

/NeighbourhoodID

2000 'C200 N2000'

2001 'C200 N2001'

2002 'C200 N2002'.

end if.

exe.

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ViAnn Beadle

Re: Value Labels with different Conditions

A value label is set to a value and not to a logical expression. How about
computing a new variable which takes into account NeighbourhoodID and cityID
and then label that variable?

-----Original Message-----
From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of Gad
Chen
Sent: Thursday, January 22, 2009 11:39 AM
To: [hidden email]
Subject: Value Labels with different Conditions

Hi, there,

I am trying to give label values with different conditions. But it is not
working. Can anyone give me any suggestion to solve this? Thanks

Gad

data list /CityID 1-3 NeighbourhoodID 5-8.

begin data

100 2000

100 2001

100 2002

200 2001

200 2002

end data.

do if (CityID=100).

value label

/NeighbourhoodID

2000 'C100 N2000'

2001 'C100 N2001'.

else.

value label

/NeighbourhoodID

2000 'C200 N2000'

2001 'C200 N2001'

2002 'C200 N2002'.

end if.

exe.

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Oliver, Richard

Re: Value Labels with different Conditions

In reply to this post by Gad Chen

Value Labels cannot be applied conditionally. You could, however, create a new string variable with the values you wanted to use as value labels.

-----Original Message-----
From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of Gad Chen
Sent: Thursday, January 22, 2009 12:39 PM
To: [hidden email]
Subject: Value Labels with different Conditions

Hi, there,

I am trying to give label values with different conditions. But it is not
working. Can anyone give me any suggestion to solve this? Thanks

Gad

data list /CityID 1-3 NeighbourhoodID 5-8.

begin data

100 2000

100 2001

100 2002

200 2001

200 2002

end data.

do if (CityID=100).

value label

/NeighbourhoodID

2000 'C100 N2000'

2001 'C100 N2001'.

else.

value label

/NeighbourhoodID

2000 'C200 N2000'

2001 'C200 N2001'

2002 'C200 N2002'.

end if.

exe.

=====================
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Valerie Guille-2

RM ANOVA-warning message

Hi all,

I hope someone will be able to answer my question.
I have to run a RM ANOVA in SPSS 3 groups w/ 2 repeated measures.
When I run the RM ANOVA in SPSS I have the following warning that
appears:

Box's Test of Equality of Covariance Matrices is not computed because
there are fewer than two nonsingular cell covariance matrices.
Subsets cannot be computed with alpha = .050

After that I have my W/I effect table as per usual, but I don't get my
B/W subject table. I understand that it is because of the warning, but I
don't understand what the warning means exactly and what is wrong w/ my
data.
What are my solutions to solve my problem?

Thanks in advance for your help.

Valerie

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Swank, Paul R

Re: RM ANOVA-warning message

It sounds like you have some dependencies in the repeated measures
within at least one of your groups. How many levels of the repeated
measures variables do you have

Paul R. Swank, Ph.D
Professor and Director of Research
Children's Learning Institute
University of Texas Health Science Center
Houston, TX 77038

-----Original Message-----
From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of
Valerie Guille
Sent: Thursday, January 22, 2009 4:30 PM
To: [hidden email]
Subject: RM ANOVA-warning message

Hi all,

I hope someone will be able to answer my question.
I have to run a RM ANOVA in SPSS 3 groups w/ 2 repeated measures.
When I run the RM ANOVA in SPSS I have the following warning that
appears:

Box's Test of Equality of Covariance Matrices is not computed because
there are fewer than two nonsingular cell covariance matrices.
Subsets cannot be computed with alpha = .050

After that I have my W/I effect table as per usual, but I don't get my
B/W subject table. I understand that it is because of the warning, but I
don't understand what the warning means exactly and what is wrong w/ my
data.
What are my solutions to solve my problem?

Thanks in advance for your help.

Valerie

=====================
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Jignesh Sutar

Re: Value Labels with different Conditions

In reply to this post by Oliver, Richard

This doesn't answer your question directly but the workaround might be
helpful?
DATASET CLOSE ALL.
NEW FILE.
OUTPUT CLOSE ALL.

data list /CityID 1-3 NeighbourhoodID 5-8.
begin data
100 2000
100 2001
100 2002
200 2001
200 2002
end data.

DO IF (CityID=100).
IF (NeighbourhoodID=2000) CityNeig=1.
IF (NeighbourhoodID=2001) CityNeig=2.
IF (NeighbourhoodID=2002) CityNeig=3.
ELSE.
IF (NeighbourhoodID=2001) CityNeig=4.
IF (NeighbourhoodID=2002) CityNeig=5.
END IF.

VALUE LABELS CityNeig
1 'C100 N2000'
2 'C100 N2001'
3 'C100 N2002'
4 'C200 N2001'
5 'C200 N2002'.
LIST.

2009/1/22 Oliver, Richard <[hidden email]>

> Value Labels cannot be applied conditionally. You could, however, create a
> new string variable with the values you wanted to use as value labels.
>
> -----Original Message-----
> From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of
> Gad Chen
> Sent: Thursday, January 22, 2009 12:39 PM
> To: [hidden email]
> Subject: Value Labels with different Conditions
>
> Hi, there,
>
>
>
> I am trying to give label values with different conditions. But it is not
> working. Can anyone give me any suggestion to solve this? Thanks
>
>
>
>
>
> Gad
>
>
>
>
>
> data list /CityID 1-3 NeighbourhoodID 5-8.
>
> begin data
>
> 100 2000
>
> 100 2001
>
> 100 2002
>
> 200 2001
>
> 200 2002
>
> end data.
>
>
>
>
>
> do if (CityID=100).
>
> value label
>
> /NeighbourhoodID
>
> 2000 'C100 N2000'
>
> 2001 'C100 N2001'.
>
> else.
>
> value label
>
> /NeighbourhoodID
>
> 2000 'C200 N2000'
>
> 2001 'C200 N2001'
>
> 2002 'C200 N2002'.
>
> end if.
>
> exe.
>
> =====================
> To manage your subscription to SPSSX-L, send a message to
> [hidden email] (not to SPSSX-L), with no body text except the
> command. To leave the list, send the command
> SIGNOFF SPSSX-L
> For a list of commands to manage subscriptions, send the command
> INFO REFCARD
>
> =====================
> To manage your subscription to SPSSX-L, send a message to
> [hidden email] (not to SPSSX-L), with no body text except the
> command. To leave the list, send the command
> SIGNOFF SPSSX-L
> For a list of commands to manage subscriptions, send the command
> INFO REFCARD
>

Bob Schacht-3

CATPCA Categories vs. conventional factor analysis

I am analyzing results from a pilot questionnaire, specifically a group of
13 questions, with 70 responses.
The data consists of Likert scale responses on a 5-point scale.

Conventional PCA with varimax rotation produces 3 components, each
explaining more than 10% of the variance. The rotated component matrix
sorts out the original questions nicely into 3 groups with approximately
the same number of questions. From these, I can easily chose one question
from each of the three groups, and add a fourth (question with the largest
standard deviation of responses, indicating the widest variability in response.

CATPCA, however, does not provide a rotated component matrix. The Iteration
history accounts for only 11 (%?) of the variance.
The three questions with the top 3 loadings on the first dimension are the
same 3 that loaded most highly on the conventional PCA, but none of the
questions loaded more highly on the second dimension than the first, so it
is not so clear how to define the second dimension.
I am just learning how to interpret CATPCA output, and some of the
terminology is unfamiliar to me.

My objective here is data reduction: With the conventional PCA, I can
see how to reduce the original set of 13 questions down to 4 questions
that seem to cover all the bases. But I'm not seeing how to do that with
the CATPCA results, which ought to be more appropriate, given the
measurement level.

What do you suggest?

Thanks in advance,

Bob Schacht

Robert M. Schacht, Ph.D. <[hidden email]>
Pacific Basin Rehabilitation Research & Training Center
1268 Young Street, Suite #204
Research Center, University of Hawaii
Honolulu, HI 96814

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Oliver, Richard

Re: Value Labels with different Conditions

In reply to this post by Jignesh Sutar

That's a bit labor intensive. You could try:

compute newvar=cityid*10000+neighbourhoodid.

If you want consecutive integers from 1 to n:

compute newvar=cityid*10000+neighbourhoodid.
autorecode newvar /into newvar2.
delete variables newvar.

________________________________

From: SPSSX(r) Discussion on behalf of J Sutar
Sent: Fri 1/23/2009 4:27 PM
To: [hidden email]
Subject: Re: Value Labels with different Conditions

This doesn't answer your question directly but the workaround might be
helpful?
DATASET CLOSE ALL.
NEW FILE.
OUTPUT CLOSE ALL.

data list /CityID 1-3 NeighbourhoodID 5-8.
begin data
100 2000
100 2001
100 2002
200 2001
200 2002
end data.

DO IF (CityID=100).
IF (NeighbourhoodID=2000) CityNeig=1.
IF (NeighbourhoodID=2001) CityNeig=2.
IF (NeighbourhoodID=2002) CityNeig=3.
ELSE.
IF (NeighbourhoodID=2001) CityNeig=4.
IF (NeighbourhoodID=2002) CityNeig=5.
END IF.

VALUE LABELS CityNeig
1 'C100 N2000'
2 'C100 N2001'
3 'C100 N2002'
4 'C200 N2001'
5 'C200 N2002'.
LIST.

2009/1/22 Oliver, Richard <[hidden email]>

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Oliver, Richard

Re: Value Labels with different Conditions

Forgot to add that the Autorecode solution automatically retains the original values as value labels.

________________________________

From: Oliver, Richard
Sent: Sat 1/24/2009 8:29 AM
To: J Sutar; [hidden email]
Subject: RE: Re: Value Labels with different Conditions

That's a bit labor intensive. You could try:

compute newvar=cityid*10000+neighbourhoodid.

If you want consecutive integers from 1 to n:

compute newvar=cityid*10000+neighbourhoodid.
autorecode newvar /into newvar2.
delete variables newvar.

________________________________

From: SPSSX(r) Discussion on behalf of J Sutar
Sent: Fri 1/23/2009 4:27 PM
To: [hidden email]
Subject: Re: Value Labels with different Conditions

This doesn't answer your question directly but the workaround might be
helpful?
DATASET CLOSE ALL.
NEW FILE.
OUTPUT CLOSE ALL.

data list /CityID 1-3 NeighbourhoodID 5-8.
begin data
100 2000
100 2001
100 2002
200 2001
200 2002
end data.

DO IF (CityID=100).
IF (NeighbourhoodID=2000) CityNeig=1.
IF (NeighbourhoodID=2001) CityNeig=2.
IF (NeighbourhoodID=2002) CityNeig=3.
ELSE.
IF (NeighbourhoodID=2001) CityNeig=4.
IF (NeighbourhoodID=2002) CityNeig=5.
END IF.

VALUE LABELS CityNeig
1 'C100 N2000'
2 'C100 N2001'
3 'C100 N2002'
4 'C200 N2001'
5 'C200 N2002'.
LIST.

2009/1/22 Oliver, Richard <[hidden email]>

Oliver, Richard

Re: Value Labels with different Conditions

And now that I've had more time to think about it, assuming cityid is always 3 digits and neighbourhoodid is always 4 digits, a solution that yields the desired value labels:

string newvar1 (a10).
compute newvar1=concat('C', string(cityid,f3), ' N', string(neighbourhoodid,f4)).
autorecode newvar1 /into newvar.
delete variables newvar1.

________________________________

From: Oliver, Richard
Sent: Sat 1/24/2009 8:32 AM
To: Oliver, Richard; J Sutar; [hidden email]
Subject: RE: Re: Value Labels with different Conditions

Forgot to add that the Autorecode solution automatically retains the original values as value labels.

________________________________

From: Oliver, Richard
Sent: Sat 1/24/2009 8:29 AM
To: J Sutar; [hidden email]
Subject: RE: Re: Value Labels with different Conditions

That's a bit labor intensive. You could try:

compute newvar=cityid*10000+neighbourhoodid.

If you want consecutive integers from 1 to n:

compute newvar=cityid*10000+neighbourhoodid.
autorecode newvar /into newvar2.
delete variables newvar.

________________________________

From: SPSSX(r) Discussion on behalf of J Sutar
Sent: Fri 1/23/2009 4:27 PM
To: [hidden email]
Subject: Re: Value Labels with different Conditions

This doesn't answer your question directly but the workaround might be
helpful?
DATASET CLOSE ALL.
NEW FILE.
OUTPUT CLOSE ALL.

data list /CityID 1-3 NeighbourhoodID 5-8.
begin data
100 2000
100 2001
100 2002
200 2001
200 2002
end data.

DO IF (CityID=100).
IF (NeighbourhoodID=2000) CityNeig=1.
IF (NeighbourhoodID=2001) CityNeig=2.
IF (NeighbourhoodID=2002) CityNeig=3.
ELSE.
IF (NeighbourhoodID=2001) CityNeig=4.
IF (NeighbourhoodID=2002) CityNeig=5.
END IF.

VALUE LABELS CityNeig
1 'C100 N2000'
2 'C100 N2001'
3 'C100 N2002'
4 'C200 N2001'
5 'C200 N2002'.
LIST.

2009/1/22 Oliver, Richard <[hidden email]>

Kooij, A.J. van der

Re: CATPCA Categories vs. conventional factor analysis

In reply to this post by Bob Schacht-3

>The Iteration history accounts for only 11 (%?) of the variance.

Is 11 in the Total VAF column of the Iteration history table?

This is the total eigenvalue then. The total percentage VAF then would be 11 divided by number of variables times 100, is 84%.

(the % VAF is given in the Model Summary table, that is, if there are no variables with missings for which Missing option is Exclude values; see below).

But if with conventional PCA you obtained about 30% VAF for 3 components, I don't think it is likely you obtained 84% VAF with CATPCA for 3 components. Treating likert items ordinal results in higher VAF then when treating the items numerical

(or equal; but lower is not possible), but usually the increase in VAF is not that big.

How many components (= dimensions in CATPCA terminology) did you specify for the CATPCA analysis?

If you want rotated CATPCA results, you can use the Save option to save the transformed variables and use them in conventional PCA.

The results of conventional PCA on the transformed variables are equal to the CATPCA results, but not if there are missings and missing option is Exclude values, which is the default; this is passive treatment of missings (cases with missing values are not excluded, missings are not imputed. Missing cells in the datamatrix are ignored in the computations, which is possible because CATPCA does not compute the solution from the correlation matrix but from the data itself). With the Exclue values Missing option, the transformed variables will have missings where the original variables have missings. With SPSS conventional PCA, the passive missing option is not available, thus the results will not be the same. So, if there are missings and you want to perform conventional PCA on the transformed variables, choose missing option Listwise deletion or Imputation in CATPCA.

With Missing option passive (Exclude values), there is no % VAFcolumn in the Model Summary table, because computing % VAF then is not possible. Also, with this missing option

loadings are not correlations of the transformed variables with the component (object) scores (it is possible to have loadings with absolute value > 1).

Regards,

Anita van der Kooij

Data Theory Group

Leiden University

________________________________

From: SPSSX(r) Discussion on behalf of Bob Schacht
Sent: Sat 24-Jan-09 01:46
To: [hidden email]
Subject: CATPCA Categories vs. conventional factor analysis

I am analyzing results from a pilot questionnaire, specifically a group of
13 questions, with 70 responses.
The data consists of Likert scale responses on a 5-point scale.

Conventional PCA with varimax rotation produces 3 components, each
explaining more than 10% of the variance. The rotated component matrix
sorts out the original questions nicely into 3 groups with approximately
the same number of questions. From these, I can easily chose one question
from each of the three groups, and add a fourth (question with the largest
standard deviation of responses, indicating the widest variability in response.

CATPCA, however, does not provide a rotated component matrix. The Iteration
history accounts for only 11 (%?) of the variance.
The three questions with the top 3 loadings on the first dimension are the
same 3 that loaded most highly on the conventional PCA, but none of the
questions loaded more highly on the second dimension than the first, so it
is not so clear how to define the second dimension.
I am just learning how to interpret CATPCA output, and some of the
terminology is unfamiliar to me.

My objective here is data reduction: With the conventional PCA, I can
see how to reduce the original set of 13 questions down to 4 questions
that seem to cover all the bases. But I'm not seeing how to do that with
the CATPCA results, which ought to be more appropriate, given the
measurement level.

What do you suggest?

Thanks in advance,

Bob Schacht

Robert M. Schacht, Ph.D. <[hidden email]>
Pacific Basin Rehabilitation Research & Training Center
1268 Young Street, Suite #204
Research Center, University of Hawaii
Honolulu, HI 96814

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Poling, Taylor Leigh

combining survey data from multiple years

I have survey data taken from two separate years. I want to test whether I can combined data from these 2 years in order to increase my N. I am assuming that if I can show that the variance/covariance matrix are equivalent, and the means of the continuous variables are not significant in a MANOVA, I will have support. However, I have many categorical variables and am not sure how to test whether the trends of responses for these variables are similar. Any suggestions will be greatly appreciated.

Thanks,
Taylor

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