SPSS Python Integration Plug-in - v.16.0.2

[Joel_Rivard]

Hi everyone,

 

Just wondering if a Python Integration Plug-in for SPSS 16.0.2 has been created. I was on the SPSS Developer Central website looking for it but couldn't find it.  I'd like to update some of our computers here to the new patched up version (16.0.2), but there doesn't appear to be an Integration plug-in for this version of SPSS.

 

Joel

 

                         

Joël Rivard

Data Technician

Maps, Data & Government Information Centre

Carleton University Library

[phone] ext.1685

[hidden email]

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[Peck, Jon]

The 16.0.1 plugin works with 16.0.2.  The plugin was not updated as part of the general 16.0.2 patch.

HTH,
Jon Peck

-----Original Message-----
From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of Joel_Rivard
Sent: Tuesday, July 29, 2008 11:49 AM
To: [hidden email]
Subject: [SPSSX-L] SPSS Python Integration Plug-in - v.16.0.2

Hi everyone,



Just wondering if a Python Integration Plug-in for SPSS 16.0.2 has been created. I was on the SPSS Developer Central website looking for it but couldn't find it.  I'd like to update some of our computers here to the new patched up version (16.0.2), but there doesn't appear to be an Integration plug-in for this version of SPSS.



Joel





Joël Rivard

Data Technician

Maps, Data & Government Information Centre

Carleton University Library

[phone] ext.1685

[hidden email]

=======
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[Manmit.Shrimali]

Hi ALl:

I have recently heard lot on parallel analysis. I was wondering if anyone can share their experience whether running PA was any help determing number of factors? If spss generates screen plot and eighenvalue so wondering what is the value of PA.

Thanks,
MS


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[Marta Garcia-Granero]

[hidden email] escribió:
> Hi ALl:
>
> I have recently heard lot on parallel analysis. I was wondering if anyone can share their experience whether running PA was any help determing number of factors? If spss generates screen plot and eighenvalue so wondering what is the value of PA.
>
The number of factors to be retained can be determined by several
procedures, and there is no clear consensus to which one is the best. It
depends on sample size, number of variables...

- Cattell's criterion uses the scree plot (not "screen"). Sometimes it
retains too few factors
- Kaiser's criterion, the oldest, selects those factors with eigenvalues
over 1. It has been much criticized and it's considered to yield
non-parsimonious solutions, with too many factors retained
- Velicer's MAP test and Horn's parallel analysis give in general good
results
- Percent variance explained criterion
- Others..

There are a lot of other details to be taken into account, like if
different solutions with different number of factors are interpretable
or not, a priori hypotheses...

Take a look at this page (accurate googling is a powerful tool for
finding answers): http://www2.chass.ncsu.edu/garson/pa765/factor.htm#facnum

HTH,
Marta García-Granero

--
For miscellaneous statistical stuff, visit:
http://gjyp.nl/marta/

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[Manmit.Shrimali]

Thanks Marta -

Does any opinion on their experience using DFACTOR of latent gold software?

-----Original Message-----
From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of Marta García-Granero
Sent: Wednesday, July 30, 2008 8:27 PM
To: [hidden email]
Subject: Re: Parallel Analysis

[hidden email] escribió:
> Hi ALl:
>
> I have recently heard lot on parallel analysis. I was wondering if anyone can share their experience whether running PA was any help determing number of factors? If spss generates screen plot and eighenvalue so wondering what is the value of PA.
>
The number of factors to be retained can be determined by several
procedures, and there is no clear consensus to which one is the best. It
depends on sample size, number of variables...

- Cattell's criterion uses the scree plot (not "screen"). Sometimes it
retains too few factors
- Kaiser's criterion, the oldest, selects those factors with eigenvalues
over 1. It has been much criticized and it's considered to yield
non-parsimonious solutions, with too many factors retained
- Velicer's MAP test and Horn's parallel analysis give in general good
results
- Percent variance explained criterion
- Others..

There are a lot of other details to be taken into account, like if
different solutions with different number of factors are interpretable
or not, a priori hypotheses...

Take a look at this page (accurate googling is a powerful tool for
finding answers): http://www2.chass.ncsu.edu/garson/pa765/factor.htm#facnum

HTH,
Marta García-Granero

--
For miscellaneous statistical stuff, visit:
http://gjyp.nl/marta/

=====================
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[Art Kendall]

PA supplies another help in deciding on the number of factors to retain.

The Kaiser criterion, i.e., retain all factors with eigenvalues that are
at least 1.00, only tells the number of factor that have "at least one
variable's worth" of the explained variance.
It is a programming device to avoid splitting hairs.

In the times I have used PA to ballpark the number of factor to CONSIDER
retaining, it has fairly well agreed with the scree and Montanelli
ballparks.

Of course the final number of factors to retain from the numbers
considered is based on the interpretability of the set of factors.

I have no formal proof.  PA has only been prominent for 15 or 20 years.
As I go back to factor analyses that I had done before PA was available,
and run a PA, I find that the eigenvalue value of the last factor
retained had at least a difference of 1.00 from the eigenvalue that
would be obtained from random data. For example, if the 5th factor's
eigenvalue only differs by 1.00 or more from the eigenvalue from random
data, that tells me interpret solutions that have about 5 factors.
However, sometimes there is more than one "elbow" in the eigenvalue
plot.  That may indicate that a solution that fewer factors should also
be interpreted.

So I have a new rule of thumb.  Find the factor number where the
obtained eigenvalue is at least 1.00 greater than the eigenvalue from
random data (PA).  Interpret solutions that involve about that number of
factors.

I would be interested in hearing from others on the list how the number
of factors ballparked by this "rule" compares to the number you actually
retained.


Art Kendall
Social Research Consultants

[hidden email] wrote:

> Hi ALl:
>
> I have recently heard lot on parallel analysis. I was wondering if anyone can share their experience whether running PA was any help determing number of factors? If spss generates screen plot and eighenvalue so wondering what is the value of PA.
>
>
> Thanks,
> MS
>
>
> This e-mail and any files transmitted with it are for the sole use of the intended recipient(s) and may contain confidential and privileged information.
> If you are not the intended recipient, please contact the sender by reply e-mail and destroy all copies of the original message.
>
> Any unauthorised review, use, disclosure, dissemination, forwarding, printing or copying of this email or any action taken in reliance on this e-mail is strictly
>
> prohibited and may be unlawful.
>
> ===================
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> [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
>
>
>

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[John Amora]

Are there available tools that can be used to test whether the eigenvalue is different from zero?  If there is, then that's a good tool to identify the number of factors.
 
JTA


--- On Thu, 7/31/08, Art Kendall <[hidden email]> wrote:

From: Art Kendall <[hidden email]>
Subject: Re: Parallel Analysis
To: [hidden email]
Date: Thursday, July 31, 2008, 8:04 PM

PA supplies another help in deciding on the number of factors to retain.

The Kaiser criterion, i.e., retain all factors with eigenvalues that are
at least 1.00, only tells the number of factor that have "at least one
variable's worth" of the explained variance.
It is a programming device to avoid splitting hairs.

In the times I have used PA to ballpark the number of factor to CONSIDER
retaining, it has fairly well agreed with the scree and Montanelli
ballparks.

Of course the final number of factors to retain from the numbers
considered is based on the interpretability of the set of factors.

I have no formal proof.  PA has only been prominent for 15 or 20 years.
As I go back to factor analyses that I had done before PA was available,
and run a PA, I find that the eigenvalue value of the last factor
retained had at least a difference of 1.00 from the eigenvalue that
would be obtained from random data. For example, if the 5th factor's
eigenvalue only differs by 1.00 or more from the eigenvalue from random
data, that tells me interpret solutions that have about 5 factors.
However, sometimes there is more than one "elbow" in the eigenvalue
plot.  That may indicate that a solution that fewer factors should also
be interpreted.

So I have a new rule of thumb.  Find the factor number where the
obtained eigenvalue is at least 1.00 greater than the eigenvalue from
random data (PA).  Interpret solutions that involve about that number of
factors.

I would be interested in hearing from others on the list how the number
of factors ballparked by this "rule" compares to the number you
actually
retained.


Art Kendall
Social Research Consultants

[hidden email] wrote:
> Hi ALl:
>
> I have recently heard lot on parallel analysis. I was wondering if anyone
can share their experience whether running PA was any help determing number of
factors? If spss generates screen plot and eighenvalue so wondering what is the
value of PA.
>
>
> Thanks,
> MS
>
>
> This e-mail and any files transmitted with it are for the sole use of the
intended recipient(s) and may contain confidential and privileged information.
> If you are not the intended recipient, please contact the sender by reply
e-mail and destroy all copies of the original message.
>
> Any unauthorised review, use, disclosure, dissemination, forwarding,
printing or copying of this email or any action taken in reliance on this
e-mail is strictly

>
> prohibited and may be unlawful.
>
> ===================
> 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
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> INFO REFCARD
>
>
>

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[Brian Moore-7]

Hi all-

It looks like copy data properties (documentation indicates this is a
replacement for apply dictionary) can be used to address my issue, but
it seems like it should be possible to just keep labels rather than
having to reapply them.

I'm using V15.

Thanks,
Brian

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