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SPSS factor scores

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SabatoPsy

SPSS factor scores

Hi Listserve,

I am wondering how the SPSS factor scores in the "Factor" command are calculated. More specifically, does the method of extraction change the calculated factor scores? I want to compare the factor scores created by a PCA compared to an EFA (with ML estimation). Is this possible in SPSS? I know there are three ways of calculating the factor scores (Regression, Bartlett, Anderson-Rubin). Let us assume I tell SPSS to use the same way: Regression. If I tell SPSS to create "Regression" factor scores with a principle component method of extraction, will they be different than the "Regression" factor scores with a maximum likelihood method of extraction?

Thank you,

David Disabato
George Mason University

David Marso

Re: SPSS factor scores

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Alex Reutter

Re: SPSS factor scores

Specifically, the top-level topic for the Statistics v22 FACTOR command algorithms is at: http://pic.dhe.ibm.com/infocenter/spssstat/v22r0m0/topic/com.ibm.spss.statistics.algorithms/alg_factor.htm

From: David Marso <[hidden email]>
To: [hidden email],
Date: 12/03/2013 11:46 AM
Subject: Re: SPSS factor scores
Sent by: "SPSSX(r) Discussion" <[hidden email]>

If I said yes why should you believe me? Try it for yourself. The specifics are documented in the algorithms available somewhere in the IBM labyrinth. < quote author="SabatoPsy"> Hi Listserve, I am wondering how the SPSS factor scores in the "Factor" command are calculated. More specifically, does the method of extraction change the calculated factor scores? I want to compare the factor scores created by a PCA compared to an EFA (with ML estimation). Is this possible in SPSS? I know there are three ways of calculating the factor scores (Regression, Bartlett, Anderson-Rubin). Let us assume I tell SPSS to use the same way: Regression. If I tell SPSS to create "Regression" factor scores with a principle component method of extraction, will they be different than the "Regression" factor scores with a maximum likelihood method of extraction? Thank you, David Disabato George Mason University ----- Please reply to the list and not to my personal email. Those desiring my consulting or training services please feel free to email me. --- "Nolite dare sanctum canibus neque mittatis margaritas vestras ante porcos ne forte conculcent eas pedibus suis." Cum es damnatorum possederunt porcos iens ut salire off sanguinum cliff in abyssum?" -- View this message in context:http://spssx-discussion.1045642.n5.nabble.com/SPSS-factor-scores-tp5723392p5723395.htmlSent from the SPSSX Discussion mailing list archive at Nabble.com. ===================== 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

Kirill Orlov

Re: SPSS factor scores

In reply to this post by SabatoPsy

Briefly:
1) Principal component scores (i.e. after PC extraction) are exact.
2) And they are the same whatever the number of components you extract.
3) And they are the same whatever method of computation (Regression, Bartlett, Anderson-Rubin). Actually, in PC the second two methods become equivalent to the "basic" one - Regression.

4) Factor scores (i.e. after other extraction methods) can be but just approximations (because uniqness values are unknown on case level).
5) And they differ from PC scores and differ after different extraction methods (since loadings differ).
6) And they depend on the method of computation. This is because of (4).
7) Regression is the basic method. The other two are its modifications ("enhancements").

03.12.2013 20:42, SabatoPsy пишет:

Hi Listserve,

I am wondering how the SPSS factor scores in the "Factor" command are
calculated. More specifically, does the method of extraction change the
calculated factor scores? I want to compare the factor scores created by a
PCA compared to an EFA (with ML estimation). Is this possible in SPSS? I
know there are three ways of calculating the factor scores (Regression,
Bartlett, Anderson-Rubin). Let us assume I tell SPSS to use the same way:
Regression. If I tell SPSS to create "Regression" factor scores with a
principle component method of extraction, will they be different than the
"Regression" factor scores with a maximum likelihood method of extraction?

Art Kendall

Re: SPSS factor scores

In reply to this post by SabatoPsy

What is the purpose of your using FACTOR? Data reduction? Creating scales? etc.

What are you going to use the factor scores for?

Art Kendall
Social Research Consultants

On 12/3/2013 11:42 AM, SabatoPsy [via SPSSX Discussion] wrote:

Hi Listserve,

I am wondering how the SPSS factor scores in the "Factor" command are calculated. More specifically, does the method of extraction change the calculated factor scores? I want to compare the factor scores created by a PCA compared to an EFA (with ML estimation). Is this possible in SPSS? I know there are three ways of calculating the factor scores (Regression, Bartlett, Anderson-Rubin). Let us assume I tell SPSS to use the same way: Regression. If I tell SPSS to create "Regression" factor scores with a principle component method of extraction, will they be different than the "Regression" factor scores with a maximum likelihood method of extraction?

Thank you,

David Disabato
George Mason University

If you reply to this email, your message will be added to the discussion below:
http://spssx-discussion.1045642.n5.nabble.com/SPSS-factor-scores-tp5723392.html

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Art Kendall
Social Research Consultants

Jon K Peck

Re: SPSS factor scores

In reply to this post by Alex Reutter

Not to mention the same information from within Statistics via
Help > Algorithms > Factor Algorithms

Jon Peck (no "h") aka Kim
Senior Software Engineer, IBM
[hidden email]
phone: 720-342-5621

From: Alex Reutter/Burlington/IBM@IBMUS
To: [hidden email],
Date: 12/03/2013 10:09 AM
Subject: Re: [SPSSX-L] SPSS factor scores
Sent by: "SPSSX(r) Discussion" <[hidden email]>

SabatoPsy

Re: SPSS factor scores

In reply to this post by Art Kendall

Thank you for all of your posts. I looked up online how the factor scores are computed in SPSS in the IBM labyrinth. I got a bunch of matrix algebra and calculus notation that I am not smart enough to follow. I did however do an experiment on my own as was also suggested. I found that there are small differences between factor scores saved via the PCA method of extraction compared to the PAF and ML extraction methods; however, there were negligible differences between the PAF and ML methods. I tested this by correlating different saved factor score variables from the exact same factor structure but with different extraction methods.

To address the question of why I am creating the factor(s) may go outside the scope of this listserve, but I will gladly take any advice/knowledge. I am running some SEM models in AMOS and am getting strange correlations between my factors. I wanted a way to test whether indeed these correlations between factors are accurate. I thought that by correlating the saved factor scores in SPSS using the ML extraction method, I should get the same correlations I got in AMOS. I am creating multiple single factors from a multiple set of 4 manifest indicators to create 5 total factors. To my knowledge, an EFA using ML and a CFA create essentially (minus negligible estimation differences) the same factor scores (when you are creating a single factor using all indicators). Therefore correlations between the SPSS factor scores I created and correlations between the latent factors I created in AMOS should be essentially equivalent. However, I am getting substantially different correlations. Does anybody know what is going on... or what I am doing wrong... or why my belief that the correlations should be the same is wrong??? I would greatly appreciate any enlightenment.

David Disabato

George Mason University

On Tue, Dec 3, 2013 at 12:26 PM, Art Kendall [via SPSSX Discussion] <[hidden email]> wrote:

What is the purpose of your using FACTOR? Data reduction? Creating scales? etc.

What are you going to use the factor scores for?
Art Kendall
Social Research Consultants
On 12/3/2013 11:42 AM, SabatoPsy [via SPSSX Discussion] wrote:
Hi Listserve,

I am wondering how the SPSS factor scores in the "Factor" command are calculated. More specifically, does the method of extraction change the calculated factor scores? I want to compare the factor scores created by a PCA compared to an EFA (with ML estimation). Is this possible in SPSS? I know there are three ways of calculating the factor scores (Regression, Bartlett, Anderson-Rubin). Let us assume I tell SPSS to use the same way: Regression. If I tell SPSS to create "Regression" factor scores with a principle component method of extraction, will they be different than the "Regression" factor scores with a maximum likelihood method of extraction?

Thank you,

David Disabato
George Mason University

If you reply to this email, your message will be added to the discussion below:

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Art Kendall
Social Research Consultants

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--
David J. Disabato

Clinical Psychology Doctoral Student

George Mason University

[hidden email]

Maguin, Eugene

Re: SPSS factor scores

Let’s back up to where the problem begins, which seems to be at the SEM stage because you are reporting “strange correlations” between factors. So, what’s strange about the correlations? And, just so that we all are on the same page, please clarify your use of ‘correlations’ in the context of an SEM. Affirmatively verify that the following sequence is true. Analyzing raw data not a covariance or correlation matrix. One loading fixed at 1.00 for each factor rather than factor variances fixed at 1.00. Fit of the model is acceptable under current standards. The strange values are standardized values and not unstandardized values.

Gene Maguin

From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of SabatoPsy
Sent: Tuesday, December 03, 2013 3:56 PM
To: [hidden email]
Subject: Re: SPSS factor scores

David Disabato

George Mason University

On Tue, Dec 3, 2013 at 12:26 PM, Art Kendall [via SPSSX Discussion] <[hidden email]> wrote:

What is the purpose of your using FACTOR? Data reduction? Creating scales? etc.

What are you going to use the factor scores for?

Art Kendall

Social Research Consultants

On 12/3/2013 11:42 AM, SabatoPsy [via SPSSX Discussion] wrote:

Hi Listserve,

I am wondering how the SPSS factor scores in the "Factor" command are calculated. More specifically, does the method of extraction change the calculated factor scores? I want to compare the factor scores created by a PCA compared to an EFA (with ML estimation). Is this possible in SPSS? I know there are three ways of calculating the factor scores (Regression, Bartlett, Anderson-Rubin). Let us assume I tell SPSS to use the same way: Regression. If I tell SPSS to create "Regression" factor scores with a principle component method of extraction, will they be different than the "Regression" factor scores with a maximum likelihood method of extraction?

Thank you,

David Disabato
George Mason University

If you reply to this email, your message will be added to the discussion below:

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Art Kendall
Social Research Consultants

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--
David J. Disabato

Clinical Psychology Doctoral Student

George Mason University

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View this message in context: Re: SPSS factor scores
Sent from the SPSSX Discussion mailing list archive at Nabble.com.

SabatoPsy

Re: SPSS factor scores

Good questions. The correlations are strange because they are so high. The literature would suggest they be highly correlated (around .70), however, I am getting correlations of .99. Everything you said is true except for good model fit. These are correlations from raw data. I have an SPSS file with no missing data. One of the factor loadings is set to 1.0 for each factor. These are standardized correlation coefficients. The model fit is poor though. I have a CFI of .77 and RMSEA of .16. Could having poor model fit change/bias my correlation estimates?

On Tue, Dec 3, 2013 at 4:18 PM, Maguin, Eugene [via SPSSX Discussion] <[hidden email]> wrote:

Let’s back up to where the problem begins, which seems to be at the SEM stage because you are reporting “strange correlations” between factors. So, what’s strange about the correlations? And, just so that we all are on the same page, please clarify your use of ‘correlations’ in the context of an SEM. Affirmatively verify that the following sequence is true. Analyzing raw data not a covariance or correlation matrix. One loading fixed at 1.00 for each factor rather than factor variances fixed at 1.00. Fit of the model is acceptable under current standards. The strange values are standardized values and not unstandardized values.

Gene Maguin

From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of SabatoPsy
Sent: Tuesday, December 03, 2013 3:56 PM
To: [hidden email]

Subject: Re: SPSS factor scores

Thank you for all of your posts. I looked up online how the factor scores are computed in SPSS in the IBM labyrinth. I got a bunch of matrix algebra and calculus notation that I am not smart enough to follow. I did however do an experiment on my own as was also suggested. I found that there are small differences between factor scores saved via the PCA method of extraction compared to the PAF and ML extraction methods; however, there were negligible differences between the PAF and ML methods. I tested this by correlating different saved factor score variables from the exact same factor structure but with different extraction methods.

To address the question of why I am creating the factor(s) may go outside the scope of this listserve, but I will gladly take any advice/knowledge. I am running some SEM models in AMOS and am getting strange correlations between my factors. I wanted a way to test whether indeed these correlations between factors are accurate. I thought that by correlating the saved factor scores in SPSS using the ML extraction method, I should get the same correlations I got in AMOS. I am creating multiple single factors from a multiple set of 4 manifest indicators to create 5 total factors. To my knowledge, an EFA using ML and a CFA create essentially (minus negligible estimation differences) the same factor scores (when you are creating a single factor using all indicators). Therefore correlations between the SPSS factor scores I created and correlations between the latent factors I created in AMOS should be essentially equivalent. However, I am getting substantially different correlations. Does anybody know what is going on... or what I am doing wrong... or why my belief that the correlations should be the same is wrong??? I would greatly appreciate any enlightenment.

David Disabato

George Mason University
On Tue, Dec 3, 2013 at 12:26 PM, Art Kendall [via SPSSX Discussion] <[hidden email]> wrote:
What is the purpose of your using FACTOR? Data reduction? Creating scales? etc.

What are you going to use the factor scores for?
Art Kendall
Social Research Consultants
On 12/3/2013 11:42 AM, SabatoPsy [via SPSSX Discussion] wrote:
Hi Listserve,

I am wondering how the SPSS factor scores in the "Factor" command are calculated. More specifically, does the method of extraction change the calculated factor scores? I want to compare the factor scores created by a PCA compared to an EFA (with ML estimation). Is this possible in SPSS? I know there are three ways of calculating the factor scores (Regression, Bartlett, Anderson-Rubin). Let us assume I tell SPSS to use the same way: Regression. If I tell SPSS to create "Regression" factor scores with a principle component method of extraction, will they be different than the "Regression" factor scores with a maximum likelihood method of extraction?

Thank you,

David Disabato
George Mason University

If you reply to this email, your message will be added to the discussion below:

http://spssx-discussion.1045642.n5.nabble.com/SPSS-factor-scores-tp5723392.html

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NAML

Art Kendall
Social Research Consultants

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--
David J. Disabato

Clinical Psychology Doctoral Student

George Mason University
[hidden email]
View this message in context: Re: SPSS factor scores

Sent from the SPSSX Discussion mailing list archive at Nabble.com.
If you reply to this email, your message will be added to the discussion below:

http://spssx-discussion.1045642.n5.nabble.com/SPSS-factor-scores-tp5723392p5723403.html

To unsubscribe from SPSS factor scores, click here.
NAML

--
David J. Disabato

Clinical Psychology Doctoral Student

George Mason University

[hidden email]

Maguin, Eugene

Re: SPSS factor scores

Ok, let’s get the ‘slap your forehead’ question out of the way first. Is missing data correctly accounted for, i.e., is there any possibility that user missing values are being treated as nonmissing? Is there any possibility that one indicator is a near duplicate of another or a composite of several indicators?

Let’s assume not. Next. I’m not familiar with Amos since I use mplus but if amos can print out the covariance or correlation matrix for the indicators, does that matrix match the matrix that spss computes? Amos is FIML and spss is not and the numbers should be very similar unless you have large amounts of missing data that are not missing completely at random and is ‘strongly’ correlated with model covariates. Lastly, if you have a correlation of .99 between factors, the correlations between the two factors’ indicators have to be extremely high, like in the upper 90’s, I’d guess. If this is true, then I’d guess you have variable construction or conceptualization problems.

Gene Maguin

From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of SabatoPsy
Sent: Tuesday, December 03, 2013 4:30 PM
To: [hidden email]
Subject: Re: SPSS factor scores

On Tue, Dec 3, 2013 at 4:18 PM, Maguin, Eugene [via SPSSX Discussion] <[hidden email]> wrote:

Gene Maguin

From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of SabatoPsy
Sent: Tuesday, December 03, 2013 3:56 PM
To: [hidden email]

Subject: Re: SPSS factor scores

David Disabato

George Mason University

On Tue, Dec 3, 2013 at 12:26 PM, Art Kendall [via SPSSX Discussion] <[hidden email]> wrote:

What is the purpose of your using FACTOR? Data reduction? Creating scales? etc.

What are you going to use the factor scores for?

Art Kendall

Social Research Consultants

On 12/3/2013 11:42 AM, SabatoPsy [via SPSSX Discussion] wrote:

Hi Listserve,

I am wondering how the SPSS factor scores in the "Factor" command are calculated. More specifically, does the method of extraction change the calculated factor scores? I want to compare the factor scores created by a PCA compared to an EFA (with ML estimation). Is this possible in SPSS? I know there are three ways of calculating the factor scores (Regression, Bartlett, Anderson-Rubin). Let us assume I tell SPSS to use the same way: Regression. If I tell SPSS to create "Regression" factor scores with a principle component method of extraction, will they be different than the "Regression" factor scores with a maximum likelihood method of extraction?

Thank you,

David Disabato
George Mason University

If you reply to this email, your message will be added to the discussion below:

http://spssx-discussion.1045642.n5.nabble.com/SPSS-factor-scores-tp5723392.html

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To unsubscribe from SPSSX Discussion, click here.
NAML

Art Kendall
Social Research Consultants

If you reply to this email, your message will be added to the discussion below:

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--
David J. Disabato

Clinical Psychology Doctoral Student

George Mason University

[hidden email]

View this message in context: Re: SPSS factor scores

Sent from the SPSSX Discussion mailing list archive at Nabble.com.

If you reply to this email, your message will be added to the discussion below:

http://spssx-discussion.1045642.n5.nabble.com/SPSS-factor-scores-tp5723392p5723403.html

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--
David J. Disabato

Clinical Psychology Doctoral Student

George Mason University

[hidden email]

View this message in context: Re: SPSS factor scores
Sent from the SPSSX Discussion mailing list archive at Nabble.com.

SabatoPsy

Re: SPSS factor scores

I just checked in my SPSS dataset. There is no user-missing data. The indicators correlation between .40 and .75 of one another. Good thinking. I am going to look into trying to print out the correlation matrix from AMOS. That should be helpful in determining what might be going on.

On Tue, Dec 3, 2013 at 4:52 PM, Maguin, Eugene [via SPSSX Discussion] <[hidden email]> wrote:

Ok, let’s get the ‘slap your forehead’ question out of the way first. Is missing data correctly accounted for, i.e., is there any possibility that user missing values are being treated as nonmissing? Is there any possibility that one indicator is a near duplicate of another or a composite of several indicators?

Let’s assume not. Next. I’m not familiar with Amos since I use mplus but if amos can print out the covariance or correlation matrix for the indicators, does that matrix match the matrix that spss computes? Amos is FIML and spss is not and the numbers should be very similar unless you have large amounts of missing data that are not missing completely at random and is ‘strongly’ correlated with model covariates. Lastly, if you have a correlation of .99 between factors, the correlations between the two factors’ indicators have to be extremely high, like in the upper 90’s, I’d guess. If this is true, then I’d guess you have variable construction or conceptualization problems.

Gene Maguin

From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of SabatoPsy
Sent: Tuesday, December 03, 2013 4:30 PM

To: [hidden email]
Subject: Re: SPSS factor scores

Good questions. The correlations are strange because they are so high. The literature would suggest they be highly correlated (around .70), however, I am getting correlations of .99. Everything you said is true except for good model fit. These are correlations from raw data. I have an SPSS file with no missing data. One of the factor loadings is set to 1.0 for each factor. These are standardized correlation coefficients. The model fit is poor though. I have a CFI of .77 and RMSEA of .16. Could having poor model fit change/bias my correlation estimates?
On Tue, Dec 3, 2013 at 4:18 PM, Maguin, Eugene [via SPSSX Discussion] <[hidden email]> wrote:
Let’s back up to where the problem begins, which seems to be at the SEM stage because you are reporting “strange correlations” between factors. So, what’s strange about the correlations? And, just so that we all are on the same page, please clarify your use of ‘correlations’ in the context of an SEM. Affirmatively verify that the following sequence is true. Analyzing raw data not a covariance or correlation matrix. One loading fixed at 1.00 for each factor rather than factor variances fixed at 1.00. Fit of the model is acceptable under current standards. The strange values are standardized values and not unstandardized values.

Gene Maguin

From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of SabatoPsy
Sent: Tuesday, December 03, 2013 3:56 PM
To: [hidden email]

Subject: Re: SPSS factor scores

Thank you for all of your posts. I looked up online how the factor scores are computed in SPSS in the IBM labyrinth. I got a bunch of matrix algebra and calculus notation that I am not smart enough to follow. I did however do an experiment on my own as was also suggested. I found that there are small differences between factor scores saved via the PCA method of extraction compared to the PAF and ML extraction methods; however, there were negligible differences between the PAF and ML methods. I tested this by correlating different saved factor score variables from the exact same factor structure but with different extraction methods.

To address the question of why I am creating the factor(s) may go outside the scope of this listserve, but I will gladly take any advice/knowledge. I am running some SEM models in AMOS and am getting strange correlations between my factors. I wanted a way to test whether indeed these correlations between factors are accurate. I thought that by correlating the saved factor scores in SPSS using the ML extraction method, I should get the same correlations I got in AMOS. I am creating multiple single factors from a multiple set of 4 manifest indicators to create 5 total factors. To my knowledge, an EFA using ML and a CFA create essentially (minus negligible estimation differences) the same factor scores (when you are creating a single factor using all indicators). Therefore correlations between the SPSS factor scores I created and correlations between the latent factors I created in AMOS should be essentially equivalent. However, I am getting substantially different correlations. Does anybody know what is going on... or what I am doing wrong... or why my belief that the correlations should be the same is wrong??? I would greatly appreciate any enlightenment.

David Disabato

George Mason University
On Tue, Dec 3, 2013 at 12:26 PM, Art Kendall [via SPSSX Discussion] <[hidden email]> wrote:
What is the purpose of your using FACTOR? Data reduction? Creating scales? etc.

What are you going to use the factor scores for?
Art Kendall
Social Research Consultants
On 12/3/2013 11:42 AM, SabatoPsy [via SPSSX Discussion] wrote:
Hi Listserve,

I am wondering how the SPSS factor scores in the "Factor" command are calculated. More specifically, does the method of extraction change the calculated factor scores? I want to compare the factor scores created by a PCA compared to an EFA (with ML estimation). Is this possible in SPSS? I know there are three ways of calculating the factor scores (Regression, Bartlett, Anderson-Rubin). Let us assume I tell SPSS to use the same way: Regression. If I tell SPSS to create "Regression" factor scores with a principle component method of extraction, will they be different than the "Regression" factor scores with a maximum likelihood method of extraction?

Thank you,

David Disabato
George Mason University

If you reply to this email, your message will be added to the discussion below:

http://spssx-discussion.1045642.n5.nabble.com/SPSS-factor-scores-tp5723392.html

To start a new topic under SPSSX Discussion, email [hidden email]
To unsubscribe from SPSSX Discussion, click here.
NAML

Art Kendall
Social Research Consultants

If you reply to this email, your message will be added to the discussion below:

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--
David J. Disabato

Clinical Psychology Doctoral Student

George Mason University
[hidden email]
View this message in context: Re: SPSS factor scores

Sent from the SPSSX Discussion mailing list archive at Nabble.com.
If you reply to this email, your message will be added to the discussion below:

http://spssx-discussion.1045642.n5.nabble.com/SPSS-factor-scores-tp5723392p5723403.html

To unsubscribe from SPSS factor scores, click here.
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--
David J. Disabato

Clinical Psychology Doctoral Student

George Mason University

[hidden email]
View this message in context: Re: SPSS factor scores
Sent from the SPSSX Discussion mailing list archive at Nabble.com.
If you reply to this email, your message will be added to the discussion below:

http://spssx-discussion.1045642.n5.nabble.com/SPSS-factor-scores-tp5723392p5723406.html

To unsubscribe from SPSS factor scores, click here.
NAML

--
David J. Disabato

Clinical Psychology Doctoral Student

George Mason University

[hidden email]

Art Kendall

Re: SPSS factor scores

Is there system missing data? (sysmis)

Art Kendall
Social Research Consultants

On 12/3/2013 5:44 PM, SabatoPsy [via SPSSX Discussion] wrote:

I just checked in my SPSS dataset. There is no user-missing data. The indicators correlation between .40 and .75 of one another. Good thinking. I am going to look into trying to print out the correlation matrix from AMOS. That should be helpful in determining what might be going on.
On Tue, Dec 3, 2013 at 4:52 PM, Maguin, Eugene [via SPSSX Discussion] <[hidden email]> wrote:
Ok, let’s get the ‘slap your forehead’ question out of the way first. Is missing data correctly accounted for, i.e., is there any possibility that user missing values are being treated as nonmissing? Is there any possibility that one indicator is a near duplicate of another or a composite of several indicators?

Let’s assume not. Next. I’m not familiar with Amos since I use mplus but if amos can print out the covariance or correlation matrix for the indicators, does that matrix match the matrix that spss computes? Amos is FIML and spss is not and the numbers should be very similar unless you have large amounts of missing data that are not missing completely at random and is ‘strongly’ correlated with model covariates. Lastly, if you have a correlation of .99 between factors, the correlations between the two factors’ indicators have to be extremely high, like in the upper 90’s, I’d guess. If this is true, then I’d guess you have variable construction or conceptualization problems.

Gene Maguin

From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of SabatoPsy
Sent: Tuesday, December 03, 2013 4:30 PM

To: [hidden email]
Subject: Re: SPSS factor scores

Good questions. The correlations are strange because they are so high. The literature would suggest they be highly correlated (around .70), however, I am getting correlations of .99. Everything you said is true except for good model fit. These are correlations from raw data. I have an SPSS file with no missing data. One of the factor loadings is set to 1.0 for each factor. These are standardized correlation coefficients. The model fit is poor though. I have a CFI of .77 and RMSEA of .16. Could having poor model fit change/bias my correlation estimates?
On Tue, Dec 3, 2013 at 4:18 PM, Maguin, Eugene [via SPSSX Discussion] <[hidden email]> wrote:
Let’s back up to where the problem begins, which seems to be at the SEM stage because you are reporting “strange correlations” between factors. So, what’s strange about the correlations? And, just so that we all are on the same page, please clarify your use of ‘correlations’ in the context of an SEM. Affirmatively verify that the following sequence is true. Analyzing raw data not a covariance or correlation matrix. One loading fixed at 1.00 for each factor rather than factor variances fixed at 1.00. Fit of the model is acceptable under current standards. The strange values are standardized values and not unstandardized values.

Gene Maguin

From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of SabatoPsy
Sent: Tuesday, December 03, 2013 3:56 PM
To: [hidden email]

Subject: Re: SPSS factor scores

Thank you for all of your posts. I looked up online how the factor scores are computed in SPSS in the IBM labyrinth. I got a bunch of matrix algebra and calculus notation that I am not smart enough to follow. I did however do an experiment on my own as was also suggested. I found that there are small differences between factor scores saved via the PCA method of extraction compared to the PAF and ML extraction methods; however, there were negligible differences between the PAF and ML methods. I tested this by correlating different saved factor score variables from the exact same factor structure but with different extraction methods.

To address the question of why I am creating the factor(s) may go outside the scope of this listserve, but I will gladly take any advice/knowledge. I am running some SEM models in AMOS and am getting strange correlations between my factors. I wanted a way to test whether indeed these correlations between factors are accurate. I thought that by correlating the saved factor scores in SPSS using the ML extraction method, I should get the same correlations I got in AMOS. I am creating multiple single factors from a multiple set of 4 manifest indicators to create 5 total factors. To my knowledge, an EFA using ML and a CFA create essentially (minus negligible estimation differences) the same factor scores (when you are creating a single factor using all indicators). Therefore correlations between the SPSS factor scores I created and correlations between the latent factors I created in AMOS should be essentially equivalent. However, I am getting substantially different correlations. Does anybody know what is going on... or what I am doing wrong... or why my belief that the correlations should be the same is wrong??? I would greatly appreciate any enlightenment.

David Disabato

George Mason University
On Tue, Dec 3, 2013 at 12:26 PM, Art Kendall [via SPSSX Discussion] <[hidden email]> wrote:
What is the purpose of your using FACTOR? Data reduction? Creating scales? etc.

What are you going to use the factor scores for?
Art Kendall
Social Research Consultants
On 12/3/2013 11:42 AM, SabatoPsy [via SPSSX Discussion] wrote:
Hi Listserve,

I am wondering how the SPSS factor scores in the "Factor" command are calculated. More specifically, does the method of extraction change the calculated factor scores? I want to compare the factor scores created by a PCA compared to an EFA (with ML estimation). Is this possible in SPSS? I know there are three ways of calculating the factor scores (Regression, Bartlett, Anderson-Rubin). Let us assume I tell SPSS to use the same way: Regression. If I tell SPSS to create "Regression" factor scores with a principle component method of extraction, will they be different than the "Regression" factor scores with a maximum likelihood method of extraction?

Thank you,

David Disabato
George Mason University

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Clinical Psychology Doctoral Student

George Mason University
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George Mason University

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Clinical Psychology Doctoral Student

George Mason University

[hidden email]
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SabatoPsy

Re: SPSS factor scores

Good question. No there is not. This is a data set I created from only the complete cases of my study. Because someone mentioned model fit, I am beginning to wonder if that is what is going on. I know I have poor model it. I read online that if you have poor model fit, your model is most likely misspecified, which can lead to biased parameter estimates (i.e. correlations). After all, SEM is solving regression equations simultaneously. Does this jive with what you know about SEM and AMOS? Again I am sorry that this is leading away from the content of this listserv.

On Tue, Dec 3, 2013 at 8:03 PM, Art Kendall [via SPSSX Discussion] <[hidden email]> wrote:

Is there system missing data? (sysmis)
Art Kendall
Social Research Consultants
On 12/3/2013 5:44 PM, SabatoPsy [via SPSSX Discussion] wrote:
I just checked in my SPSS dataset. There is no user-missing data. The indicators correlation between .40 and .75 of one another. Good thinking. I am going to look into trying to print out the correlation matrix from AMOS. That should be helpful in determining what might be going on.
On Tue, Dec 3, 2013 at 4:52 PM, Maguin, Eugene [via SPSSX Discussion] <[hidden email]> wrote:
Ok, let’s get the ‘slap your forehead’ question out of the way first. Is missing data correctly accounted for, i.e., is there any possibility that user missing values are being treated as nonmissing? Is there any possibility that one indicator is a near duplicate of another or a composite of several indicators?

Let’s assume not. Next. I’m not familiar with Amos since I use mplus but if amos can print out the covariance or correlation matrix for the indicators, does that matrix match the matrix that spss computes? Amos is FIML and spss is not and the numbers should be very similar unless you have large amounts of missing data that are not missing completely at random and is ‘strongly’ correlated with model covariates. Lastly, if you have a correlation of .99 between factors, the correlations between the two factors’ indicators have to be extremely high, like in the upper 90’s, I’d guess. If this is true, then I’d guess you have variable construction or conceptualization problems.

Gene Maguin

From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of SabatoPsy
Sent: Tuesday, December 03, 2013 4:30 PM

To: [hidden email]
Subject: Re: SPSS factor scores

Good questions. The correlations are strange because they are so high. The literature would suggest they be highly correlated (around .70), however, I am getting correlations of .99. Everything you said is true except for good model fit. These are correlations from raw data. I have an SPSS file with no missing data. One of the factor loadings is set to 1.0 for each factor. These are standardized correlation coefficients. The model fit is poor though. I have a CFI of .77 and RMSEA of .16. Could having poor model fit change/bias my correlation estimates?
On Tue, Dec 3, 2013 at 4:18 PM, Maguin, Eugene [via SPSSX Discussion] <[hidden email]> wrote:
Let’s back up to where the problem begins, which seems to be at the SEM stage because you are reporting “strange correlations” between factors. So, what’s strange about the correlations? And, just so that we all are on the same page, please clarify your use of ‘correlations’ in the context of an SEM. Affirmatively verify that the following sequence is true. Analyzing raw data not a covariance or correlation matrix. One loading fixed at 1.00 for each factor rather than factor variances fixed at 1.00. Fit of the model is acceptable under current standards. The strange values are standardized values and not unstandardized values.

Gene Maguin

From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of SabatoPsy
Sent: Tuesday, December 03, 2013 3:56 PM
To: [hidden email]

Subject: Re: SPSS factor scores

Thank you for all of your posts. I looked up online how the factor scores are computed in SPSS in the IBM labyrinth. I got a bunch of matrix algebra and calculus notation that I am not smart enough to follow. I did however do an experiment on my own as was also suggested. I found that there are small differences between factor scores saved via the PCA method of extraction compared to the PAF and ML extraction methods; however, there were negligible differences between the PAF and ML methods. I tested this by correlating different saved factor score variables from the exact same factor structure but with different extraction methods.

To address the question of why I am creating the factor(s) may go outside the scope of this listserve, but I will gladly take any advice/knowledge. I am running some SEM models in AMOS and am getting strange correlations between my factors. I wanted a way to test whether indeed these correlations between factors are accurate. I thought that by correlating the saved factor scores in SPSS using the ML extraction method, I should get the same correlations I got in AMOS. I am creating multiple single factors from a multiple set of 4 manifest indicators to create 5 total factors. To my knowledge, an EFA using ML and a CFA create essentially (minus negligible estimation differences) the same factor scores (when you are creating a single factor using all indicators). Therefore correlations between the SPSS factor scores I created and correlations between the latent factors I created in AMOS should be essentially equivalent. However, I am getting substantially different correlations. Does anybody know what is going on... or what I am doing wrong... or why my belief that the correlations should be the same is wrong??? I would greatly appreciate any enlightenment.

David Disabato

George Mason University
On Tue, Dec 3, 2013 at 12:26 PM, Art Kendall [via SPSSX Discussion] <[hidden email]> wrote:
What is the purpose of your using FACTOR? Data reduction? Creating scales? etc.

What are you going to use the factor scores for?
Art Kendall
Social Research Consultants
On 12/3/2013 11:42 AM, SabatoPsy [via SPSSX Discussion] wrote:
Hi Listserve,

I am wondering how the SPSS factor scores in the "Factor" command are calculated. More specifically, does the method of extraction change the calculated factor scores? I want to compare the factor scores created by a PCA compared to an EFA (with ML estimation). Is this possible in SPSS? I know there are three ways of calculating the factor scores (Regression, Bartlett, Anderson-Rubin). Let us assume I tell SPSS to use the same way: Regression. If I tell SPSS to create "Regression" factor scores with a principle component method of extraction, will they be different than the "Regression" factor scores with a maximum likelihood method of extraction?

Thank you,

David Disabato
George Mason University

If you reply to this email, your message will be added to the discussion below:

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NAML

Art Kendall
Social Research Consultants

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--
David J. Disabato

Clinical Psychology Doctoral Student

George Mason University
[hidden email]
View this message in context: Re: SPSS factor scores

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--
David J. Disabato

Clinical Psychology Doctoral Student

George Mason University

[hidden email]
View this message in context: Re: SPSS factor scores
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David J. Disabato
Clinical Psychology Doctoral Student

George Mason University
[hidden email]
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David J. Disabato

Clinical Psychology Doctoral Student

George Mason University

[hidden email]

Art Kendall

Re: SPSS factor scores

Perhaps you should start at the beginning.
Please describe your research questions and your data.
What is a case?
How were the cases selected?
How many did you start with?
How many did you have after the listwise deletion of case?
When data was missing why was it missing?
Did you have a manipulated treatment?
Was it randomly assigned?
What constructs are you operationalizing in measures?
How are you operationalizing them?
     Are your measures pre-established summative scales?
    Or did you write the items?
    etc.

Art Kendall
Social Research Consultants

On 12/3/2013 9:04 PM, SabatoPsy [via SPSSX Discussion] wrote:

Good question. No there is not. This is a data set I created from only the complete cases of my study. Because someone mentioned model fit, I am beginning to wonder if that is what is going on. I know I have poor model it. I read online that if you have poor model fit, your model is most likely misspecified, which can lead to biased parameter estimates (i.e. correlations). After all, SEM is solving regression equations simultaneously. Does this jive with what you know about SEM and AMOS? Again I am sorry that this is leading away from the content of this listserv.
On Tue, Dec 3, 2013 at 8:03 PM, Art Kendall [via SPSSX Discussion] <[hidden email]> wrote:
Is there system missing data? (sysmis)
Art Kendall
Social Research Consultants
On 12/3/2013 5:44 PM, SabatoPsy [via SPSSX Discussion] wrote:
I just checked in my SPSS dataset. There is no user-missing data. The indicators correlation between .40 and .75 of one another. Good thinking. I am going to look into trying to print out the correlation matrix from AMOS. That should be helpful in determining what might be going on.
On Tue, Dec 3, 2013 at 4:52 PM, Maguin, Eugene [via SPSSX Discussion] <[hidden email]> wrote:
Ok, let’s get the ‘slap your forehead’ question out of the way first. Is missing data correctly accounted for, i.e., is there any possibility that user missing values are being treated as nonmissing? Is there any possibility that one indicator is a near duplicate of another or a composite of several indicators?

Let’s assume not. Next. I’m not familiar with Amos since I use mplus but if amos can print out the covariance or correlation matrix for the indicators, does that matrix match the matrix that spss computes? Amos is FIML and spss is not and the numbers should be very similar unless you have large amounts of missing data that are not missing completely at random and is ‘strongly’ correlated with model covariates. Lastly, if you have a correlation of .99 between factors, the correlations between the two factors’ indicators have to be extremely high, like in the upper 90’s, I’d guess. If this is true, then I’d guess you have variable construction or conceptualization problems.

Gene Maguin

From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of SabatoPsy
Sent: Tuesday, December 03, 2013 4:30 PM

To: [hidden email]
Subject: Re: SPSS factor scores

Good questions. The correlations are strange because they are so high. The literature would suggest they be highly correlated (around .70), however, I am getting correlations of .99. Everything you said is true except for good model fit. These are correlations from raw data. I have an SPSS file with no missing data. One of the factor loadings is set to 1.0 for each factor. These are standardized correlation coefficients. The model fit is poor though. I have a CFI of .77 and RMSEA of .16. Could having poor model fit change/bias my correlation estimates?
On Tue, Dec 3, 2013 at 4:18 PM, Maguin, Eugene [via SPSSX Discussion] <[hidden email]> wrote:
Let’s back up to where the problem begins, which seems to be at the SEM stage because you are reporting “strange correlations” between factors. So, what’s strange about the correlations? And, just so that we all are on the same page, please clarify your use of ‘correlations’ in the context of an SEM. Affirmatively verify that the following sequence is true. Analyzing raw data not a covariance or correlation matrix. One loading fixed at 1.00 for each factor rather than factor variances fixed at 1.00. Fit of the model is acceptable under current standards. The strange values are standardized values and not unstandardized values.

Gene Maguin

From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of SabatoPsy
Sent: Tuesday, December 03, 2013 3:56 PM
To: [hidden email]

Subject: Re: SPSS factor scores

Thank you for all of your posts. I looked up online how the factor scores are computed in SPSS in the IBM labyrinth. I got a bunch of matrix algebra and calculus notation that I am not smart enough to follow. I did however do an experiment on my own as was also suggested. I found that there are small differences between factor scores saved via the PCA method of extraction compared to the PAF and ML extraction methods; however, there were negligible differences between the PAF and ML methods. I tested this by correlating different saved factor score variables from the exact same factor structure but with different extraction methods.

To address the question of why I am creating the factor(s) may go outside the scope of this listserve, but I will gladly take any advice/knowledge. I am running some SEM models in AMOS and am getting strange correlations between my factors. I wanted a way to test whether indeed these correlations between factors are accurate. I thought that by correlating the saved factor scores in SPSS using the ML extraction method, I should get the same correlations I got in AMOS. I am creating multiple single factors from a multiple set of 4 manifest indicators to create 5 total factors. To my knowledge, an EFA using ML and a CFA create essentially (minus negligible estimation differences) the same factor scores (when you are creating a single factor using all indicators). Therefore correlations between the SPSS factor scores I created and correlations between the latent factors I created in AMOS should be essentially equivalent. However, I am getting substantially different correlations. Does anybody know what is going on... or what I am doing wrong... or why my belief that the correlations should be the same is wrong??? I would greatly appreciate any enlightenment.

David Disabato

George Mason University
On Tue, Dec 3, 2013 at 12:26 PM, Art Kendall [via SPSSX Discussion] <[hidden email]> wrote:
What is the purpose of your using FACTOR? Data reduction? Creating scales? etc.

What are you going to use the factor scores for?
Art Kendall
Social Research Consultants
On 12/3/2013 11:42 AM, SabatoPsy [via SPSSX Discussion] wrote:
Hi Listserve,

I am wondering how the SPSS factor scores in the "Factor" command are calculated. More specifically, does the method of extraction change the calculated factor scores? I want to compare the factor scores created by a PCA compared to an EFA (with ML estimation). Is this possible in SPSS? I know there are three ways of calculating the factor scores (Regression, Bartlett, Anderson-Rubin). Let us assume I tell SPSS to use the same way: Regression. If I tell SPSS to create "Regression" factor scores with a principle component method of extraction, will they be different than the "Regression" factor scores with a maximum likelihood method of extraction?

Thank you,

David Disabato
George Mason University

If you reply to this email, your message will be added to the discussion below:

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NAML

Art Kendall
Social Research Consultants

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--
David J. Disabato

Clinical Psychology Doctoral Student

George Mason University
[hidden email]
View this message in context: Re: SPSS factor scores

Sent from the SPSSX Discussion mailing list archive at Nabble.com.
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--
David J. Disabato

Clinical Psychology Doctoral Student

George Mason University

[hidden email]
View this message in context: Re: SPSS factor scores
Sent from the SPSSX Discussion mailing list archive at Nabble.com.
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--
David J. Disabato
Clinical Psychology Doctoral Student

George Mason University
[hidden email]
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Social Research Consultants

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David J. Disabato
Clinical Psychology Doctoral Student

George Mason University

[hidden email]
If you reply to this email, your message will be added to the discussion below:
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Maguin, Eugene

Re: SPSS factor scores

In reply to this post by SabatoPsy

Would you describe your model please in enough detail that anybody could recreate your analysis. Or, post the amos syntax or post an image of the model picture if you used the graphic interface to define the model. Gene Maguin

From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of SabatoPsy
Sent: Tuesday, December 03, 2013 9:04 PM
To: [hidden email]
Subject: Re: SPSS factor scores

On Tue, Dec 3, 2013 at 8:03 PM, Art Kendall [via SPSSX Discussion] <[hidden email]> wrote:

Is there system missing data? (sysmis)

Art Kendall

Social Research Consultants

On 12/3/2013 5:44 PM, SabatoPsy [via SPSSX Discussion] wrote:

I just checked in my SPSS dataset. There is no user-missing data. The indicators correlation between .40 and .75 of one another. Good thinking. I am going to look into trying to print out the correlation matrix from AMOS. That should be helpful in determining what might be going on.
On Tue, Dec 3, 2013 at 4:52 PM, Maguin, Eugene [via SPSSX Discussion] <[hidden email]> wrote:
Ok, let’s get the ‘slap your forehead’ question out of the way first. Is missing data correctly accounted for, i.e., is there any possibility that user missing values are being treated as nonmissing? Is there any possibility that one indicator is a near duplicate of another or a composite of several indicators?

Let’s assume not. Next. I’m not familiar with Amos since I use mplus but if amos can print out the covariance or correlation matrix for the indicators, does that matrix match the matrix that spss computes? Amos is FIML and spss is not and the numbers should be very similar unless you have large amounts of missing data that are not missing completely at random and is ‘strongly’ correlated with model covariates. Lastly, if you have a correlation of .99 between factors, the correlations between the two factors’ indicators have to be extremely high, like in the upper 90’s, I’d guess. If this is true, then I’d guess you have variable construction or conceptualization problems.

Gene Maguin

From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of SabatoPsy
Sent: Tuesday, December 03, 2013 4:30 PM

To: [hidden email]
Subject: Re: SPSS factor scores

Good questions. The correlations are strange because they are so high. The literature would suggest they be highly correlated (around .70), however, I am getting correlations of .99. Everything you said is true except for good model fit. These are correlations from raw data. I have an SPSS file with no missing data. One of the factor loadings is set to 1.0 for each factor. These are standardized correlation coefficients. The model fit is poor though. I have a CFI of .77 and RMSEA of .16. Could having poor model fit change/bias my correlation estimates?
On Tue, Dec 3, 2013 at 4:18 PM, Maguin, Eugene [via SPSSX Discussion] <[hidden email]> wrote:
Let’s back up to where the problem begins, which seems to be at the SEM stage because you are reporting “strange correlations” between factors. So, what’s strange about the correlations? And, just so that we all are on the same page, please clarify your use of ‘correlations’ in the context of an SEM. Affirmatively verify that the following sequence is true. Analyzing raw data not a covariance or correlation matrix. One loading fixed at 1.00 for each factor rather than factor variances fixed at 1.00. Fit of the model is acceptable under current standards. The strange values are standardized values and not unstandardized values.

Gene Maguin

From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of SabatoPsy
Sent: Tuesday, December 03, 2013 3:56 PM
To: [hidden email]

Subject: Re: SPSS factor scores

Thank you for all of your posts. I looked up online how the factor scores are computed in SPSS in the IBM labyrinth. I got a bunch of matrix algebra and calculus notation that I am not smart enough to follow. I did however do an experiment on my own as was also suggested. I found that there are small differences between factor scores saved via the PCA method of extraction compared to the PAF and ML extraction methods; however, there were negligible differences between the PAF and ML methods. I tested this by correlating different saved factor score variables from the exact same factor structure but with different extraction methods.

To address the question of why I am creating the factor(s) may go outside the scope of this listserve, but I will gladly take any advice/knowledge. I am running some SEM models in AMOS and am getting strange correlations between my factors. I wanted a way to test whether indeed these correlations between factors are accurate. I thought that by correlating the saved factor scores in SPSS using the ML extraction method, I should get the same correlations I got in AMOS. I am creating multiple single factors from a multiple set of 4 manifest indicators to create 5 total factors. To my knowledge, an EFA using ML and a CFA create essentially (minus negligible estimation differences) the same factor scores (when you are creating a single factor using all indicators). Therefore correlations between the SPSS factor scores I created and correlations between the latent factors I created in AMOS should be essentially equivalent. However, I am getting substantially different correlations. Does anybody know what is going on... or what I am doing wrong... or why my belief that the correlations should be the same is wrong??? I would greatly appreciate any enlightenment.

David Disabato

George Mason University
On Tue, Dec 3, 2013 at 12:26 PM, Art Kendall [via SPSSX Discussion] <[hidden email]> wrote:
What is the purpose of your using FACTOR? Data reduction? Creating scales? etc.

What are you going to use the factor scores for?
Art Kendall
Social Research Consultants
On 12/3/2013 11:42 AM, SabatoPsy [via SPSSX Discussion] wrote:
Hi Listserve,

I am wondering how the SPSS factor scores in the "Factor" command are calculated. More specifically, does the method of extraction change the calculated factor scores? I want to compare the factor scores created by a PCA compared to an EFA (with ML estimation). Is this possible in SPSS? I know there are three ways of calculating the factor scores (Regression, Bartlett, Anderson-Rubin). Let us assume I tell SPSS to use the same way: Regression. If I tell SPSS to create "Regression" factor scores with a principle component method of extraction, will they be different than the "Regression" factor scores with a maximum likelihood method of extraction?

Thank you,

David Disabato
George Mason University

If you reply to this email, your message will be added to the discussion below:

http://spssx-discussion.1045642.n5.nabble.com/SPSS-factor-scores-tp5723392.html

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To unsubscribe from SPSSX Discussion, click here.
NAML

Art Kendall
Social Research Consultants

If you reply to this email, your message will be added to the discussion below:

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--
David J. Disabato

Clinical Psychology Doctoral Student

George Mason University
[hidden email]
View this message in context: Re: SPSS factor scores

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View this message in context: Re: SPSS factor scores
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SabatoPsy

Re: SPSS factor scores

Attached is an AMOS graphics picture of the model with the standardized parameter estimates. The data is from an observational psychology study of participants completing online questionnaires repeatedly over the course of one year. The data is in wide format and thus each case is a participant. I am performing a cross-lagged autoregressive model between gratitude and well-being. Gratitude and well-being are measured the same across 5 time points, each 3 months apart. I was given a dataset with list-wise deletion of the data such that only participants who had all 5 time points are included. I am not sure how many original participants there were, but this dataset has 851. Gratitude is a manifest variable, and well-being a latent variable created from 4 validated scale sum totals: life satisfaction, two happiness scales, and depression. My concern is that I have standardized estimates connecting the well-being factors across time (i.e., well-being stability paths) that range from .97 - 1.03. These are the largest regression coefficient magnitudes I have ever had, and I am therefore spectacle. I also attached the model fit output.

On Wed, Dec 4, 2013 at 9:37 AM, Maguin, Eugene [via SPSSX Discussion] <[hidden email]> wrote:

Would you describe your model please in enough detail that anybody could recreate your analysis. Or, post the amos syntax or post an image of the model picture if you used the graphic interface to define the model. Gene Maguin

From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of SabatoPsy
Sent: Tuesday, December 03, 2013 9:04 PM

To: [hidden email]
Subject: Re: SPSS factor scores

Good question. No there is not. This is a data set I created from only the complete cases of my study. Because someone mentioned model fit, I am beginning to wonder if that is what is going on. I know I have poor model it. I read online that if you have poor model fit, your model is most likely misspecified, which can lead to biased parameter estimates (i.e. correlations). After all, SEM is solving regression equations simultaneously. Does this jive with what you know about SEM and AMOS? Again I am sorry that this is leading away from the content of this listserv.
On Tue, Dec 3, 2013 at 8:03 PM, Art Kendall [via SPSSX Discussion] <[hidden email]> wrote:
Is there system missing data? (sysmis)
Art Kendall
Social Research Consultants
On 12/3/2013 5:44 PM, SabatoPsy [via SPSSX Discussion] wrote:
I just checked in my SPSS dataset. There is no user-missing data. The indicators correlation between .40 and .75 of one another. Good thinking. I am going to look into trying to print out the correlation matrix from AMOS. That should be helpful in determining what might be going on.
On Tue, Dec 3, 2013 at 4:52 PM, Maguin, Eugene [via SPSSX Discussion] <[hidden email]> wrote:
Ok, let’s get the ‘slap your forehead’ question out of the way first. Is missing data correctly accounted for, i.e., is there any possibility that user missing values are being treated as nonmissing? Is there any possibility that one indicator is a near duplicate of another or a composite of several indicators?

Let’s assume not. Next. I’m not familiar with Amos since I use mplus but if amos can print out the covariance or correlation matrix for the indicators, does that matrix match the matrix that spss computes? Amos is FIML and spss is not and the numbers should be very similar unless you have large amounts of missing data that are not missing completely at random and is ‘strongly’ correlated with model covariates. Lastly, if you have a correlation of .99 between factors, the correlations between the two factors’ indicators have to be extremely high, like in the upper 90’s, I’d guess. If this is true, then I’d guess you have variable construction or conceptualization problems.

Gene Maguin

From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of SabatoPsy
Sent: Tuesday, December 03, 2013 4:30 PM

To: [hidden email]
Subject: Re: SPSS factor scores

Good questions. The correlations are strange because they are so high. The literature would suggest they be highly correlated (around .70), however, I am getting correlations of .99. Everything you said is true except for good model fit. These are correlations from raw data. I have an SPSS file with no missing data. One of the factor loadings is set to 1.0 for each factor. These are standardized correlation coefficients. The model fit is poor though. I have a CFI of .77 and RMSEA of .16. Could having poor model fit change/bias my correlation estimates?
On Tue, Dec 3, 2013 at 4:18 PM, Maguin, Eugene [via SPSSX Discussion] <[hidden email]> wrote:
Let’s back up to where the problem begins, which seems to be at the SEM stage because you are reporting “strange correlations” between factors. So, what’s strange about the correlations? And, just so that we all are on the same page, please clarify your use of ‘correlations’ in the context of an SEM. Affirmatively verify that the following sequence is true. Analyzing raw data not a covariance or correlation matrix. One loading fixed at 1.00 for each factor rather than factor variances fixed at 1.00. Fit of the model is acceptable under current standards. The strange values are standardized values and not unstandardized values.

Gene Maguin

From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of SabatoPsy
Sent: Tuesday, December 03, 2013 3:56 PM
To: [hidden email]

Subject: Re: SPSS factor scores

Thank you for all of your posts. I looked up online how the factor scores are computed in SPSS in the IBM labyrinth. I got a bunch of matrix algebra and calculus notation that I am not smart enough to follow. I did however do an experiment on my own as was also suggested. I found that there are small differences between factor scores saved via the PCA method of extraction compared to the PAF and ML extraction methods; however, there were negligible differences between the PAF and ML methods. I tested this by correlating different saved factor score variables from the exact same factor structure but with different extraction methods.

To address the question of why I am creating the factor(s) may go outside the scope of this listserve, but I will gladly take any advice/knowledge. I am running some SEM models in AMOS and am getting strange correlations between my factors. I wanted a way to test whether indeed these correlations between factors are accurate. I thought that by correlating the saved factor scores in SPSS using the ML extraction method, I should get the same correlations I got in AMOS. I am creating multiple single factors from a multiple set of 4 manifest indicators to create 5 total factors. To my knowledge, an EFA using ML and a CFA create essentially (minus negligible estimation differences) the same factor scores (when you are creating a single factor using all indicators). Therefore correlations between the SPSS factor scores I created and correlations between the latent factors I created in AMOS should be essentially equivalent. However, I am getting substantially different correlations. Does anybody know what is going on... or what I am doing wrong... or why my belief that the correlations should be the same is wrong??? I would greatly appreciate any enlightenment.

David Disabato

George Mason University
On Tue, Dec 3, 2013 at 12:26 PM, Art Kendall [via SPSSX Discussion] <[hidden email]> wrote:
What is the purpose of your using FACTOR? Data reduction? Creating scales? etc.

What are you going to use the factor scores for?
Art Kendall
Social Research Consultants
On 12/3/2013 11:42 AM, SabatoPsy [via SPSSX Discussion] wrote:
Hi Listserve,

I am wondering how the SPSS factor scores in the "Factor" command are calculated. More specifically, does the method of extraction change the calculated factor scores? I want to compare the factor scores created by a PCA compared to an EFA (with ML estimation). Is this possible in SPSS? I know there are three ways of calculating the factor scores (Regression, Bartlett, Anderson-Rubin). Let us assume I tell SPSS to use the same way: Regression. If I tell SPSS to create "Regression" factor scores with a principle component method of extraction, will they be different than the "Regression" factor scores with a maximum likelihood method of extraction?

Thank you,

David Disabato
George Mason University

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George Mason University
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Screenshot 2013-12-04 10.14.01.png (339K) Download Attachment

Screenshot 2013-12-04 10.27.00.png (99K) Download Attachment

Maguin, Eugene

Re: SPSS factor scores

In reply to this post by SabatoPsy

The list does not accept attachments. Did the attachments show up in nabble?

Perhaps you’ve done this already but if not, I’d take the model apart and check out the well-being (WB) measurement model. Just allow the factors to be correlated. That will show you the factor covariances/correlations. You are fitting an autoregressive structure to that so the thing is that the autoregressive coefficients have to reproduce the factor cov/corr matrix and they may not. (Same issue applies to the gratitude line.) Gene Maguin

By the way, does the amos corrs match the spss corrs?

From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of SabatoPsy
Sent: Tuesday, December 03, 2013 9:04 PM
To: [hidden email]
Subject: Re: SPSS factor scores

On Tue, Dec 3, 2013 at 8:03 PM, Art Kendall [via SPSSX Discussion] <[hidden email]> wrote:

Is there system missing data? (sysmis)

Art Kendall

Social Research Consultants

On 12/3/2013 5:44 PM, SabatoPsy [via SPSSX Discussion] wrote:

I just checked in my SPSS dataset. There is no user-missing data. The indicators correlation between .40 and .75 of one another. Good thinking. I am going to look into trying to print out the correlation matrix from AMOS. That should be helpful in determining what might be going on.
On Tue, Dec 3, 2013 at 4:52 PM, Maguin, Eugene [via SPSSX Discussion] <[hidden email]> wrote:
Ok, let’s get the ‘slap your forehead’ question out of the way first. Is missing data correctly accounted for, i.e., is there any possibility that user missing values are being treated as nonmissing? Is there any possibility that one indicator is a near duplicate of another or a composite of several indicators?

Let’s assume not. Next. I’m not familiar with Amos since I use mplus but if amos can print out the covariance or correlation matrix for the indicators, does that matrix match the matrix that spss computes? Amos is FIML and spss is not and the numbers should be very similar unless you have large amounts of missing data that are not missing completely at random and is ‘strongly’ correlated with model covariates. Lastly, if you have a correlation of .99 between factors, the correlations between the two factors’ indicators have to be extremely high, like in the upper 90’s, I’d guess. If this is true, then I’d guess you have variable construction or conceptualization problems.

Gene Maguin

From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of SabatoPsy
Sent: Tuesday, December 03, 2013 4:30 PM

To: [hidden email]
Subject: Re: SPSS factor scores

Good questions. The correlations are strange because they are so high. The literature would suggest they be highly correlated (around .70), however, I am getting correlations of .99. Everything you said is true except for good model fit. These are correlations from raw data. I have an SPSS file with no missing data. One of the factor loadings is set to 1.0 for each factor. These are standardized correlation coefficients. The model fit is poor though. I have a CFI of .77 and RMSEA of .16. Could having poor model fit change/bias my correlation estimates?
On Tue, Dec 3, 2013 at 4:18 PM, Maguin, Eugene [via SPSSX Discussion] <[hidden email]> wrote:
Let’s back up to where the problem begins, which seems to be at the SEM stage because you are reporting “strange correlations” between factors. So, what’s strange about the correlations? And, just so that we all are on the same page, please clarify your use of ‘correlations’ in the context of an SEM. Affirmatively verify that the following sequence is true. Analyzing raw data not a covariance or correlation matrix. One loading fixed at 1.00 for each factor rather than factor variances fixed at 1.00. Fit of the model is acceptable under current standards. The strange values are standardized values and not unstandardized values.

Gene Maguin

From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of SabatoPsy
Sent: Tuesday, December 03, 2013 3:56 PM
To: [hidden email]

Subject: Re: SPSS factor scores

Thank you for all of your posts. I looked up online how the factor scores are computed in SPSS in the IBM labyrinth. I got a bunch of matrix algebra and calculus notation that I am not smart enough to follow. I did however do an experiment on my own as was also suggested. I found that there are small differences between factor scores saved via the PCA method of extraction compared to the PAF and ML extraction methods; however, there were negligible differences between the PAF and ML methods. I tested this by correlating different saved factor score variables from the exact same factor structure but with different extraction methods.

To address the question of why I am creating the factor(s) may go outside the scope of this listserve, but I will gladly take any advice/knowledge. I am running some SEM models in AMOS and am getting strange correlations between my factors. I wanted a way to test whether indeed these correlations between factors are accurate. I thought that by correlating the saved factor scores in SPSS using the ML extraction method, I should get the same correlations I got in AMOS. I am creating multiple single factors from a multiple set of 4 manifest indicators to create 5 total factors. To my knowledge, an EFA using ML and a CFA create essentially (minus negligible estimation differences) the same factor scores (when you are creating a single factor using all indicators). Therefore correlations between the SPSS factor scores I created and correlations between the latent factors I created in AMOS should be essentially equivalent. However, I am getting substantially different correlations. Does anybody know what is going on... or what I am doing wrong... or why my belief that the correlations should be the same is wrong??? I would greatly appreciate any enlightenment.

David Disabato

George Mason University
On Tue, Dec 3, 2013 at 12:26 PM, Art Kendall [via SPSSX Discussion] <[hidden email]> wrote:
What is the purpose of your using FACTOR? Data reduction? Creating scales? etc.

What are you going to use the factor scores for?
Art Kendall
Social Research Consultants
On 12/3/2013 11:42 AM, SabatoPsy [via SPSSX Discussion] wrote:
Hi Listserve,

I am wondering how the SPSS factor scores in the "Factor" command are calculated. More specifically, does the method of extraction change the calculated factor scores? I want to compare the factor scores created by a PCA compared to an EFA (with ML estimation). Is this possible in SPSS? I know there are three ways of calculating the factor scores (Regression, Bartlett, Anderson-Rubin). Let us assume I tell SPSS to use the same way: Regression. If I tell SPSS to create "Regression" factor scores with a principle component method of extraction, will they be different than the "Regression" factor scores with a maximum likelihood method of extraction?

Thank you,

David Disabato
George Mason University

If you reply to this email, your message will be added to the discussion below:

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Art Kendall
Social Research Consultants

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David J. Disabato

Clinical Psychology Doctoral Student

George Mason University
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View this message in context: Re: SPSS factor scores

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David J. Disabato

Clinical Psychology Doctoral Student

George Mason University

[hidden email]
View this message in context: Re: SPSS factor scores
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Clinical Psychology Doctoral Student

George Mason University
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Clinical Psychology Doctoral Student

George Mason University

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View this message in context: Re: SPSS factor scores
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SabatoPsy

Re: SPSS factor scores

I am not exactly sure what nabble is, however the attachments can be found on the website thread: http://spssx-discussion.1045642.n5.nabble.com/SPSS-factor-scores-td5723392.html. I did what you suggested and created a measurement model. Here are my two correlation matrices:

SPSS:	WB1	WB2	WB3	WB4	WB5
WB1	-
WB2	0.81	-
WB3	0.75	0.8	-
WB4	0.76	0.76	0.77	-
WB5	0.76	0.76	0.76	0.8	-

AMOS:	WB1	WB2	WB3	WB4	WB5
WB1	-
WB2	0.86	-
WB3	0.82	0.87	-
WB4	0.82	0.82	0.84	-
WB5	0.82	0.82	0.82	0.87	-

As you can see the comparisons within the matrix are similar, but the actual magnitudes are different such that the SPSS correlations are slightly weaker. That difference is what is confusing me. Thank you for suggesting doing a measurement model though. The correlations between well-being in AMOS changed from .99 to in the .8's.

On Wed, Dec 4, 2013 at 3:49 PM, Maguin, Eugene [via SPSSX Discussion] <[hidden email]> wrote:

The list does not accept attachments. Did the attachments show up in nabble?

Perhaps you’ve done this already but if not, I’d take the model apart and check out the well-being (WB) measurement model. Just allow the factors to be correlated. That will show you the factor covariances/correlations. You are fitting an autoregressive structure to that so the thing is that the autoregressive coefficients have to reproduce the factor cov/corr matrix and they may not. (Same issue applies to the gratitude line.) Gene Maguin

By the way, does the amos corrs match the spss corrs?

From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of SabatoPsy
Sent: Tuesday, December 03, 2013 9:04 PM

To: [hidden email]
Subject: Re: SPSS factor scores

Good question. No there is not. This is a data set I created from only the complete cases of my study. Because someone mentioned model fit, I am beginning to wonder if that is what is going on. I know I have poor model it. I read online that if you have poor model fit, your model is most likely misspecified, which can lead to biased parameter estimates (i.e. correlations). After all, SEM is solving regression equations simultaneously. Does this jive with what you know about SEM and AMOS? Again I am sorry that this is leading away from the content of this listserv.
On Tue, Dec 3, 2013 at 8:03 PM, Art Kendall [via SPSSX Discussion] <[hidden email]> wrote:
Is there system missing data? (sysmis)
Art Kendall
Social Research Consultants
On 12/3/2013 5:44 PM, SabatoPsy [via SPSSX Discussion] wrote:
I just checked in my SPSS dataset. There is no user-missing data. The indicators correlation between .40 and .75 of one another. Good thinking. I am going to look into trying to print out the correlation matrix from AMOS. That should be helpful in determining what might be going on.
On Tue, Dec 3, 2013 at 4:52 PM, Maguin, Eugene [via SPSSX Discussion] <[hidden email]> wrote:
Ok, let’s get the ‘slap your forehead’ question out of the way first. Is missing data correctly accounted for, i.e., is there any possibility that user missing values are being treated as nonmissing? Is there any possibility that one indicator is a near duplicate of another or a composite of several indicators?

Let’s assume not. Next. I’m not familiar with Amos since I use mplus but if amos can print out the covariance or correlation matrix for the indicators, does that matrix match the matrix that spss computes? Amos is FIML and spss is not and the numbers should be very similar unless you have large amounts of missing data that are not missing completely at random and is ‘strongly’ correlated with model covariates. Lastly, if you have a correlation of .99 between factors, the correlations between the two factors’ indicators have to be extremely high, like in the upper 90’s, I’d guess. If this is true, then I’d guess you have variable construction or conceptualization problems.

Gene Maguin

From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of SabatoPsy
Sent: Tuesday, December 03, 2013 4:30 PM

To: [hidden email]
Subject: Re: SPSS factor scores

Good questions. The correlations are strange because they are so high. The literature would suggest they be highly correlated (around .70), however, I am getting correlations of .99. Everything you said is true except for good model fit. These are correlations from raw data. I have an SPSS file with no missing data. One of the factor loadings is set to 1.0 for each factor. These are standardized correlation coefficients. The model fit is poor though. I have a CFI of .77 and RMSEA of .16. Could having poor model fit change/bias my correlation estimates?
On Tue, Dec 3, 2013 at 4:18 PM, Maguin, Eugene [via SPSSX Discussion] <[hidden email]> wrote:
Let’s back up to where the problem begins, which seems to be at the SEM stage because you are reporting “strange correlations” between factors. So, what’s strange about the correlations? And, just so that we all are on the same page, please clarify your use of ‘correlations’ in the context of an SEM. Affirmatively verify that the following sequence is true. Analyzing raw data not a covariance or correlation matrix. One loading fixed at 1.00 for each factor rather than factor variances fixed at 1.00. Fit of the model is acceptable under current standards. The strange values are standardized values and not unstandardized values.

Gene Maguin

From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of SabatoPsy
Sent: Tuesday, December 03, 2013 3:56 PM
To: [hidden email]

Subject: Re: SPSS factor scores

Thank you for all of your posts. I looked up online how the factor scores are computed in SPSS in the IBM labyrinth. I got a bunch of matrix algebra and calculus notation that I am not smart enough to follow. I did however do an experiment on my own as was also suggested. I found that there are small differences between factor scores saved via the PCA method of extraction compared to the PAF and ML extraction methods; however, there were negligible differences between the PAF and ML methods. I tested this by correlating different saved factor score variables from the exact same factor structure but with different extraction methods.

To address the question of why I am creating the factor(s) may go outside the scope of this listserve, but I will gladly take any advice/knowledge. I am running some SEM models in AMOS and am getting strange correlations between my factors. I wanted a way to test whether indeed these correlations between factors are accurate. I thought that by correlating the saved factor scores in SPSS using the ML extraction method, I should get the same correlations I got in AMOS. I am creating multiple single factors from a multiple set of 4 manifest indicators to create 5 total factors. To my knowledge, an EFA using ML and a CFA create essentially (minus negligible estimation differences) the same factor scores (when you are creating a single factor using all indicators). Therefore correlations between the SPSS factor scores I created and correlations between the latent factors I created in AMOS should be essentially equivalent. However, I am getting substantially different correlations. Does anybody know what is going on... or what I am doing wrong... or why my belief that the correlations should be the same is wrong??? I would greatly appreciate any enlightenment.

David Disabato

George Mason University
On Tue, Dec 3, 2013 at 12:26 PM, Art Kendall [via SPSSX Discussion] <[hidden email]> wrote:
What is the purpose of your using FACTOR? Data reduction? Creating scales? etc.

What are you going to use the factor scores for?
Art Kendall
Social Research Consultants
On 12/3/2013 11:42 AM, SabatoPsy [via SPSSX Discussion] wrote:
Hi Listserve,

I am wondering how the SPSS factor scores in the "Factor" command are calculated. More specifically, does the method of extraction change the calculated factor scores? I want to compare the factor scores created by a PCA compared to an EFA (with ML estimation). Is this possible in SPSS? I know there are three ways of calculating the factor scores (Regression, Bartlett, Anderson-Rubin). Let us assume I tell SPSS to use the same way: Regression. If I tell SPSS to create "Regression" factor scores with a principle component method of extraction, will they be different than the "Regression" factor scores with a maximum likelihood method of extraction?

Thank you,

David Disabato
George Mason University

If you reply to this email, your message will be added to the discussion below:

http://spssx-discussion.1045642.n5.nabble.com/SPSS-factor-scores-tp5723392.html

To start a new topic under SPSSX Discussion, email [hidden email]
To unsubscribe from SPSSX Discussion, click here.
NAML

Art Kendall
Social Research Consultants

If you reply to this email, your message will be added to the discussion below:

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--
David J. Disabato

Clinical Psychology Doctoral Student

George Mason University
[hidden email]
View this message in context: Re: SPSS factor scores

Sent from the SPSSX Discussion mailing list archive at Nabble.com.
If you reply to this email, your message will be added to the discussion below:

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--
David J. Disabato

Clinical Psychology Doctoral Student

George Mason University

[hidden email]
View this message in context: Re: SPSS factor scores
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View this message in context: Re: SPSS factor scores
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David Marso

Re: SPSS factor scores

Administrator

You don't provide any info as to what generated these two matrices. I am NOT game to applying any ESPss to doing any Sherlock Holmes thing. I eyeballed your path model and you need to make it easier for people to assist. That is the fugliest monstrosity I have seen for a very long time (I'm not going to spend a lot of time disentangling that spider web). Redraw it so time goes either top down or left to right and eliminate all the cross-noise. Have you thought of going back to basics and seeing if the single time models fit (If they don't you are basically wasting your time)? If so are the models invariant over time? The fact that the betas connecting the sequential time points are roughly 1.0 I suspect they are (but it might in fact be crap replicating crap). OTOH, you have a dataset which has been created based on all complete data over several times. You in all likelihood have a VERY restricted self selected group of robotic compliant respondents.
BTW: nabble is where you apparently posted this ;-)

SabatoPsy wrote

I am not exactly sure what nabble is, however the attachments can be found
on the website thread:
http://spssx-discussion.1045642.n5.nabble.com/SPSS-factor-scores-td5723392.html.
I did what you suggested and created a measurement model. Here are my two
correlation matrices:

SPSS: WB1 WB2 WB3 WB4 WB5 WB1 - WB2 0.81 - WB3 0.75 0.8 - WB4 0.76
0.76 0.77 - WB5 0.76 0.76 0.76 0.8 -
AMOS: WB1 WB2 WB3 WB4 WB5 WB1 - WB2 0.86 - WB3 0.82 0.87 - WB4 0.82
0.82 0.84 - WB5 0.82 0.82 0.82 0.87 -
As you can see the comparisons within the matrix are similar, but the
actual magnitudes are different such that the SPSS correlations are
slightly weaker. That difference is what is confusing me. Thank you for
suggesting doing a measurement model though. The correlations between
well-being in AMOS changed from .99 to in the .8's.

On Wed, Dec 4, 2013 at 3:49 PM, Maguin, Eugene [via SPSSX Discussion] <
[hidden email]> wrote:

> The list does not accept attachments. Did the attachments show up in
> nabble?
>
>
>
> Perhaps you’ve done this already but if not, I’d take the model apart and
> check out the well-being (WB) measurement model. Just allow the factors to
> be correlated. That will show you the factor covariances/correlations. You
> are fitting an autoregressive structure to that so the thing is that the
> autoregressive coefficients have to reproduce the factor cov/corr matrix
> and they may not. (Same issue applies to the gratitude line.) Gene Maguin
>
>
>
> By the way, does the amos corrs match the spss corrs?
>
>
>
> *From:* SPSSX(r) Discussion [mailto:[hidden email]<http://user/SendEmail.jtp?type=node&node=5723437&i=0>]
> *On Behalf Of *SabatoPsy
> *Sent:* Tuesday, December 03, 2013 9:04 PM
>
> *To:* [hidden email]<http://user/SendEmail.jtp?type=node&node=5723437&i=1>
> *Subject:* Re: SPSS factor scores
>
>
>
> Good question. No there is not. This is a data set I created from only the
> complete cases of my study. Because someone mentioned model fit, I am
> beginning to wonder if that is what is going on. I know I have poor model
> it. I read online that if you have poor model fit, your model is most
> likely misspecified, which can lead to biased parameter estimates (i.e.
> correlations). After all, SEM is solving regression equations
> *simultaneously.* Does this jive with what you know about SEM and AMOS?
> Again I am sorry that this is leading away from the content of this
> listserv.
>
>
>
> On Tue, Dec 3, 2013 at 8:03 PM, Art Kendall [via SPSSX Discussion] <[hidden
> email] <http://user/SendEmail.jtp?type=node&node=5723411&i=0>> wrote:
>
> Is there system missing data? (sysmis)
>
>
>
> Art Kendall
>
> Social Research Consultants
>
> On 12/3/2013 5:44 PM, SabatoPsy [via SPSSX Discussion] wrote:
>
> I just checked in my SPSS dataset. There is no user-missing data. The
> indicators correlation between .40 and .75 of one another. Good thinking. I
> am going to look into trying to print out the correlation matrix from AMOS.
> That should be helpful in determining what might be going on.
>
>
>
> On Tue, Dec 3, 2013 at 4:52 PM, Maguin, Eugene [via SPSSX Discussion] <[hidden
> email] <http://user/SendEmail.jtp?type=node&node=5723407&i=0>> wrote:
>
> Ok, let’s get the ‘slap your forehead’ question out of the way first. Is
> missing data correctly accounted for, i.e., is there any possibility that
> user missing values are being treated as nonmissing? Is there any
> possibility that one indicator is a near duplicate of another or a
> composite of several indicators?
>
>
>
> Let’s assume not. Next. I’m not familiar with Amos since I use mplus but
> if amos can print out the covariance or correlation matrix for the
> indicators, does that matrix match the matrix that spss computes? Amos is
> FIML and spss is not and the numbers should be very similar unless you have
> large amounts of missing data that are not missing completely at random and
> is ‘strongly’ correlated with model covariates. Lastly, if you have a
> correlation of .99 between factors, the correlations between the two
> factors’ indicators have to be extremely high, like in the upper 90’s, I’d
> guess. If this is true, then I’d guess you have variable construction or
> conceptualization problems.
>
>
>
> Gene Maguin
>
>
>
>
>
> *From:* SPSSX(r) Discussion [mailto:[hidden email]<http://user/SendEmail.jtp?type=node&node=5723406&i=0>]
> *On Behalf Of *SabatoPsy
> *Sent:* Tuesday, December 03, 2013 4:30 PM
>
>
> *To:* [hidden email]<http://user/SendEmail.jtp?type=node&node=5723406&i=1>
> *Subject:* Re: SPSS factor scores
>
>
>
> Good questions. The correlations are strange because they are so high. The
> literature would suggest they be highly correlated (around .70), however, I
> am getting correlations of .99. Everything you said is true except for good
> model fit. These are correlations from raw data. I have an SPSS file with
> no missing data. One of the factor loadings is set to 1.0 for each factor.
> These are *standardized* correlation coefficients. The model fit is poor
> though. I have a CFI of .77 and RMSEA of .16. Could having poor model fit
> change/bias my correlation estimates?
>
>
>
> On Tue, Dec 3, 2013 at 4:18 PM, Maguin, Eugene [via SPSSX Discussion] <[hidden
> email] <http://user/SendEmail.jtp?type=node&node=5723405&i=0>> wrote:
>
> Let’s back up to where the problem begins, which seems to be at the SEM
> stage because you are reporting “strange correlations” between factors. So,
> what’s strange about the correlations? And, just so that we all are on the
> same page, please clarify your use of ‘correlations’ in the context of an
> SEM. Affirmatively verify that the following sequence is true. Analyzing
> raw data not a covariance or correlation matrix. One loading fixed at 1.00
> for each factor rather than factor variances fixed at 1.00. Fit of the
> model is acceptable under current standards. The strange values are
> standardized values and not unstandardized values.
>
> Gene Maguin
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> *From:* SPSSX(r) Discussion [mailto:[hidden email]<http://user/SendEmail.jtp?type=node&node=5723403&i=0>]
> *On Behalf Of *SabatoPsy
> *Sent:* Tuesday, December 03, 2013 3:56 PM
> *To:* [hidden email]<http://user/SendEmail.jtp?type=node&node=5723403&i=1>
>
>
> *Subject:* Re: SPSS factor scores
>
>
>
> Thank you for all of your posts. I looked up online how the factor scores
> are computed in SPSS in the IBM labyrinth. I got a bunch of matrix algebra
> and calculus notation that I am not smart enough to follow. I did however
> do an experiment on my own as was also suggested. I found that there are
> small differences between factor scores saved via the PCA method of
> extraction compared to the PAF and ML extraction methods; however, there
> were negligible differences between the PAF and ML methods. I tested this
> by correlating different saved factor score variables from the exact same
> factor structure but with different extraction methods.
>
>
>
> To address the question of why I am creating the factor(s) may go outside
> the scope of this listserve, but I will gladly take any advice/knowledge. I
> am running some SEM models in AMOS and am getting strange correlations
> between my factors. I wanted a way to test whether indeed these
> correlations between factors are accurate. I thought that by correlating
> the saved factor scores in SPSS using the ML extraction method, I should
> get the same correlations I got in AMOS. I am creating multiple single
> factors from a multiple set of 4 manifest indicators to create 5 total
> factors. To my knowledge, an EFA using ML and a CFA create essentially
> (minus negligible estimation differences) the same factor scores (when you
> are creating a single factor using all indicators). Therefore correlations
> between the SPSS factor scores I created and correlations between the
> latent factors I created in AMOS should be essentially equivalent. However,
> I am getting substantially different correlations. Does anybody know what
> is going on... or what I am doing wrong... or why my belief that the
> correlations should be the same is wrong??? I would greatly appreciate any
> enlightenment.
>
>
>
> David Disabato
>
> George Mason University
>
>
>
> On Tue, Dec 3, 2013 at 12:26 PM, Art Kendall [via SPSSX Discussion] <[hidden
> email] <http://user/SendEmail.jtp?type=node&node=5723401&i=0>> wrote:
>
> What is the purpose of your using FACTOR? Data reduction? Creating scales?
> etc.
>
> What are you going to use the factor scores for?
>
>
> Art Kendall
>
> Social Research Consultants
>
> On 12/3/2013 11:42 AM, SabatoPsy [via SPSSX Discussion] wrote:
>
> Hi Listserve,
>
> I am wondering how the SPSS factor scores in the "Factor" command are
> calculated. More specifically, does the method of extraction change the
> calculated factor scores? I want to compare the factor scores created by a
> PCA compared to an EFA (with ML estimation). Is this possible in SPSS? I
> know there are three ways of calculating the factor scores (Regression,
> Bartlett, Anderson-Rubin). Let us assume I tell SPSS to use the same way:
> Regression. If I tell SPSS to create "Regression" factor scores with a
> principle component method of extraction, will they be different than the
> "Regression" factor scores with a maximum likelihood method of extraction?
>
> Thank you,
>
> David Disabato
> George Mason University
> ------------------------------
>
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> Social Research Consultants
>
>
> ------------------------------
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> --
> David J. Disabato
>
> Clinical Psychology Doctoral Student
>
> George Mason University
>
> [hidden email] <http://user/SendEmail.jtp?type=node&node=5723401&i=1>
>
>
> ------------------------------
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> --
> David J. Disabato
>
> Clinical Psychology Doctoral Student
>
> George Mason University
>
> [hidden email] <http://user/SendEmail.jtp?type=node&node=5723405&i=1>
>
>
> ------------------------------
>
> View this message in context: Re: SPSS factor scores<http://spssx-discussion.1045642.n5.nabble.com/SPSS-factor-scores-tp5723392p5723405.html>
> Sent from the SPSSX Discussion mailing list archive<http://spssx-discussion.1045642.n5.nabble.com/>at Nabble.com.
>
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>
>
> --
> David J. Disabato
>
> Clinical Psychology Doctoral Student
>
> George Mason University
>
> [hidden email] <http://user/SendEmail.jtp?type=node&node=5723407&i=1>
>
>
> ------------------------------
>
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>
>
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>
>
> --
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>
> Clinical Psychology Doctoral Student
>
> George Mason University
>
> [hidden email] <http://user/SendEmail.jtp?type=node&node=5723411&i=1>
>
>
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--
David J. Disabato
Clinical Psychology Doctoral Student
George Mason University
[hidden email]

SabatoPsy

Re: SPSS factor scores

I understand. I think that this question is more complicated that I thought and is best suited for someone in the statistics department at my University who I can meet with in person. I appreciate everyone's time and effort in helping me. I do feel like I have a better understanding of my data (and the original purpose of this post - factor scores) after discussion from this listserv.

On Wed, Dec 4, 2013 at 9:43 PM, David Marso [via SPSSX Discussion] <[hidden email]> wrote:

You don't provide any info as to what generated these two matrices. I am NOT game to applying any ESPss to doing any Sherlock Holmes thing. I eyeballed your path model and you need to make it easier for people to assist. That is the fugliest monstrosity I have seen for a very long time (I'm not going to spend a lot of time disentangling that spider web). Redraw it so time goes either top down or left to right and eliminate all the cross-noise. Have you thought of going back to basics and seeing if the single time models fit (If they don't you are basically wasting your time)? If so are the models invariant over time? The fact that the betas connecting the sequential time points are roughly 1.0 I suspect they are (but it might in fact be crap replicating crap). OTOH, you have a dataset which has been created based on all complete data over several times. You in all likelihood have a VERY restricted self selected group of robotic compliant respondents.
BTW: nabble is where you apparently posted this ;-)

SabatoPsy wrote

I am not exactly sure what nabble is, however the attachments can be found
on the website thread:
http://spssx-discussion.1045642.n5.nabble.com/SPSS-factor-scores-td5723392.html.
I did what you suggested and created a measurement model. Here are my two
correlation matrices:

SPSS: WB1 WB2 WB3 WB4 WB5 WB1 - WB2 0.81 - WB3 0.75 0.8 - WB4 0.76
0.76 0.77 - WB5 0.76 0.76 0.76 0.8 -
AMOS: WB1 WB2 WB3 WB4 WB5 WB1 - WB2 0.86 - WB3 0.82 0.87 - WB4 0.82
0.82 0.84 - WB5 0.82 0.82 0.82 0.87 -
As you can see the comparisons within the matrix are similar, but the
actual magnitudes are different such that the SPSS correlations are
slightly weaker. That difference is what is confusing me. Thank you for
suggesting doing a measurement model though. The correlations between
well-being in AMOS changed from .99 to in the .8's.

On Wed, Dec 4, 2013 at 3:49 PM, Maguin, Eugene [via SPSSX Discussion] <
[hidden email]> wrote:

> The list does not accept attachments. Did the attachments show up in
> nabble?
>
>
>
> Perhaps you’ve done this already but if not, I’d take the model apart and
> check out the well-being (WB) measurement model. Just allow the factors to
> be correlated. That will show you the factor covariances/correlations. You
> are fitting an autoregressive structure to that so the thing is that the
> autoregressive coefficients have to reproduce the factor cov/corr matrix
> and they may not. (Same issue applies to the gratitude line.) Gene Maguin
>
>
>
> By the way, does the amos corrs match the spss corrs?
>
>
>
> *From:* SPSSX(r) Discussion [mailto:[hidden email]<http://user/SendEmail.jtp?type=node&node=5723437&i=0>]
> *On Behalf Of *SabatoPsy
> *Sent:* Tuesday, December 03, 2013 9:04 PM
>
> *To:* [hidden email]<http://user/SendEmail.jtp?type=node&node=5723437&i=1>
> *Subject:* Re: SPSS factor scores
>
>
>
> Good question. No there is not. This is a data set I created from only the
> complete cases of my study. Because someone mentioned model fit, I am
> beginning to wonder if that is what is going on. I know I have poor model
> it. I read online that if you have poor model fit, your model is most
> likely misspecified, which can lead to biased parameter estimates (i.e.
> correlations). After all, SEM is solving regression equations
> *simultaneously.* Does this jive with what you know about SEM and AMOS?
> Again I am sorry that this is leading away from the content of this
> listserv.
>
>
>
> On Tue, Dec 3, 2013 at 8:03 PM, Art Kendall [via SPSSX Discussion] <[hidden
> email] <http://user/SendEmail.jtp?type=node&node=5723411&i=0>> wrote:

>
> Is there system missing data? (sysmis)
>
>
>
> Art Kendall
>
> Social Research Consultants
>
> On 12/3/2013 5:44 PM, SabatoPsy [via SPSSX Discussion] wrote:
>
> I just checked in my SPSS dataset. There is no user-missing data. The
> indicators correlation between .40 and .75 of one another. Good thinking. I
> am going to look into trying to print out the correlation matrix from AMOS.
> That should be helpful in determining what might be going on.
>
>
>
> On Tue, Dec 3, 2013 at 4:52 PM, Maguin, Eugene [via SPSSX Discussion] <[hidden
> email] <http://user/SendEmail.jtp?type=node&node=5723407&i=0>> wrote:

>
> Ok, let’s get the ‘slap your forehead’ question out of the way first. Is
> missing data correctly accounted for, i.e., is there any possibility that
> user missing values are being treated as nonmissing? Is there any
> possibility that one indicator is a near duplicate of another or a
> composite of several indicators?
>
>
>
> Let’s assume not. Next. I’m not familiar with Amos since I use mplus but
> if amos can print out the covariance or correlation matrix for the
> indicators, does that matrix match the matrix that spss computes? Amos is
> FIML and spss is not and the numbers should be very similar unless you have
> large amounts of missing data that are not missing completely at random and
> is ‘strongly’ correlated with model covariates. Lastly, if you have a
> correlation of .99 between factors, the correlations between the two
> factors’ indicators have to be extremely high, like in the upper 90’s, I’d
> guess. If this is true, then I’d guess you have variable construction or
> conceptualization problems.
>
>
>
> Gene Maguin
>
>
>
>
>
> *From:* SPSSX(r) Discussion [mailto:[hidden email]<http://user/SendEmail.jtp?type=node&node=5723406&i=0>]
> *On Behalf Of *SabatoPsy
> *Sent:* Tuesday, December 03, 2013 4:30 PM
>
>
> *To:* [hidden email]<http://user/SendEmail.jtp?type=node&node=5723406&i=1>
> *Subject:* Re: SPSS factor scores
>
>
>
> Good questions. The correlations are strange because they are so high. The
> literature would suggest they be highly correlated (around .70), however, I
> am getting correlations of .99. Everything you said is true except for good
> model fit. These are correlations from raw data. I have an SPSS file with
> no missing data. One of the factor loadings is set to 1.0 for each factor.
> These are *standardized* correlation coefficients. The model fit is poor
> though. I have a CFI of .77 and RMSEA of .16. Could having poor model fit
> change/bias my correlation estimates?
>
>
>
> On Tue, Dec 3, 2013 at 4:18 PM, Maguin, Eugene [via SPSSX Discussion] <[hidden
> email] <http://user/SendEmail.jtp?type=node&node=5723405&i=0>> wrote:

>
> Let’s back up to where the problem begins, which seems to be at the SEM
> stage because you are reporting “strange correlations” between factors. So,
> what’s strange about the correlations? And, just so that we all are on the
> same page, please clarify your use of ‘correlations’ in the context of an
> SEM. Affirmatively verify that the following sequence is true. Analyzing
> raw data not a covariance or correlation matrix. One loading fixed at 1.00
> for each factor rather than factor variances fixed at 1.00. Fit of the
> model is acceptable under current standards. The strange values are
> standardized values and not unstandardized values.
>
> Gene Maguin
>
>
>
>
>
>
>
>
>
>
>
>
>
>
>
> *From:* SPSSX(r) Discussion [mailto:[hidden email]<http://user/SendEmail.jtp?type=node&node=5723403&i=0>]
> *On Behalf Of *SabatoPsy
> *Sent:* Tuesday, December 03, 2013 3:56 PM
> *To:* [hidden email]<http://user/SendEmail.jtp?type=node&node=5723403&i=1>
>
>
> *Subject:* Re: SPSS factor scores
>

>
>
> Thank you for all of your posts. I looked up online how the factor scores
> are computed in SPSS in the IBM labyrinth. I got a bunch of matrix algebra
> and calculus notation that I am not smart enough to follow. I did however
> do an experiment on my own as was also suggested. I found that there are
> small differences between factor scores saved via the PCA method of
> extraction compared to the PAF and ML extraction methods; however, there
> were negligible differences between the PAF and ML methods. I tested this
> by correlating different saved factor score variables from the exact same
> factor structure but with different extraction methods.
>
>
>
> To address the question of why I am creating the factor(s) may go outside
> the scope of this listserve, but I will gladly take any advice/knowledge. I
> am running some SEM models in AMOS and am getting strange correlations
> between my factors. I wanted a way to test whether indeed these
> correlations between factors are accurate. I thought that by correlating
> the saved factor scores in SPSS using the ML extraction method, I should
> get the same correlations I got in AMOS. I am creating multiple single
> factors from a multiple set of 4 manifest indicators to create 5 total
> factors. To my knowledge, an EFA using ML and a CFA create essentially
> (minus negligible estimation differences) the same factor scores (when you
> are creating a single factor using all indicators). Therefore correlations
> between the SPSS factor scores I created and correlations between the
> latent factors I created in AMOS should be essentially equivalent. However,
> I am getting substantially different correlations. Does anybody know what
> is going on... or what I am doing wrong... or why my belief that the
> correlations should be the same is wrong??? I would greatly appreciate any
> enlightenment.
>
>
>
> David Disabato
>
> George Mason University
>
>
>
> On Tue, Dec 3, 2013 at 12:26 PM, Art Kendall [via SPSSX Discussion] <[hidden
> email] <http://user/SendEmail.jtp?type=node&node=5723401&i=0>> wrote:

>
> What is the purpose of your using FACTOR? Data reduction? Creating scales?
> etc.
>
> What are you going to use the factor scores for?
>
>
> Art Kendall
>
> Social Research Consultants
>
> On 12/3/2013 11:42 AM, SabatoPsy [via SPSSX Discussion] wrote:
>
> Hi Listserve,
>
> I am wondering how the SPSS factor scores in the "Factor" command are
> calculated. More specifically, does the method of extraction change the
> calculated factor scores? I want to compare the factor scores created by a
> PCA compared to an EFA (with ML estimation). Is this possible in SPSS? I
> know there are three ways of calculating the factor scores (Regression,
> Bartlett, Anderson-Rubin). Let us assume I tell SPSS to use the same way:
> Regression. If I tell SPSS to create "Regression" factor scores with a
> principle component method of extraction, will they be different than the
> "Regression" factor scores with a maximum likelihood method of extraction?
>
> Thank you,
>
> David Disabato
> George Mason University
> ------------------------------
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> --
> David J. Disabato
>
> Clinical Psychology Doctoral Student
>
> George Mason University
>
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>
> Clinical Psychology Doctoral Student
>
> George Mason University
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> [hidden email] <http://user/SendEmail.jtp?type=node&node=5723405&i=1>
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> --
> David J. Disabato
>
> Clinical Psychology Doctoral Student
>
> George Mason University
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> Art Kendall
> Social Research Consultants
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>
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> --
> David J. Disabato
>
> Clinical Psychology Doctoral Student
>
> George Mason University
>
> [hidden email] <http://user/SendEmail.jtp?type=node&node=5723411&i=1>
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Clinical Psychology Doctoral Student
George Mason University
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