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] <
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>
> ------------------------------
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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] <
http://user/SendEmail.jtp?type=node&node=5723405&i=1>>
>
> ------------------------------
>
> 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] <
http://user/SendEmail.jtp?type=node&node=5723407&i=1>>
>
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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] <
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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]