Date: Thu, 2 Jun 2011 11:14:32 +1000
Reply-To: "Benjamin Spivak (Med)" <benjamin.spivak@monash.edu>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: "Benjamin Spivak (Med)" <benjamin.spivak@monash.edu>
Subject: Re: two factor hierachical model
In-Reply-To: <006401cc20be$f218d310$d64a7930$@net>
Content-Type: multipart/alternative;
Hi Matt,
Thanks for the response.
Yes, I have tried the unstructured covariance structure, unfortunately when
I attempt to do this SPSS gives me an error message and proceeds by
defaulting to scaled identity covariance structure. As for your second
point, I am not sure how to compute this within spss.
In regards to the last point, I am worried that this might be the case, as
mean differences from jury to jury are quite small. I'm not sure what to do,
any ideas?
Thanks,
Ben.
On 2 June 2011 10:49, Matthew Pirritano <matthewpirritano@sbcglobal.net>wrote:
> Ben,
>
>
>
> Two ideas. Have you tried the unstructured covariance structure. Or what
> about looking at the frequencies of scores on your dv and covariates by your
> categorical ivs. Maybe some of those cells have gotten too small?
>
>
>
> A quick google search also leads me to think the variance of your
> intercept across jury’s may not be varying.
>
>
>
>
> http://groups.google.com/group/comp.soft-sys.sas/browse_thread/thread/da82a20cc8aba8d1/aa1d0c37f4d8a0e5?hl=en&lnk=gst&q=hessian+matrix+positive+definite+stringplayer_2#aa1d0c37f4d8a0e5
>
>
>
> Although it’s hard for me to imagine what that means for your data. After
> adjusting for your categorical ivs and your covariates there is no
> difference in means across jurys?
>
>
>
> Matt
>
>
>
> *From:* SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] *On Behalf
> Of *Benjamin Spivak (Med)
> *Sent:* Wednesday, June 01, 2011 4:35 PM
> *To:* SPSSX-L@LISTSERV.UGA.EDU
> *Subject:* Fwd: two factor hierachical model
>
>
>
>
>
> ---------- Forwarded message ----------
> From: *Benjamin Spivak (Med)* <benjamin.spivak@monash.edu>
> Date: 2 June 2011 09:34
> Subject: Re: two factor hierachical model
> To: Rich Ulrich <rich-ulrich@live.com>
>
> Hello Rich and Ryan,
>
>
>
> I have provided the informaton below
>
>
>
> Ryan: I am performing a 2x3 design experiment looking at juries and their
> understanding of the law. I have 63 juries with roughly 10-12 jurors in each
> group. Both my IV's are categorical and are based on juror level data. I am
> also attempting to use age, education and gender as predictors in the model.
> The DV that I am using appears to be normally distributed and has satisfied
> the assumption of homogeneity of variance. My Syntax is as follows:
>
>
>
> MIXED Standards BY EduCon Jurycharge WITH Age Education Gender
> /CRITERIA=CIN(95) MXITER(100) MXSTEP(10) SCORING(1)
> SINGULAR(0.000000000001) HCONVERGE(0,
> ABSOLUTE) LCONVERGE(0, ABSOLUTE) PCONVERGE(0.000001, ABSOLUTE)
> /FIXED=EduCon Jurycharge Age Education Gender | SSTYPE(3)
> /METHOD=ML
> /PRINT=SOLUTION TESTCOV
> /RANDOM=INTERCEPT | SUBJECT(Jury) COVTYPE(VC)
> /EMMEANS=TABLES(EduCon) COMPARE ADJ(BONFERRONI)
> /EMMEANS=TABLES(Jurycharge) COMPARE ADJ(BONFERRONI).
>
> Rich: It is calling my subject grouping a covariate. As for missings, yes
> there was a proportion of missing data in my DV (no missing data anywhere
> else). However, I tried replacing missing values with group means and still
> encountered the same problem.
>
>
>
> Thanks to both of you,
>
>
>
> Ben.
>
>
>
>
> On 2 June 2011 03:56, Rich Ulrich <rich-ulrich@live.com> wrote:
>
>
> My first guess would be that you have mis-specified the model,
> with the consequence that the "covariance parameter is redundant".
> What is it calling a "covariate"?
>
> My second guess would be that the data, as it is being used by
> the problem, is not exactly what you expect. Missings? Did it
> seem to use all the cases?
>
> --
> Rich Ulrich
>
>
>
>
>
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