http://spssx-discussion.165.s1.nabble.com/Missing-values-in-MIXED-tp5718714p5718775.html
' ... If there are multiple groups, then one might consider fitting group-specific unstructured matrices.'
How would you do that? Just to keep the discussion context clear, we have been talking about a mixed analysis that includes a repeated statement and not one that includes only a random statement. True?
That certainly makes sense to determine if there is a discernible pattern in the residual covariance matrix. In fact, I almost always begin with an unstructured matrix, and based on the pattern, decide which restrictive structures to test against the unstructured matrix. If there are multiple groups, then one might consider fitting group-specific unstructured matrices.
> Off the top of my head, I can't say where I read it (and I don't have
> my books with me today), but I do think that at least one author I've
> read recommends always /starting/ with an unstructured residual
> covariance matrix, and imposing restrictions if/when it makes sense to
> do so. I wonder if this is the approach Diana was actually promoting.
>
> Cheers,
> Bruce
>
>
> Ryan Black wrote
>> Since Bruce pointed out a typo I made, I decided to reread my entire
>> response to the OP. I noticed another typo. In this post, I correct
>> both typos and I have decided to add another comment. All changes are
>> ***CAPITALIZED*** in the text BELOW my name. But, I also have another
>> comment to make right here:
>>
>> The reason I'm taking such an interest in this thread is that I have
>> heard this general recommendation before; that is, to always use an
>> unstructured residual-covariance matrix. I don't know if there is a
>> textbook out there that makes such a silly (at best) or dangerous (at
>> worst) recommendation, but my guess is because of the assumption that
>> the unstructured matrix can never be wrong due to the lack of
>> restrictions. Let me make a somewhat provocative statement...An
>> unstructured residual variance-covariance structure applied to ALL
>> subjects is not always the LEAST restrictive residual
>> variance-covariance structure. I realize that in the past I have even
>> said that an unstructured matrix is the least restrictive, but I
>> should have couched that statement in the context of single group designs only.
>>
>> Ryan
>> On Sun, Mar 17, 2013 at 8:40 AM, <
>
>> ryan.andrew.black@
>
>> > wrote:
>>
>>> Diana,
>>>
>>> See my comments below.
>>>
>>> On Mar 17, 2013, at 4:43 AM, "Kornbrot, Diana" <
>
>> d.e.kornbrot@.ac
>
>> >
>>> wrote:
>>>
>>> Ryan
>>>
>>> Thanks
>>> Done all that. Converting horizontal to vertical is straightforward
>>> using the data structuring wizard [don’t need syntax], once one gets
>>> the hang of it
>>>
>>> My ACTUAL question was:
>>> MIXED with data in long form can cope with missing data, with
>>> correction for denominator df GLM REPEATED insists on NO missing
>>> data So what is the difference?
>>>
>>> With the help of Bruce Weaver, I have NOW worked out that the
>>> difference lies in the covariance matrix used for estimation of
>>> parameters REPEATED applies list wise deletion and so discards any
>>> subjects that do not have values for all variables, MIXED applies
>>> pair wise deletion.
>>>
>>>
>>> That is exactly what I showed in the illustration.
>>>
>>> Suspect the reduced df is harmonic mean of df for relevant groups,
>>> but do not know
>>>
>>>
>>> No need to suspect. I provided a link to the formula for df error. I
>>> don't know what you mean by reduced.
>>>
>>>
>>> Bruce provides following useful refs that suggest that using MIXED
>>> may actually be less biased than any of a whole slew of complicated
>>> imputation
>>> procedures:
>>> Twisk & de Vente (2002):
>>> *
http://europepmc.org/abstract/MED/11927199>>> *Twisk (2003):
>>> *
>>>
http://books.google.ca/books?hl=en&lr=&id=TCg02e-tI_cC&oi=fnd&pg=PR1>>> 5&dq=Twisk+2003&ots=2GfodRIiu9&sig=z8BSBQoRaZNavIzj_QOeATBP_nw#v=one
>>> page&q=Twisk%202003&f=false *Singer & Willett (/Applied Longitudinal
>>> Data Analysis/, Chapter 5).
>>>
>>> I NOW recommend MIXED with UNSTRUCTURED covariance matrix across the
>>> board.
>>>
>>>
>>> That is a poor recommendation. The goal should be to find the
>>> optimal residual variance-covariance structure. You could reduce
>>> statistical power if you employ an unstructured matrix if there is a
>>> ****MORE*** restrictive structure that fits that data equally well
>>> (e.g., AR1. TOEP). There may be other aspects to your data as well
>>> (G-side random effects that should be incorporated).
>>>
>>> No doubt it will take time to ‘filter down’ to all users Output much
>>> simpler as all inferential tests in 1 table Can do appropriate post
>>> hoc or planned comparisons with standard errors correctly estimated
>>> from unstructured covariance matrix.
>>>
>>>
>>> That is not only true for the unstructured matrix.
>>>
>>>
>>> MIXED has limitation of not supplying effect sizes.
>>> Jason Becksted points out that on can calculate partial eta squared
>>> = F*df1/(F*df1+df2), where df1 is the hypothesis df and df2 is the
>>> error df.
>>>
>>>
>>> So did I, publicly, when you asked. And I pointed out that one would
>>> have to employ ML to use the ***ALTERNATIVE*** formula to obtain
>>> partial eta squared from a fully balanced fixed effects only design.
>>> But, I would question the validity of using that formula ***ABOVE***
>>> under all circumstances, which is why I provided the alternative.
>>> For example, what if you are trying to determine the effect size of
>>> a random effect? What if your fixed effect predictor is at a higher
>>> level? There have been plenty of discussions on this matter on the
>>> multilevel listserve and in multilevel textbooks. I would not simply
>>> apply that formula to all circumstances. In fact, I would generally
>>> recommend using the second approach I showed. ***SPEAKING OF EFFECT
>>> SIZE, WE MUST ALSO BE CAREFUL TO DEFINE WHAT WE MEAN BY EFFECT
>>> SIZE***
>>>
>>>
>>> REPEATED, no doubt ground breaking in its time [distant past], is
>>> fiddly & potentially misleading. Although the multivariate option
>>> uses correct unstructured covariance matrix, the post hocs use SEs
>>> based on inappropriate diagnonal covraince matrix, with GG
>>> corrections.
>>> Personally,
>>> have never seen a covariance matrix with all pair wise covariances
>>> equal – seems improbable in the real world.
>>>
>>>
>>> Again, there are alternatives to both extremes. It is not one versus
>>> the other.
>>>
>>>
>>> Best
>>>
>>> Diana
>>>
>>> On 16/03/2013 16:49, "R B" <
>
>> ryan.andrew.black@
>
>> > wrote:
>>>
>>> Diana,
>>>
>>> In order to employ a linear mixed model in SPSS, one must construct
>>> the dataset in vertical format, such that there are "k" cases per
>>> subject with an identification variable with non-repeating numbers
>>> for cases associated with a particular subject. Assuming the
>>> within-subjects variable is either nominal, ordinal, or is composed
>>> of equally-spaced intervals, it is common practice for the
>>> within-subjects variable to be a numeric integer variable with
>>> sequential values from 1 through "k" levels of the within-subjects
>>> variable. Finally, the response variable must be concatenated
>>> vertically with each measurement linked to the appropriate ID and
>>> level of the within-subject variable.
>>>
>>> Here is an illustration:
>>>
>>> ID Time y
>>> 1 1 34
>>> 1 2 22
>>> 1 3 12
>>> 1 4 11
>>> 2 1 33
>>> 2 2 32
>>> 2 3 .
>>> 2 4 22
>>> 3 1 38
>>> 3 2 37
>>> 3 3 34
>>> 3 4 30
>>> .
>>> .
>>> .
>>> .
>>>
>>> As you can see above, the second subject was not measured at time 3.
>>> As a result, that case will be excluded from the linear mixed model analysis.
>>> However, data obtained from other times points for that particular
>>> subject will be included in the analysis. The assumption we must
>>> make in order to obtain unbiased estimates derived from a linear
>>> mixed model is that the data are missing randomly. With that said,
>>> the MIXED procedure in SPSS calculates degrees of freedom using
>>> Satterthwaite's Approximation:
>>>
>>>
>>>
http://publib.boulder.ibm.com/infocenter/spssstat/v20r0m0/index.jsp?
>>> topic=%2Fcom.ibm.spss.statistics.help%2Falg_mixed_custom-tests_satte
>>> rthwaite.htm
>>>
>>> This approximation has been shown to be valid for balanced and
>>> unbalanced designs.
>>>
>>> In addition to the benefits of not having to exclude all data from
>>> subjects who happen to have data which are missing randomly for
>>> parameter estimation, the MIXED procedure allows for modeling of
>>> continuous response variables using various hierarchical designs and
>>> residual covariance structures.
>>>
>>> Ryan
>>> On Fri, Mar 15, 2013 at 11:46 AM, Kornbrot, Diana <
>
>> d.e.kornbrot@.ac
>
>>> wrote:
>>>
>>> If one uses repeated in procedure GLM then it appears that all
>>> subjects must have vlaues for all combinations of the rpeated
>>> measures BUT using MIXED, there is then a non-integer error df How
>>> is SPSS actually handling the missing values?
>>> Nb Am using unstructured covariance matrix
>>>
>>> Thanks for help
>>> Best
>>> Diana
>>> ------------------------------
>>> Emeritus Professor Diana Kornbrot
>>> email:
>
>> d.e.kornbrot@.ac
>
>> <http://
>
>> d.e.kornbrot@.ac
>
>> >
>>> web:
http://dianakornbrot.wordpress.com/>>> *Work
>>> *Department of Psychology
>>> School of Life and Medical Sciences
>>> University of Hertfordshire
>>> College Lane, Hatfield, Hertfordshire AL10 9AB, UK
>>> voice: +44 (0) 170 728 4626
>>> <tel:%2B44%20%280%29%20170%20728%204626>
>>> *Home
>>> *19 Elmhurst Avenue
>>> London N2 0LT, UK
>>> voice: +44 (0) 208 444
>>> 2081<tel:%2B44%20%280%29%20208%20%C2%A0444%202081>
>>> mobile: +44 (0) 740 318 1612
>>> <tel:%2B44%20%280%29%20740%20318%201612>
>>>
>>>
>>>
>>>
>>>
>>> ------------------------------
>>> Emeritus Professor Diana Kornbrot
>>> email:
>
>> d.e.kornbrot@.ac
>
>>> web:
http://dianakornbrot.wordpress.com/>>> *Work
>>> *Department of Psychology
>>> School of Life and Medical Sciences
>>> University of Hertfordshire
>>> College Lane, Hatfield, Hertfordshire AL10 9AB, UK
>>> voice: +44 (0) 170 728 4626
>>> *Home
>>> *19 Elmhurst Avenue
>>> London N2 0LT, UK
>>> voice: +44 (0) 208 444 2081
>>> mobile: +44 (0) 740 318 1612
>
>
>
>
>
> -----
> --
> Bruce Weaver
>
[hidden email]
>
http://sites.google.com/a/lakeheadu.ca/bweaver/>
> "When all else fails, RTFM."
>
> NOTE: My Hotmail account is not monitored regularly.
> To send me an e-mail, please use the address shown above.
>
> --
> View this message in context:
>
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