Re: Missing values in MIXED

Posted by Ryan on Mar 16, 2013; 4:49pm
URL: http://spssx-discussion.165.s1.nabble.com/Missing-values-in-MIXED-tp5718714p5718737.html

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_satterthwaite.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 <[hidden email]> 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

---

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