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RE: st: Repeated Measures ANOVA and Missing Values

From   amir gahremanpour <>
To   statalist <>
Subject   RE: st: Repeated Measures ANOVA and Missing Values
Date   Mon, 17 Oct 2011 14:27:08 -0500

Dear Phil,

what is considered to be small sample size for xtmixed? from what I understood from you xtmixed Chicago lecture with small sample size xtmixed may cause type I error, if I am correct please advise about a good sample size (total sample size) for xtmixed.

many thanks


> Date: Sun, 16 Oct 2011 16:27:37 -0700
> Subject: Re: st: Repeated Measures ANOVA and Missing Values
> From:
> To:
> I'm trying to run a oneway repeated measures ANOVA (the variable is =
> session, with three levels).  I have organized my data in long format, =
> with one column for subject ID, one column for session number and one =
> column for the dependent variable.  Here is a listing of part of my data =
> file:
> ...
> There are obviously several cases that are missing observations at =
> session 2 or session 3.  Does wsanova or Stata's anova command =
> automatically drop cases with missing values at any of the three =
> sessions?  My first pass with the analysis seems to suggest it does not.
> What is the best way to deal with these missing values (either deleting =
> cases with missing values or telling wsanova or anova to ignore them in =
> the analysis)?
> -------------------------------
> John,
> You are correct, -wsanova- does not drop cases that have missing
> observations.  If
> want complete case analysis you will have to drop observations manually.
> However, if you want to use all of the observations I would recommend
> -xtmixed-.  It
> would look something like this in Stata 12:
> xtmixed DV i.session || ID:, reml
> testparm i.session
> If you have lots of subjects you can use the chi-square value as is.
> If your sample is on
> the small size, you can rescale the chi-square to F by dividing it by
> its df (in your case 2).
> You can use the residual df from the -wsanova- as denominator df.  The
> -Ftail- function
> will get you the p-value.
> This approach is not universally approved depending upon what
> discipline you come from.
> My Chicago Stata Users Group presentation on this topic can be found at
> -- 
> Phil Ender
> UCLA Statistical Consulting Group
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