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Re: st: Repeated Measures ANOVA and Missing Values

From   Cameron McIntosh <>
Subject   Re: st: Repeated Measures ANOVA and Missing Values
Date   Mon, 17 Oct 2011 00:58:11 -0400

Do you really believe that the missing values are MCAR in this case (as you must assume with listwise deletion/ignoring cases)? I think you run the risk of biased estimates if you go that route. Why not use multiple imputation instead? 

Marchenko, Y. (2010). Multiple-imputation analysis using Stata’s mi command. Stata Conference, Boston 2010.

Marchenko, Y. (2011). Chained equations and more in multiple imputation in Stata 12. 2011 UK Stata Users Group Meeting.


Royston, P. (2009). Multiple imputation of missing values: Further update of ice, with an emphasis on categorical variables. The Stata Journal 9(3), 466–477.

van Buuren, S. (2010). Item Imputation Without Specifying Scale Structure. Methodology, 6(1), 31–36.

van Buuren, S. & Groothuis-Oudshoorn, K. (March 26, 2011). Multivariate Imputation by Chained Equations: Package ‘mice’, Version 2.8.

Van Buuren, S., & Groothuis-Oudshoorn, K. (2011). MICE: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, forthcoming.

Van Buuren, S., Brand, J.P.L., Groothuis-Oudshoorn C.G.M., & Rubin, D.B. (2006). Fully conditional specification in multivariate imputation. Journal of Statistical Computation and Simulation, 76(12), 1049–1064.

Van Buuren, S. (2007). Multiple imputation of discrete and continuous data by fully conditional specification. Statistical Methods in Medical Research, 16, 3, 219–242.

Lee, K.J., & Carlin, J.B. (2010). Multiple Imputation for Missing Data: Fully Conditional Specification Versus Multivariate Normal Imputation. American Journal of Epidemiology, 171(5), 624-632. 


> 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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