Pairwise deletion and number of valid... PreviousNext
Mplus Discussion > Missing Data Modeling >
 cindy chan posted on Thursday, April 17, 2008 - 10:32 pm
1/ i am doing path analysis model. i have some missing values in my dataset and want to use pairwise deletion so that i can have more cases in the analysis. what should i do for that?

2/ after running the analysis with pairwise deletion, is there a way to know (output) the number of valid cases used in the analysis between any two variables (e.g., x1 and y).
 Linda K. Muthen posted on Friday, April 18, 2008 - 8:49 am
I assume that your outcomes are categorical. With categorical outcomes, missing data. and a model without covariates, pairwise present data are analyzed. You can deduce the number of observations used by looking at the covariance coverage output and the missing data patterns that are printed with the PATTERNS option of the OUTPUT command.
 cindy chan posted on Friday, April 18, 2008 - 10:39 am
thank you.
 Eric Teman posted on Sunday, June 19, 2011 - 3:34 pm
How do you specify pairwise deletion in Mplus?
 Linda K. Muthen posted on Sunday, June 19, 2011 - 7:55 pm
There is no command for this.
 Patchara Popaitoon posted on Monday, September 19, 2011 - 4:20 am
Dear Linda,

I am not sure what this means 'The default is to estimate the model under missing data theory using all available data. To turn on listwise deletion, specify..' (Mplus manual, p.459-60). Does this suggest that all available data are included in the model and path analysis? If I have, say 80 missing values for one variable and 130 for another, how does the system deal with this inconsistent number of missing data for each variable? Does the system use pairwise deletion as a defualt to analyze the path coefficients?
 Linda K. Muthen posted on Monday, September 19, 2011 - 12:35 pm
See the Topic 4 course handout and video where missing data where estimation iwth missing data is discussed.
 Stafanie Chris posted on Monday, March 17, 2014 - 7:12 am
Dear Muthen,

I am doing a correlation analysis in mplus. According to my data, I decided to use TYPE=COMPLEX and Pairwise deletion, but I didn't find any option to specify pairwise deletion, does this mean it is not provided in mplus?


 Linda K. Muthen posted on Monday, March 17, 2014 - 9:44 am
There is an option for listwise deletion. Add LISTWISE = ON to the DATA command. There is not option for pairwise present.
 seungjin lee posted on Wednesday, April 13, 2016 - 9:29 pm
You explained that there is NO pairwise command in mplus for a path analysis above. Does it mean that I should remove the missing values manually before conducting the model with Mplus?

How about listwise then?

 Linda K. Muthen posted on Thursday, April 14, 2016 - 11:49 am
If you want pairwise deletion, you will need to do it yourself. We do not have that option.
 John C posted on Wednesday, May 09, 2018 - 11:39 am

I have a few questions on the default missing data mechanism for categorical outcomes using weighted least squares estimation.

In the user guide, it says "For censored and categorical outcomes using weighted least squares estimation, missingness is allowed to be a function of the observed covariates but not the observed outcomes. When there are no covariates in the model, this is analogous to pairwise present analysis."


1 - when there are no covariates, in what sense is this only "analogous," but not equivalent to, pairwise missing?

2 - when there are covariates, how is this no longer analogous to pairwise present?

3 - is there a paper I can cite for more information on this mechanism?
 Bengt O. Muthen posted on Thursday, May 10, 2018 - 2:51 pm
1. The idea is that regular pairwise implies that you estimate means/thresholds, variances, and correlation for those subjects. In our weighted least squares, thresholds are estimated for everyone having observations on that variable, leaving the correlation estimation for the subjects with complete data on both variables.

2. If the covariates predict missing on the outcomes, we have (an approximation to) ML under MAR.

3. No paper on this specifically as far as I can recall.
 John C posted on Friday, May 11, 2018 - 12:26 pm
Ok thanks, and would it be true to say that the only other option with a weighted least squares estimation would be listwise deletion?
 Bengt O. Muthen posted on Friday, May 11, 2018 - 1:37 pm
That, or combine it with a first-step multiple imputation of missing data.
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