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FMISS: Stata module to identify variables with problematic missing values


  • Florian Chavez Juarez

    (University of Geneva)

Programming Language



fmiss allows you to identify not only the total number missing values in each variable, but also how many of them are unique in the sense that for all other variables of the observation the information is available. This distinction is important to see which variable is causing a large drop int he sample size on its own. The module identifies missing value in numerical and string variable. For the case of numerical variables, also Stata-coded missing values (e.g. “.a”) are identified. Since a main issue of missing values is that it might introduce a sample selection problem, fmiss offers a very simple and purely introductive way to detect such problems. Using the option detail, a mean-comparison test between the original sample and the sample one would get by including the variable (this means dropping the unique missing values) is computed and variable where the difference is significant are reported.

Suggested Citation

  • Florian Chavez Juarez, 2012. "FMISS: Stata module to identify variables with problematic missing values," Statistical Software Components S457560, Boston College Department of Economics.
  • Handle: RePEc:boc:bocode:s457560
    Note: This module should be installed from within Stata by typing "ssc install fmiss". The module is made available under terms of the GPL v3 ( Windows users should not attempt to download these files with a web browser.

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    missing data; patterns; Stata;
    All these keywords.


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