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FMISS: Stata module to identify variables with problematic missing values

Author

Listed:
  • Florian Chavez Juarez

    () (University of Geneva)

Abstract

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". Windows users should not attempt to download these files with a web browser.
    as

    Download full text from publisher

    File URL: http://fmwww.bc.edu/repec/bocode/f/fmiss.ado
    File Function: program code
    Download Restriction: no

    File URL: http://fmwww.bc.edu/repec/bocode/f/fmiss.hlp
    File Function: help file
    Download Restriction: no

    More about this item

    Keywords

    missing data; patterns;

    Statistics

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