Missing data

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Most XLSTAT functions include options to handle missing data. However, only few approaches are available. This tool allows you to complete or clean your dataset using advanced missing value treatment methods.

There are three types of missing values (Allison, 2001): data missing completely at random (MCAR), data missing at random (MAR) and data not missing at random (NMAR).

Data is missing completely at random (MCAR) if the event that leads to a missing data is independent of observable variables and of unobservable parameters. It should occur entirely at random. When data are MCAR, the analyses performed on the data are unbiased.

Data is missing at random (MAR) when the event that leads to a missing data is related to a particular variable, but it is not related to the value of the variable that has missing data. This is the most common case.

Data is not missing at random (NMAR) when data is missing for a particular reason. An example of this is the filtered questions in a questionnaire (the question is only intended for some respondents, the others are missing)

The methods available in this tool correspond to the MCAR and MAR cases.

Different methods are available depending on your needs and data:

  • For quantitative data, XLSTAT allows you to:
    • Remove observations with missing value.
    • Use a mean imputation method.
    • Use a nearest neighbor approach.
    • Use the NIPALS algorithm.
    • Use an MCMC multiple imputation algorithm.
  • For qualitative data, XLSTAT allows you to:
    • Remove the observations with missing value.
    • Use a mode imputation method.
    • Use a nearest neighbor approach


This analysis is available in the XLStat-Base addin for Microsoft Excel

About KCS

Kovach Computing Services (KCS) was founded in 1993 by Dr. Warren Kovach. The company specializes in the development and marketing of inexpensive and easy-to-use statistical software for scientists, as well as in data analysis consulting.

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