UPCOMING WEBINAR: The Roadmap for Market Researchers in the Age of AI This article describes how to use a built-in QScript to check the selected numeric data for outliers and creates new copies of the data with the outliers removed. Outliers are defined as values that are not within a certain number of standard deviations from the variable mean. You can choose how many standard deviations are used to determine which values are considered to be outliers. The default value is 3 standard deviations. The new copies of data will have the outlier values replaced with missing values. Data that does not contain outliers will not be copied.
Requirements
- A data file loaded in Displayr
- One or more numeric variables
Method
To run the script:
- Select a numeric, numeric - multi, or numeric - grid in the Data Sources tree.
- Click + > Data Quality > Remove Outliers.
- Enter a cut-off value to identify cases whose standard deviations are not within that value. The default value is 3.
A folder will be created in the Report tree that contains tables for the selected data and any new copies of data with the outliers removed.
The new copies of variables use a JavaScript formula to assign respondents with outlying values with a new value of NaN. The means and standard deviations are determined when this script is run. As a result, the definition of an outlier in variables where the outliers have been removed will not be updated if the underlying data is changed or updated.
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