Missing data
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What does it mean?
Missing data occur when an expected observation value is absent. Nonresponse, inapplicable questions and recording failures are different causes.
Think through an example.
Fifteen of 100 students leave travel expenditure blank. Replacing blanks with zero assumes they had no expenditure.
Test your understanding.
A blank income field always means zero income.
Make the next connection
Explore related concepts to deepen your understanding.
Put this knowledge into practice.
Practice with short steps in related tools, then test yourself.
Merge duplicate references
Organize repeated records of the same publication.
Pretest branching
Catch incorrect routing in conditional questions.
Count categories with a PivotTable
Find how many records fall in each category.
Inspect blanks with a filter
Examine missing values before deleting anything.
Read a frequency table
Check category distributions and missing responses.
Count categories including missing values
See category counts and NA values together.
Take a first look at your CSV
Inspect columns and missing-value counts with Python and pandas.
Sources and further reading
Explore the foundations and methodological details in these resources. Examples on this page were created for illustration.
- UK Data Service Source in EnglishUnderstanding Society: Main Survey User Guide — Missing values