Handle missing values cleanly

Define freely which codes count as missing values and exclude them per category — so your bases stay honest, without touching the raw dataset.

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Table with a cleaned base in DataLion

DataLion handles missing values right on the dataset: you define freely which codes count as missing values and exclude empty or NULL values deliberately — per category rather than global. Bases stay correct, without touching the raw dataset.

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Trusted by research, insights & media teams

  • YouGov
  • SevenOne Media
  • Mediengruppe RTL Deutschland
  • Nielsen Sports
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  • 2 weeks release cadence
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Define missing values freely

What counts as a missing value? You decide. In DataLion you define freely which codes are treated as missing values — say “don’t know”, “no answer” or project-specific special codes.

So the definition fits your questionnaire exactly, instead of following a rigid default.

  • Define codes freely as missing values
  • Map special codes like “don’t know” cleanly
  • Definition fits the questionnaire, not a fixed default
Defining missing values in DataLion

Exclude empty and NULL values

DataLion excludes empty and NULL values deliberately so they don’t skew your analysis. You control what feeds the base — and what doesn’t.

That keeps percentages and means clean, without deleting or copying records around.

  • Exclude empty and NULL values deliberately
  • Percentages and means stay unbiased
  • No deleting or copying of records

Exclusions per category

The key difference: exclusions apply per category rather than global. You remove a missing value exactly where it gets in the way — without dropping it from every other analysis.

That keeps bases honest and every question keeps its correct population — right on the dataset, with no detour through SPSS or Excel.

  • Exclusions per category rather than global
  • Every question keeps its correct base
  • All on the dataset — no export
Distribution cleaned per category in DataLion

See DataLion with your own data

Start a free trial or book a personal demo — from raw data to a finished dashboard.

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We now work much more efficiently, giving us more time to take care of the derivations and insights from the data for the customers.
Jens Falkenau, Vice President of Market Research · Nielsen Sports
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Common questions about missing values

How does DataLion handle missing values?
You define freely which codes count as missing values and exclude empty or NULL values deliberately — per category rather than global. Bases stay correct, without changing the raw dataset.
Can I set what counts as missing myself?
Yes. You define freely which codes are treated as missing values — say “don’t know” or project-specific special codes — to fit your questionnaire instead of a fixed default.
Do exclusions apply to the whole dataset?
No. Exclusions apply per category rather than global. You remove a missing value exactly where it gets in the way, without dropping it from every other analysis — so every question keeps its correct base.
Do I have to change the raw dataset?
No. Missing-value handling happens right on the dataset in DataLion, without deleting records, copying them around or exporting to SPSS.

Keep your bases honest

Try DataLion free: define missing values, exclude empty and NULL values per category — with no edits to the raw data. Or book a demo.

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