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Measure · Tool guide 05 of 40

Data Type

Data type describes the nature of a recorded variable and determines which summaries and methods make sense. Categories can be nominal or ordered; numerical variables can be discrete counts or continuous measurements. A numeric code does not make a category quantitative.

Expected output: A data dictionary that identifies each variable’s meaning and type.

01 · Purpose

Why and when to use it

Classify variables before choosing charts, calculations or measurement-system studies.

Who takes part?

The measurement owner works with people who understand the process and the recorded data. Statistical or measurement-system decisions should involve someone able to check the assumptions and study design.

02 · Application

How to apply it

Start with an agreed measurement purpose, explicit units and trustworthy observations. Identify missing cases and assumptions before calculating.

  1. Ask what each recorded value means.
  2. Distinguish categories from numerical quantities.
  3. For categories, check whether there is a meaningful order.
  4. For quantities, distinguish counts from continuous measurements.
  5. Record units, allowed values and suitable summaries in a data dictionary.

03 · Example

A worked measurement example

Original fictional teaching example. Small datasets illustrate the method; they are not proposed as sufficient study sizes. Supplied parameters are assumptions, not estimates from a real process.

VariableTypeSuitable first summary
Shift A/BNominal categoryCounts or percentages by shift
Service rating 1–5Ordinal categoryCategory distribution; ordering retained
Errors per orderDiscrete countCount distribution and defects per unit
Packing time in minutesContinuous measurementDot plot, median and spread

04 · Interpretation

What the result tells you

The mean of shift codes 1 and 2 is meaningless. Whether recorded time is rounded to whole minutes does not change that duration is a continuous characteristic.

Completion check: A data dictionary that identifies each variable’s meaning and type.

05 · Judgement

Mistakes and limitations

Avoid treating ordinal ratings as equal-interval measurements without justification. Check whether zero is meaningful before interpreting ratios.

07 · Reading

Sources and further reading

The tool list follows the accepted LeanTrainingLab overview and its reference to Six Sigma+Lean Toolset, second edition (Lunau, editor; Springer, 2013). The book chapters were not available for direct review during this build. Explanations, examples and worksheets are original teaching material. Further reading supplies professional background; local conventions are stated explicitly.