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Measurement bias

As explained in Effective Data Analysis

Measurement bias is systematic error introduced when the measurement process produces responses that differ from the true values or construct of interest.

Measurement bias is systematic error introduced when a measurement process produces responses that differ from the true value or construct of interest. In self-report research, small changes to question wording, response scales, or scale descriptors can substantially change response patterns. section 6.2.1

Watch for measurement bias when:

  • A survey measures perceptions rather than the phenomenon itself, creating indirect measurement. section 6.2.1
  • Researchers compare responses collected with different wording, scales, or descriptors. section 6.2.1

Self-report versus behavioral data

An employee satisfaction survey and user feedback survey capture self-reported perceptions, while interaction logs, version-control history, and helpdesk tickets capture behavioral data. Comparing these sources can clarify what people report versus what they do.

How Effective Data Analysis covers it

How deep it goes
Defines survey-response distortion and identifies wording, scales, descriptors, and indirect measurement, but does not develop correction methods.
Examples
Uses survey wording and scale variations, NPS, satisfaction surveys, interaction logs, version-control history, and helpdesk tickets.
What it assumes
Assumes familiarity with surveys, self-report data, behavioral data, and basic organizational analytics.
What you can do after
Evaluate whether reported responses represent the intended construct and compare them with behavioral evidence.
How it connects
Connects measurement bias with self-report measures, behavioral measures, self-selection bias, indirect measurement, and Net Promoter Score.

Also explained in

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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Effective Data Analysis. The section links point to the parts of the book it draws on.