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Questionnaire Wording Bias

Also Known As: Survey wording effect Question framing bias Acquiescence bias
Aspect ID: questionnaire_wording_bias

Definition

Questionnaire wording bias encompasses the systematic distortion of survey responses caused by how questions are phrased, ordered, or formatted. Even logically equivalent questions yield substantially different response distributions when worded differently — for example, 'allow' versus 'forbid' framing, leading versus neutral questions, or positive versus negative scale anchors.

Examples

Asking 'Do you favor allowing abortion in some circumstances?' yields around 77% agreement, while 'Do you favor forbidding abortion in all circumstances?' yields only about 20% agreement — yet both questions logically address the same policy position.

A customer satisfaction survey asks: 'How satisfied were you with our outstanding support team?' The embedded positive descriptor ('outstanding') inflates satisfaction scores compared to a neutral phrasing like 'How satisfied were you with our support team?' — making the service appear more highly rated than it actually is.

An employee engagement survey asks first about specific frustrations with management, then immediately asks 'Overall, how satisfied are you with your job?' The negative priming from the earlier questions causes overall satisfaction scores to drop significantly compared to a version where the overall satisfaction question is asked first, even though nothing about the employees' actual experience has changed.

Verification Steps
Verification Steps
Binary yes/no questions that an AI must answer to detect a reasoning pattern in a text.
Each of the 452 aspects has verification steps — simple yes/no questions designed to systematically detect whether a pattern appears in a text. For ad hominem: "Does the argument attack a person rather than their claim?" For false dichotomy: "Are only two options presented when more exist?" This ensures consistent, reproducible analysis.

Binary (yes/no) questions an LLM must answer to identify this aspect:

  1. 1

    Does the question framing use loaded or emotionally valenced language?

    Type: binary
  2. 2

    Would reversing the question wording or scale anchors change the reported results?

    Type: binary
  3. 3

    Is the question ordering likely to prime certain responses through context effects?

    Type: binary
  4. 4

    Were the questionnaire items pre-tested for equivalence of phrasing?

    Type: binary
Deep Dive
The expandable detail section on each aspect page with examples, psychology, and counter-strategies.
The Deep Dive section provides in-depth information about each aspect: a real-world example showing the pattern in action, an explanation of why it works psychologically, practical advice on how to counter it, alternative names, and links to related aspects.