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    Ranking Question

    A ranking question asks respondents to order a set of items by preference, importance, or priority, producing ordinal data on relative preference.

    Ranking questions force trade-offs that rating questions allow respondents to avoid (where everything can be 'very important'). They are ideal for feature prioritization, value-prop testing, and persona work. The trade-off is cognitive load: ranking more than 5-7 items reliably is hard for respondents, with later ranks becoming noisy. For more than 7 items, use MaxDiff (best-worst scaling) instead. Drag-and-drop interfaces produce cleaner data than numbered dropdowns; mobile completion drops 8-12% on ranking questions versus simple ratings.

    Example

    Rank these five features by importance: AI question writing, branching logic, custom branding, integrations, advanced analytics. Top-ranked across n = 412: AI question writing (34%), integrations (28%), branching (18%), branding (12%), analytics (8%).

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    Related terms

    Matrix Question

    A matrix question (or grid question) displays multiple sub-questions sharing the same response scale in a table, letting respondents rate many items efficiently.

    Semantic Differential Scale

    A semantic differential scale presents two opposing adjectives at the ends of a 5- or 7-point scale and asks respondents to mark their position between them.

    Likert Scale

    A Likert scale is a survey rating scale with ordered response options (typically 5 or 7 points) used to measure attitudes, agreement, or frequency.

    Closed-Ended Question

    A closed-ended question presents respondents with a fixed set of answer choices to select from.

    Kano Model

    The Kano model categorizes product features by their impact on customer satisfaction: must-haves, performance, delighters, indifferent, reverse.

    Open-Ended Question

    An open-ended question allows respondents to answer in their own words with free-text input rather than choosing from pre-defined options.