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Run your next card sort

Understand how your users naturally group and label content.

Launch a card sort in minutes

Understand how your users naturally group and label content with an open, closed, or hybrid card sort in Great Question.

Try it free for 14 days

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At Great Question, AI isn't about hype or panic. It's about finding a happy medium for UX researchers who want to augment their work.

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AI Moderated Interviews is here

Extend the limits of qualitative research with the reach of a survey and the depth of an interview. Now in beta in Great Question.

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Launch a card sort in minutes

Understand how your users naturally group and label content with an open, closed, or hybrid card sort in Great Question.

Try it free for 14 days

Run your next tree test today

Evaluate and improve the information architecture of your product, website, or app with tree testing in Great Question.

Try it free for 14 days

Run your next tree test today

Evaluate and improve the information architecture of your product, website, or app with a tree test in Great Question.

Try it free for 14 days

Risk / confidence level	Participants needed for statistical significance
Low risk / exploratory	20
Medium risk / confident	24
High risk / very confident	30

Number of cards	Study duration (minutes)
10	6
20	12
30	18
40	24
50	30

Study duration (minutes)	Incentive for general consumers (B2C)	Incentive for professionals (B2B)
5	$5	$10
10	$10	$15
15	$15	$20
20	$20	$25
25	$25	$30
30	$30	$35

Study type	When to use	Goal
Open card sorting	Early exploration	Learn how users naturally organize info
Closed card sorting	Later validation	Test if users understand your proposed structure
Hybrid card sorting	Mid-stage refinement	Validate or improve existing categories

Method	When to use	What it means
Similarity Matrix	Quantify how similar each pair of items is based on sorts	Higher values mean greater similarity between items
Agreement Matrix	Capture how often users group the same pair of items together	Higher values mean stronger agreement on grouping
Dendrogram	Visualize hierarchical clustering of items by similarities or agreements	Items joined lower in the tree are more similar/agreed upon
Distance Matrix	Measure dissimilarity between items	The higher the value, the more dissimilar they are
Multidimensional Scaling (MDS)	Places items in 2D/3D space based on pairwise distances/similarities	The closer items are together, the more similar they are

Tool	Open	Closed	Hybrid	Randomization	Images	Live moderation	Agreement matrix	Similarity matrix	Dendrogram	AI insights
Great Question	✅	✅	✅	✅	✅	❌	✅	❌	❌	✅
Optimal Workshop	✅	✅	✅	✅	✅	✅	❌	✅	✅	❌
Maze	✅	✅	❌	✅	✅	❌	✅	✅	❌	❌
Lyssna	✅	✅	❌	✅	✅	❌	✅	✅	❌	❌
UserTesting	✅	✅	✅	✅	✅	❌	❌	❌	✅	❌
Dscout	✅	✅	✅	✅	❌	❌	✅	❌	❌	❌
Userberry	✅	✅	❌	❌	✅	❌	❌	❌	❌	❌
UXtweak	✅	✅	✅	❌	❌	❌	❌	❌	❌	❌
Userbit	✅	✅	✅	❌	❌	✅	❌	✅	❌	❌
Userlytics	✅	✅	✅	❌	❌	✅	❌	❌	❌	✅
UX Metrics	✅	✅	✅	❌	❌	❌	❌	❌	❌	❌
UXArmy	✅	✅	✅	✅	❌	❌	❌	❌	❌	❌
kardSort	✅	✅	✅	❌	❌	❌	❌	❌	❌	❌

Waste type	Description	Examples
Overproduction	Producing sooner or more than needed	Conducting more research than is necessary, like 15 interviews when 8 would have sufficed Asking questions in research when the answers are already well known Generating excessive data or documentation that isn’t analyzed or used
Waiting	Delays while expecting information, people, or goods	Delays waiting for participant recruitment, stakeholder feedback, approvals, or decisions Delays waiting for concepts or prototypes to be research-ready
Transportation	Unnecessary moves of data, documents, or people	Unnecessary hand-offs Emailing reports back and forth Moving data between tools without added value
Overprocessing	Providing more than what is required or valued	Requiring multiple rounds of reviews or approvals of test plans and materials prior to research Creating pixel-perfect prototypes for usability testing, when lower fidelity would work just as well Creating long polished presentations when only an executive summary is needed Adding unnecessary analysis
Inventory	Storing too much and carrying cost of maintaining stock	Stockpiling un-analyzed data Maintaining a backlog of research findings beyond what can be synthesized or shared Holding onto archives of video footage beyond what is needed, used, or current Keeping all draft versions of a document, even after the work is finalized
Motion	Too much movement or activity	Inefficient or manual workflows, such manual data entry Creating new documents from scratch rather than using a template or leveraging existing work Duplicating efforts across teams Using too many different tools for the same job
Defects	Error that results in failing to meet customer expectations	Errors in research (flawed methodologies, biased sampling, incorrect data, unclear reporting) that require rework or lead to inaccurate or unreliable findings