CBC core settings

The goal of CBC design is to collect enough information from each respondent to accurately estimate their preferences without overwhelming them. Discover automatically recommends settings for the number of tasks, concepts, and versions in your exercise.

These recommendations strike a balance between precision and respondent effort, reduce order bias, and ensure that attribute levels are combined and distributed evenly across respondents.

If you want to change these defaults, turn on the Override recommendations toggle:

• With the toggle off, input fields are disabled, and Discover’s recommended settings are applied.
• With the toggle on, you can enter your own values. Switching it back off reverts to the defaults.

This setting controls the number of choice tasks each respondent will answer.

Sometimes, Discover may recommend a relatively high task count. In general, we don’t recommend lowering this number, since fewer tasks reduce the information available for estimating accurate preference scores.

If the recommended number of tasks feels too high for your audience, consider:

• Reducing attributes or levels. This reduces the number of tasks required while maintaining high accuracy high. We suggest limiting exercises to about 10 attributes or fewer to minimize fatigue.
• Using larger sample sizes. With hundreds of respondents, random error tends to cancel out. In these cases, you may be able to use fewer tasks while still achieving reliable aggregate results.

This setting controls the number of concepts respondents compare in each task.

• Discover bases its recommendation on the largest number of levels in any attribute.
• Statistically, more concepts per task improve precision.
• Practically, more concepts increase cognitive burden and may be harder to display on smaller screens.

Guidelines:

• Show at least three concepts per task (plus the “None” option) in most studies.
• In sensitive contexts (e.g., healthcare decisions), two concepts may be more manageable.

As you set up a CBC, certain design choices may be flagged with warnings or errors in the survey audit. These messages highlight potential issues with data quality or respondent experience. Here’s what each one means and how to address it.