Question Types

Choose one answer. (Radio Button)

Check all that apply. (Checkbox)

Choose one answer (Drop Down Menu)

Bipolar Scaling for example: Strongly Agree...Neutral...Strongly Disagree

The most widely used scales used in the measurement of attitudes

Multiple questions grouped together. Create single select, select many, and spreadsheet style answer options.

Choose from numeric text, free-form text, or single-row text. Each type can be validated.

Change the size of any text input field.

Consolidated contact information collection via a single question.

Distribute values over answer choices summing to 100 with validation.

Rank answer choices with validation.

Respondents can drag and drop the answer choices to re-order them as they choose.

Smiley face question is a fun and engaging way of collecting feedback on a 5-point rating scale, intended to represent a range of sentiments from negative to neutral to positive.

Rank answer choices using gold stars.

Share positive feedback/rating on social sharing platform.

Validated Date/Time questions.

Question with introductory text at the beginning of the survey with validation for acceptance checkbox.

Display text with no answer options.

Display formatted heading text for sections.

Use slider scale question type to collect data on up to 100 point scales. Slider scales work great on mobiles and tabs too!

Use Image / Multimedia type question for rating / ranking.

Ability to select and display a single question from a list of question.

Ability to randomly display blocks of questions.

Collect and segment responses on a 0 - 10 point scale into promoters, passives, and detractors. Based on a scoring model developed by Bain & Co. Consultant Frederick F. Reichheld.

Use Side-By-Side Matrix for gathering information on different dimensions for the same attributes.

Allow users to validate data against standardized databases like Zip Codes, Canadian Postal Codes etc.

Allow users to upload / attach files on the Survey.

Create dynamic "Parent-Child" relationships (Eg. State->County, or Division->Dept.)

Using machine learning to rank and sort a large number of items - in pairs.