
The Basics
Some of the popular survey logic includes question skip patterns, showing and hiding answer choices, piping of responses, randomization of answer choices and questions, value range requirements, and much more.
Collecting quality data starts with building the best possible survey experience for each type of device. Browse through the demo below to see how we crafted each question type to look its best on mobile and desktop.
We take care of all the hard stuff behind the scenes to create a seamless survey experience for respondents. Our amazing team of in-house survey programmers and our powerful survey engine makes it possible to handle virtually any logic requirement. From simple survey flow logic to more advanced back-end calculations, we’re confident we can accommodate any request.
Some of the popular survey logic includes question skip patterns, showing and hiding answer choices, piping of responses, randomization of answer choices and questions, value range requirements, and much more.
Besides handling basic logic, we can accommodate any custom logic requests, from custom calculations and typing tools to complex trade-off exercises and beyond. If you can think of it, we can probably do it.
It’s hard to ask healthcare professionals to participate in what is often an involved and lengthy survey. Our default survey continuity feature enables busy, on-the-go healthcare professionals to switch to a different device while taking a survey and seamlessly continue from where they stopped.
The quality of gathered survey data is critical to making smart decisions. Using a variety of real-time quality checks, we constantly monitor the collected data for any irregularities. If any responses look suspicious, we will manually review the entire record and replace it if it does not meet our quality expectations.
A respondent who rates all attributes in a grid with the same number may have a legitimate reason to do so, but we’ll flag and check the record to make sure that the quality of data is not compromised.
Some respondents may respond to a question quicker than others, but there are reasonable thresholds that we monitor for each question, and flag records that deviate from the expected average.
Quality of data depends on whether or not the respondent is paying attention to the questions in front of them. An easy way to test this is by asking respondents to answer simple questions with expected responses.
In order to achieve a representative sample, a sophisticated quota engine monitors all pre-defined quota groups to ensure the success of the project. Quotas can be defined by one or more survey questions, sample data, or other variables. Quota groups can also be nested in a hierarchy to execute a more complex sample plan.