When evaluating or gathering feedback about a product or service is it very common to ask open-ended questions to respondents alongside the closed-ended survey questions. A simple ‘yes’, ‘no’ or any predefined answer might not be sufficient as a response option.
If your survey consists of only closed-ended survey questions, respondents select from a finite set of pre-defined responses, and it does not give them the freedom to express their opinion further.
The common problem with open-ended questions, or more specific, their answers, is that after all the data is collected the user is left with a bunch of uncategorized data. Everyone has their own opinion and their own wording about a product, service or subject. Text data from open-ended questions in surveys is often ignored due to the challenge of analyzing. Answers will differ in level of detail and scope. This data is important as it does not constraint the answers given by respondents.
In order to make this whole collection of various text a whole lot easier EyeQuestion can categorize those -at times- endless pieces of text into easy-to-understand words which have a more defined meaning.
Our solution for this is called Remark Labelling which is available from version 5.0.8.x for all customers. It gives the user the option to assign one or multiple labels to pieces of text from open ended responses and analyze the data afterwards. Once the data has been labelled the data analyst is able to identify key themes and patterns.
In EyeQuestion, the user can manually allocate an attribute that is considered as relevant
to the free comments.
The size of the words in the cloud reflects the number of time a label has been attributed to one
of the free comments from the panellists related to this product.
For a step-by-step guide on How to use Remark Labelling, please go to our Knowledge Base.