Multiple choice questions often produce this kind of data (though not always). blue), genders (male v female) or brand names (Chrysler v Mitsubishi). No item is treated as being more or less, better or worse, than the others. This kind of data exists in categories that have no hierarchical relationship to each other. It typically requires advanced tools such as Natural Language Processing and sentiment analysis to extract the full value from how the respondents answered, because of its complexity and volume. This type of data, known as unstructured data, is rich in information. Questions might begin with ‘how,’ ‘why,’ ‘describe…’ or other conversational phrases that encourage the respondent to open up. This type of response is usually given in open field (text box) question formats. Natural language data (open-ended questions)Īnswers written in the respondent’s own words are also a form of survey data. However, its simplicity means you lose out on some of the finer details that respondents could have provided. ![]() This type of question produces structured data that is easy to sort, code and quantify since the responses will fit into a limited number of ‘buckets’. Respondents can’t qualify their choice between the options or explain why they chose which one they did. Closed-ended questions can also take the form of multiple-choice, ranking, or drop-down menu items. They could be a ‘yes’ or ‘no’ question such as ‘do you live in Portland, OR?’. These are questions with a limited range of responses. Image Source: Intellispot Closed-ended questions Qualitative data highlights the “why” behind the what. It’s more likely to be descriptive or subjective, although it doesn’t have to be. It may be verbal or visual, or consist of spoken audio or video.
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