Open-Ended Survey Questions Analysis: Approaches to Analyzing Answers

People can always learn something good and interesting when they include open-ended questions in their online surveys. They can take advantage of it when there are uncertainties about the answers people might provide to their problems. With open-ended questions, researchers can provide respondents text boxes and let them say what they want to say.

But the first time companies look at all the responses in their data files, they might have a crucial question of their own: What’s the best approach to understand and analyze these replies? In reality, there is no one-way (a perfect way at least) to turn all answers into clear findings. Every approach has its advantages and disadvantages and may depend on the situation. Listed below are some strategies that the national survey company finds pretty helpful when checking open-ended responses.

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Code response in buckets

The everyday use of these pieces of information is coding. Using this approach, people’s responses are assigned their own numerical code. Each code will represent a bucket of segments with similar answers. For instance, if you code the answer, “This software is pretty easy to use” under the bucket “Easy to Use.”

Once responses have been assigned with a code, the text data can then be treated and quantifies like variables in a “Select All That Apply” or “Select One” question format. Market research firms can also check out or run an analysis on the numeric date to generate a chart and cross-tabulation.

The disadvantage with coding is that it can be a time-consuming and manual process. Usually, researchers will check through open-ended responses one by one. They will then decide what codes or codes can best fit with it. Although it is pretty tedious, it can be a very accurate way to group responses into more significant themes.

Share the full list

Another excellent way to present answers in surveys is to provide every available list of texts. The best practice includes cleaning every answer to make it client-friendly and client-ready. The process consists of checking for grammar, spelling, inappropriate responses, proper nouns, and punctuation.

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The main benefit of this approach is the ability for clients to read every answer in their original information. Sharing all the lists can also make a lot of sense when most of the replies contain a couple of sentences or more.

Understanding the context of these answers will help companies get the most out of the information. The disadvantage of sharing every set of open-ended replies is focused on the time it will take to clean the responses and the consequences of sharing unchecked or unfiltered answers with clients.

Companies need to consider how long it will take to check and correct single replies before delivering open-ended responses. Some negative feedback in the answers can also do more harm than good.

Put every text into the word cloud

The word cloud can offer a low-effort and fast way to identify buzzwords across the open-ended survey questions analysis industry. They are typically designed with software and are visualizations that are based on the frequency of the words being mentioned. If needed, phrases can also be bundled together.

The size of words inside the cloud will immediately tell readers what types of words are necessary to the question being asked. This kind of approach will work well when the associated question only asks for a phrase or word. The cloud is less helpful in cases if the replies needed multiple sentences or are complicated in nature. There are also usually little contexts when reading word clouds. Companies are not getting the full context, just keywords or phrases.

Use text analytics

Automation is starting to be used in open-ended answers from surveys. These tools attempt to make these things very efficient to get answers from the text information with minimal human intervention. One of the techniques used is automated coding. It is artificial intelligence used to code open-ended replies into buckets automatically.

It is usually a significant time-saver, but its accuracy is still not on the level of manual coding. Another example of text analytic is sentiment analysis. Tools can determine the sentiment of the response to see if it is positive, neutral, or negative. This method is very useful for unstructured data like social media posts.