How to analyze open-ended survey responses with simple coding and themes

Open-ended survey questions give the respondents the opportunity to share their opinions in their own words. Feedback in this manner can provide more insight into the opinions, frustrations, motivations, and experiences of the people.

Organizations and businesses use open-ended survey questions to gain more insight into the “why” of the results obtained from the survey. Even though quantitative survey questions provide specific results, the feedback obtained from open-ended survey questions provides context and explanations for the results obtained from the survey.

However, analyzing the feedback obtained from the survey can be a daunting task when the number of responses is in the hundreds. Without a clear structure, valuable insights may remain hidden in long blocks of text. Qualitative analysis of the results can help in the analysis of the results obtained from the survey.

The Challenge of Interpreting Open Text Responses

In contrast to multiple-choice answers, open-ended answers do not have a similar pattern. Some answers may be long, while others may be very short. The difference in writing styles, word usage, and even emotions makes it difficult to compare them. Analyzing a large number of answers can be confusing.

If the researcher tries to read each answer separately without a plan, it can even cause a personal bias where they only identify the answers that they find important. Qualitative coding techniques can be used to solve this problem. By using these techniques, researchers can easily identify the important ideas presented in each answer and group them. This makes it easy to organize the answers and analyze them.

What Qualitative Coding Means

Qualitative coding is the practice of assigning certain parts of the text a word or phrase that describes the content of the text. These words or phrases are referred to as codes. Each code is a summary of the main idea of the text being discussed in the responses.

For example, if many people have trouble using a website, the responses can be coded with the phrase “website usability issues.” By using coding, the researcher is not required to read and analyze all the responses individually. Instead, they can look at groups of responses with the same code.

This makes the responses more manageable and helps readers understand the experiences and concerns of the survey respondents. However, coding is not the practice of simplifying the responses too much; rather, it is the practice of organizing the responses in a way that makes the reader understand them better.

Step 1 – Read the Entire Dataset First

The first step in analyzing open-ended survey responses is to read all responses without coding. This is important as it allows one to understand the tone of the responses. At this stage, it is important to recognize ideas in the responses without coding. One can take notes of common phrases or ideas. For example, there might be many responses that complain about delays in customer service or the pricing of products.

At this stage, one gets familiar with the responses. It is also important to ensure that one is not analyzing responses based on assumptions. The responses should be read completely to ensure that one is analyzing responses based on the participants’ views. This is an important stage as it lays a foundation for coding.

Step 2 – Develop an Initial Coding List

Following this, the researchers will start developing a list of codes. These are general ideas that are present in the overall dataset. For example, responses from a survey based on a shopping experience may have codes like “pricing concerns,” “delays in delivery,” or “good customer experience.”

These need to be kept simple and descriptive enough to identify the general idea behind them. At this point, the codes do not have to be perfect. They can be changed as the analysis moves forward. More codes may be identified as the responses are read more carefully. Having a coding list helps in maintaining consistency in the analysis. When similar responses are given the same code, patterns can be identified more easily.

Step 3 – Assign Codes to Individual Responses

Once the coding framework has been established, the researcher will begin the process of applying the codes. This involves carefully reading the comments and applying the appropriate code or codes that reflect the ideas presented. Some comments will have several themes, which means that several codes will be applied. This is because the respondent will have several ideas in one comment.

For example, the respondent will have ideas about the price and the quality of the product in the same comment. In this case, the researcher will apply two codes. Coding involves careful consideration of the context in order to ensure that the right ideas have been captured. As the researchers continue the process of coding, they will have the opportunity to modify the codes.

Step 4 – Organize Codes into Broader Themes

Once the coding is done, the next step is to group the codes into larger themes. Themes are used to describe larger trends that can be identified in the data. For instance, if the codes used in the data include “slow shipping,” “late deliveries,” and “missing packages,” it is likely that all these would fit into a theme that is defined as “delivery issues.”

It is important to note that themes help move beyond individual comments in the data to focus on larger trends. This is because themes help in summarizing the data in a way that ensures the most important trends in the data are identified. Additionally, themes help in effectively communicating the results of the data to stakeholders because it is easier to identify the overall message in the data.

Comparing Theme Frequency

While the emphasis of qualitative research is not based on numbers, the overall frequency of the themes can be useful in providing additional context. For example, the overall frequency of the themes can be used by the researchers. They can look at the overall frequency of the themes that are present in the responses.

However, they should not forget the importance of the less frequent responses. At times, the less frequent responses can provide critical insight into the problem. By using the thematic and frequency analysis together, the researchers can provide a balanced approach during the analysis.

Using Example Quotes to Illustrate Themes

The use of quotations from survey participants would enrich the qualitative analysis. Quotes are used to give specific examples that represent each theme and how the participants expressed their experiences. When presenting the analysis findings, quotations are often used to convey the theme’s main idea.

For example, if the theme is about delays in customer service, there would be a quote about the long wait time experienced while waiting for support. The use of quotes would give the reader an idea about the tone and context of the feedback. It would also prove that the analysis is not based on assumptions but is grounded in reality.

Manual Coding vs Software Tools

Researchers can use various techniques to analyze the open-ended survey data. They can do it manually or with the assistance of various tools. Manually coding survey data can be useful for a small number of surveys. However, for many surveys, researchers can use qualitative data analysis tools.

These tools can be useful for organizing and analyzing large volumes of data. Although researchers can use these tools to assist in analyzing survey data, they must still interpret the data and determine how to code it. The tools can assist them in organizing the data more efficiently.

Turning Themes into Actionable Insights

The end result of all the analysis of the open-ended survey results is the creation of insights that can be used for decision-making. After the themes have been identified, the results need to be interpreted in relation to the organization.

For example, if the results show that many people have trouble with the instructions, this may mean that the product instructions need clarification. Similarly, if the results show that people are pleased with the customer support, this may mean that the customer support is effective and should be continued.

By effectively communicating the results of the analysis, the organization can better understand how the feedback of the survey participants can be used for recommendations. By analyzing open-ended survey results, the organization can gain a better understanding of the audience.

Conclusion

Although it might seem like an intricate task at first, analyzing open-ended survey results is actually quite simple. It is achieved by reading the results, coding them in an important manner, and then arranging them into larger themes. This is an effective way of allowing organizations to find commonalities, hidden opportunities, and even the reasons behind the opinions of the participants.

It is also useful to include examples in the results, as well as the overall themes. It is imperative that organizations conduct an effective analysis of the results from open-ended surveys. This is because it gives an organization valuable context that is not provided by the results of quantitative surveys.

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