If you find yourself slightly disappointed by the quantity or quality of text comments provided by your respondents you are definitely not alone. This is a common problem especially when survey respondents are not compensated for their answers and when they are allowed to leave open-ended questions unanswered.
However, don’t give up and immediately start collecting more data or design a new survey. You current dataset may still contain valuable information in the form of text comments. A good practice is to pool together all text comments from a number of text variables in your dataset. You can select all of them or just a subset that makes the most sense to be analyzed together.
Figure 1. Pooling text data for a richer analysis.
In the attached figure, the bubble on the left represents probably the most frequently analyzed question in customer satisfaction surveys – the open-ended question following a key rating (e.g., Overall Satisfaction Rating or Net Promoters Score Rating). Most of these surveys will have at least one or more very good questions that can compliment the answers given to the open-ended question on the left (see the remaining bubbles on the right of the figure). So why not analyze them altogether? To do that – simply merge these text variables in your data editor remembering to leave a blank space between the content of the columns you are merging.
Conclusion: Enriching your data can be simple.
This very simple pooling of text data from various open-ended questions will allow you to significantly enrich you analysis in OdinText.
[NOTE: Gosia is a Data Scientist at OdinText Inc. Experienced in text mining and predictive analytics, she is a Ph.D. with extensive research experience in mass media’s influence on cognition, emotions, and behavior. Please feel free to request additional information or an OdinText demo here.]