You have recently started an online survey? Congratulations! The next step is to draw conclusions from a wide variety of data, and you might raise the question of how to set up an effective survey data analysis. There are several ways to make sense of qualitative data or quantitative data. Thus, you can examine overreaching patterns and trends you would have missed otherwise.
When conducting a survey, you usually ask a set of questions to a particular target group, e.g., your customers. A question can have Yes / No answer options, or respondents can select from a list of options. Another type is to ask open-ended questions where respondents can write free text, for example, about how they have experienced a product or an event. The aim can be to understand factors that influence a target group.
When you read an article, you might come across statements like “60% of all customers” bought a brand X T-shirt because of its unique design. This is a clue that a survey has been conducted. Answers to open-ended questions can provide further insight. Respondents might elaborate on why they like the design, on the reactions they have received when wearing the T-shirt, and so on. When combining the responses to qualitative and quantitative data, you may find out that those customers writing about their friends’ reactions are younger than 35. When adding survey data to ATLAS.ti, the primary data body will be the answers to the open-ended questions. Additional quantitative data can be used for a mixed-methods analysis to compare and contrast responses of the various groups in your target sample.
To get actionable insights, you need to ensure that you measure the right things. Be aware that every business is different. What works for company A may not work for company B. Firstly, find out what is essential for your business or field. Secondly, ask the right questions (e.g., ask yourself whether your target audience can answer them). And thirdly, address them to the right stakeholders to get the information you need. Also, think about appropriate demographic to later segment the data and compare and contrast relevant groups.
When analyzing the data, keep in mind how you will present them. Make sure everybody understands why these data are essential and what they mean. How will they affect the business?
All survey programs can export the data as an Excel table. This is what you need to import the data into ATLAS.ti. Since version 22 of ATLAS.ti, there is no longer the need to prepare the data following a specific format. You select the Import Survey option, and a wizard guides you through the process of adding the data.
You decide which section should make up the document name (e.g., the respondent number, the IP address, an email, a name), which variables should be turned into document groups for later data comparisons, and which columns in the Excel table contain answers to open-ended questions.
The software creates a document for each participant, and the answers to the open-ended questions make up the body of each document. The papers are added to their respective groups as indicated by the variables. Each question is automatically coded. A short name can be used as a code label, and the complete question can be used as a code definition. ATLAS.ti thus performs crucial preparatory work in a few seconds.
The next step could be to run a concept search. This tool suggests suitable concepts that you can use to code the data automatically. Other machine learning (ML) tools that will help you quickly generate insights are Sentiment Analysis and Named Entity Recognition (NER). Sentiment analysis codes the data by positive, neutral, or negative sentiment. NER finds persons, organizations, locations, and miscellaneous items like famous art pieces, landmarks, etc. This allows you to relate text found through the concept and NER searches to their sentiments. Which aspects were reported on positively, and which ones have been perceived as unfavorable? Further, you can run a comparative analysis exploring differences among target groups. Do men and women express the same opinions? If not, where do they differ and why? Are there differences between age groups or locations?
For further information on how ATLAS.ti works, please read this article.