I am an administrative analyst at Productive Access, the creators of mTAB™. I hold a masters degree in Sociology, and used qualitative analysis to write my thesis. With this background I understand the statistics behind survey data analysis and the use of cross-tabulation to analyze survey data, but neither technique is really my forte.
I had always considered mTAB™ a program that gives priority to quantitative data. Generally marketing departments, and analysts, are looking to crunch the numbers of their demographics in order to fully understand the popularity of their products, so qualitative analysis isn’t in high demand. Some executives, however, recognize the importance and need for qualitative research. Data tabulations can only go so far; as a means of truly understanding what data is saying, innovative analysts utilize qualitative analysis techniques using survey verbatim questions, otherwise known as unstructured data.
Ultimately quantitative and qualitative data work best as a team. For example a particular brand of car might not be selling well in certain demographics. Some may look at this quantitative finding and decide to scrap their model. This is where qualitative research really shines; looking into what is said by those unsatisfied consumers may highlight a product need that would have been otherwise overlooked. In sociology we were always made very aware of this phenomenon.
My professors always said, “Correlation does not equal causation”. This sentiment is true in all research. Correlations can be spurious and based on another factor. For example: rainbows seem to be positively correlated with a healthy garden. Seeing this data I may ask my employees to create a rainbow machine so my tomatoes will grow. You may think this sounds silly, but many marketing departments and analysts look at the correlations of data and don’t look into the other factors. Rainbows happen after it rains. Rain helps gardens grow. Rainbows have a spurious relationship to healthy gardens.
Luckily Productive Access understands the need for both quantitative and qualitative research, and has worked hard to incorporate a system that will help the mTAB™ user better understand the implications of their survey’s unstructured data: tag clouds. I was extremely pleased when I saw this function added to our latest release of mTAB™. Now when an analyst wants to further understand their data they can look to their verbatim responses.
As a researcher I understand that qualitative data is really rather daunting; the researcher may have no idea where to start, and the paragraphs of responses are not easily sorted into crosstabs that show percentages. With the addition of the Tag Clouds in mTAB™ you now have an immediate indication of the most prevalent words used within the verbatim cells. When using tag clouds in mTAB™ a dialogue box appears showing the most commonly occurring words from the Verbatim Report and indicates the frequency of occurrence by size and boldness. The largest and boldest being the most frequent. Now with one look you can tell what issue is most prevalent within the group you are analyzing.
There is a great webinar on the PAI web site that illustrates how to incorporate a qualitative analysis of unstructured survey data within the framework of qualitative cross-tabulation survey analysis. This short video clearly illustrates how mTAB’s tag cloud feature benefits the analysis of surveys containing both structured and unstructured data.
I love the new Tag Clouds in mTAB™, and am happy to be with a company that persistently works to improve their program for all users.
If you would like to find out how the PAI team could help you combine the analysis of your survey structured and unstructured data, please register here for a no-obligation consultation.