The Difference Between Causation and Correlation Research

Correlational research can tell you who buys your products, but it may or may not tell you why. For example: Let’s say that you are trying to sell instant meals. If your research tells you that working mothers buy more instant meals, you cannot draw the conclusion that being a working mother causes people to purchase instant meals. The purchase of instant meals could be due to a third factor such as being too busy to cook, having extra money, or trying to control calorie intake.

More complex correlational research studies can help you narrow down the causation, but you still cannot draw a conclusion from them with absolute certainly. In fact, the more factors you add, the more confused you may become. Let’s say you discover that only single working mothers purchase instant meals and that married working mothers rely on their spouses to cook. Should you try to market instant meals to the spouses of working mothers, or could there be yet another factor involved that is still eluding you?

Knowing for sure

If you really want to know whether being a working mother causes people to purchase more instant meals, you need to conduct a controlled experiment. This means that you would need to have a large pool of subjects, randomly assign them to be working mothers, and monitor their purchasing habits. Unfortunately, we cannot ethically force people to be or not to be working mothers. This is a lifestyle choice, and an experiment where we let the subjects choose their own treatment would not be an experiment at all.

A good way to dig deeper in your research without dealing with ethical issues is to ask an open-ended question. It may take you more time to read through all of the responses, but with the right software you can make the process much faster. You can find out how often a certain keyword or phrase is used to determine why people purchase instant meals. If a common response is, “I am a working mother,” you can be reasonably certain that being a working mother causes people to purchase instant meals. While this is still technically a correlational research study, it can give you more useful information than a survey in which you only allow simple, discrete responses.

Qualitative Research – Tips on how to use Unstructured Research Data

In my previous blog article ‘mTAB Understands Qualitative Research Needs!’  I discussed the need, or advantages, to utilizing qualitative, or unstructured, data when reviewing research results; now I want to help you learn how to implement this suggestion.

I have always visualized structured response data in my imagination as a city with sky-rise buildings and lots of traffic; there are mathematical equations creating a functioning city and it works as well as we need it to. In my imaginary city I visualize unstructured data as a reservoir of gold in the underground tunnels that the city is built on. Burgeoning cities have a tendency to expand, and expansion will generally follow the basis of the original model, but improvements are made by utilizing the underground reservoir.

Yes, fanciful, I know. Keep following me, structured response data gives you an answer to exactly what you’ve asked, but how did you decide what to ask? Have you based your survey response questions on your desires, or your consumer’s desires? Unstructured data allows for responses that are unsuspected and can lead to great improvements.

Let’s use an example: Car manufacturing. The survey asks for demographics, model preferences, etc. Generally an unstructured data response will be positioned as a means of gathering more information about a previous structured data question; e.g. ‘why did you answer yes to this question?’ This is brilliant because the survey is looking to dig deeper into reasoning, however, the full potential of unstructured data is not being utilized.

To harness the power of unstructured data one must think outside of their normal understandings. If you work in the automobile industry you immediately take for granted the need for automobiles. This assumption is the basis for the rest of your survey. Now, if you realize your previously held assumptions about your field and bring them into question, you may find that reservoir! Survey respondents don’t want to write a book while filling out your survey, so you’ve got to choose your open-ended non structured questions wisely. Some examples may be: why do you drive a car instead of taking mass transit? If money was not an issue what car would you buy, and why? What is the most important aspect of a car in your opinion? If you’ve ever considered fuel-alternative transportation, what factors concerned you? Are there any questions that you would want to ask us?

Asking your customers questions that you haven’t assumed answers to will result in responses that you couldn’t have anticipated, and insight you may have overlooked. By incorporating unstructured data responses in your survey you may just find the seedling of an idea that will greatly improve your product, and also give your customers the satisfaction of knowing that you’re interested in their thoughts.

Please be sure to follow my blog posts regarding qualitative survey analysis.  Keep an eye out as well for text analytic posts that we’re planning to run in the near future.

mTAB Understands Qualitative Research Needs!

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.