Useful Facts about Factor Analysis

While some may have heard of the technique “factor analysis” many remain unclear about exactly what it is. How does factor analysis figure into decision making? How is it applied? What is it used for?  What are “latent factors”? While these terms may sound complicated or otherwise cumbersome the fact is that the ideas behind factor analysis are rather straightforward.

What Does Factor Analysis Do?

We are all living, breathing statistics. That may sound cold, but it’s a fact. We are all consumers of media, news, information, products, and services. Because all people really are to statisticians is statistics, it’s helpful to be able to strip away unnecessary information. Take people; as complex and individual and amazing though they are; and try to reduce them down to the lowest common denominator. In doing this the statistical “dimensionality” is reduced. Rather than complex octagons we are now simple boxes.

Latent Factors

Another important thing which this technique addresses are the “latent factors.” What does that mean? Well anything latent; a feeling, an impression, a hunch; is something that can’t be measured. So when you’re doing market research there are things you can know; name, age, gender, ethnicity, income level, occupation; and things you can’t know; passions, intelligence, motivating factors, upbringing. Still with factor analysis we’re able to group these individuals accordingly by the responses they give. As some have keenly observed “the observable data doesn’t create the underlying factor; the underlying factor creates the observable data.”

How Does it Help

One thing which many people conducting surveys do is ask questions which have no real merit unless observed repeatedly. If you asked 100 people in a restaurant with 35 tables and 100 chairs and 1 waitress how their experience was; likely all of them would say terrible. However if the one waitress was covering for 30 others, then the data you got for this one particular day about this one particular waitress would not stick in any real way. Diners experience on the next day when the place was fully staffed would be far different.

As the body giving the survey you want to be dealing with facts. Cold, hard, facts; things which can be correctly transposed to different settings with the same result. A plane takes off from tremendous speed; a helicopter takes off from a stationary position. These are things which cannot be debated. If you’re trying to group results together, factor analysis can really help. You are dealing with measureable items, your latent observation has been borne out and you can group these respondents accordingly.

Return Shoppers

You can also get measurable results if you’re asking the right questions. Using this technique you should be able to accurately peg a customer’s likelihood that they will purchase a product or service after using it once. If this technique was found to be accurate over an extended period of time it would be immeasurably valuable to all businesses who are trying to discover a products survivability in the marketplace. Using a tool like factor analysis to determine which products to push on full force and which to abandon would vastly change the landscape of the marketplace.

Of course whether or not this remains true over the longer haul; whether this techniques implementation has any lasting chance of thinking before and ahead of consumers, is still up for debate. Still with so much potentially riding on factor analysis’ success or failure, it’s something we all should try and get our heads around sooner rather than later. Nobody wants to be behind the 8-ball on the next big, sure thing!

The Rise of Infographics in Presenting Data Analysis

In an earlier post we talked about the difference between graphics used for visualization of data points and graphics used for presentation.  We concluded that the point of an analyst’s effort when analyzing survey data was to communicate the results to busy decision makers in a format they could understand.

Enter The Infographic

Using information graphics to convey an idea or meaning has been around since the earliest cave paintings. Today, infographics are an essential part of the survey analysts tool box because they convey complex data in an easy to follow and visually appealing format.

From blog posts and web articles to glossy brochures and of course, data analysis presentation, infographics are a ubiquitous part of the information landscape. But why have they become so prevalent?

Infographics Are Easier Than Ever To Create

Modern computers and sophisticated software can easily render thousands, even millions, of data points into a visual representation, often with nothing more than a mouse click.  What used to take hours to create by hand (and yes, most graphics used to be done by hand, I’m talking 1980s and 90s, not the 1800s!) can now be done as a matter of course.

Decision Makers Have Faster Access To More Data than Ever Before

The trend toward greater use of infographics results in part from the speed at which information is available to decision makers. The Internet and the World Wide Web have transformed not only how we receive our information but how fast we have access to it.  Also, our expectations regarding how much information we are willing to absorb has changed.  When was the last time anyone picked up a 2000 page reference book and actually read it?

It’s Easier To Look At An Infographic Than It Is To Read About The Same Thing

Today data and information comes at us in packets.  This blog post is an excellent example.  It’s short, concise, and to the point.  The title and sub headings tell you most of what you want to know regarding the topic and they provide key information you might need to justify a decision to use more infographics in your next data analysis presentation.  The rest of these words are written to support the headings but the important information might’ve been rendered visually rather than in prose.  If it were, you might have spent half the time absorbing the information.

Now, if I could present this post using only an infographic…

Closing the Information Loop with Actionable Data

Analyzing survey data and turning it into actionable data is the key to maintaining your current customers and adding to that customer base. But what is actionable data? Basically, actionable data is your survey results input into an improvement program to modify your business and make it better.

In order to effectively transform your piles of information into actionable data and make analyzing survey data work, you need to look at the actual questions you ask on your survey. If you don’t ask the right type of questions your data could be misleading and as a result, you will misuse it with no benefit to your company.

Close Ended Questions

These are simple clear questions that ask a survey taker to rate specific aspects of the products, services and experiences they engaged with. They respond on a numbered scale or check boxes of different graduated measurement to reply to the questions. This type of questioning has two useful advantages.

  • Easy Analyzing: Analyzing the survey results is very simple, since all the data is coming to you as a check mark, number or yes/ no answers. The guess work is taken out of the analysis and ambiguity is kept to a minimum. The simple responses make calculating the results and transposing them into percentile information or mathematical equations very easy.
  • Easy to Take: The actual survey itself is easier for your subject to navigate through. Clicking on responses they identify with is much easier than writing down specific detailed narratives. If it’s easy more people will take it and that will give you more data to use in your growth process.

Open Ended Questions

Open ended questions ask your survey takers to describe something in detail. While analyzing survey data, the more detail oriented the survey answers are, the more focused and exact our improvement efforts can be. The problem is that open ended questions rarely get answered and if they do it’s with one or two words. Not really the detail you were looking for and very misleading if you use it. Open ended questions should be used sparingly and only as an addendum or supplement to the closed question parts.

Filtering and Branching

Two survey construction techniques that, when used correctly, will make analyzing survey data easier by pre-sorting and categorizing certain elements of the survey as determined by you.

  • Filtering: In the survey, it serves the useful purpose of weeding out the subjects that you don’t need results from and lets you focus on subjects whose opinions you want. When it comes to analyzing, you can implement filters like location, age, or whatever to help with the process. This allows you not waste peoples time (including yours) and will increase the amount of useful data.
  • Branching: Similar to filtering, branching just divides up your subjects and sends them down different survey roads, if you will. This allows you to view specific results easily and categorize your raw data. Branching serves as a pre-sorter that will streamline you process for analyzing survey data.

Conduct Interviews: Yes, more surveys. I know it sounds weird but if you think about it, it makes sense. Interviews effectively expand the scope of your resulting data. The pre- and post-survey interview helps to make sure the subjects understand the topic or field the survey is concerned with and they add dimension to the scope of the main survey.

Analyzing survey data and turning it into usable, actionable data will positively effect your business. Invest the resources into well thought out data gathering and analysis programs and reap the rewards.

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.