Software For Survey Data Analysis

[Aliza Pointon]

Thistype of data, known as unstructured data, is rich in information. It typically requires advanced tools such as Natural Language Processing and sentiment analysis to extract the full value from how the respondents answered, because of its complexity and volume.

This kind of data exists in categories that have no hierarchical relationship to each other. No item is treated as being more or less, better or worse, than the others. Examples would be primary colors (red v. blue), genders (male v female) or brand names (Chrysler v Mitsubishi).

Unlike categorical data, ordinal data has an intrinsic rank that relates to quantity or quality, such as degrees of preference, or how strongly someone agrees or disagrees with a statement.

Like ordinal data, scalar data deals with quantity and quality on a relative basis, with some items ranking above others. What makes it different is that it uses an established scale, such as age (expressed as a number), test scores (out of 100), or time (in days, hours, minutes etc.)

Target the survey questions that best address your research question. For example, if you want to know how many people would be interested in buying from you in the future, cross-tabulating the data will help you see whether some groups were more likely than others to want to return. This gives you an idea of where to focus your efforts when improving your product design or your customer experience.

Cross-tabulation works best for categorical data and other types of structured data. You can cross-tabulate your data in multiple ways across different questions and sub-groups using survey analysis software. Be aware, though, that slicing and dicing your data very finely will give you a smaller sample size, which then affects the reliability of your results.

Look at how different demographics within your sample or research population have answered, and compare your findings to other data on these groups. For example, does your survey analysis tell you something about why a certain group is purchasing less, or more? Does the data tell you anything about how well your company is meeting strategic goals, such as changing brand perceptions or appealing to a younger market?

Look at quantitative measures too. Which questions were answered the most? Which ones produced the most polarized responses? Were there any questions with very skewed data? This could be a clue to issues with survey design.

One of the most powerful aspects of survey data analysis is its ability to build on itself. By repeating market research surveys at different points in time, you can not only use it to uncover insights from your results, but to strengthen those insights over time.

Maintaining your question and data types and your data analysis methods means you achieve a like-for-like measurement of results over time. And if you collect data consistently enough to see patterns and processes emerging, you can use these to make predictions about future events and outcomes.

Another approach is to express data using the power of storytelling, using a beginning-middle-end or situation-crisis-resolution structure to talk about how trends have emerged or challenges have been overcome. This helps people understand the context of your research and why you did it the way you did.

The #1 way to make your research hit the mark is to start with the end in mind. Before you even write your survey questions, make sample headlines of what the survey will discover. Sample headlines are the main data takeaways from your research. Some sample headlines might be:

Your survey is one star in a constellation of information that combines to tell a story. Use every atom of information at your disposal. Just be sure to let your audience know when you are showing them findings from statistically significant research and when it comes from a different source.

One way you can ignite change with your research is to write a press release dated six months into the future that proudly announces all the changes as a result of your research. Maybe it touts the three new features that were added to your product. Perhaps it introduces your new approach to technical support. Maybe it outlines the improvements to your website.

Everyone consumes information differently. Some people want to fly over your findings at 30,000 feet and others want to slog through the weeds in their rubber boots. You should package your research for these different research consumer types.

As was true with designing a survey, books have been written and entire courses developed on survey analysis. What follows are some questions to ask yourself before and during your analysis along with resources to learn (much) more.

One of the first things you should report in a survey analysis is the response rate (number of responses divided by the number invited to take the survey). This is important to give the reader a sense of how generalizable (more below) your results may be.

Still, researchers have found that even low response rates (i.e. 5% to 10%), in higher education student surveys with sampling sizes of at least 500 produced reliable estimates of several measures of student engagement.

Early in your reporting, share your confidence interval and/or margin of error with your audience so they may more fully understand the limitations of your results. The following are straight copies and pastes from Statistics How To.

You need to report how representative or generalizable your responses are to the larger population. This is often done by looking at the breakdown of responses by sub-groups (e.g. gender, race, age) compared to the overall population. If the proportions in the responses are fairly close (consider using a Chi-Square Goodness of Fit Test) to the proportions in the overall population, you might feel your results are fairly generalizable.

Customer surveys can have a huge impact on your organization. Whether that impact is positive or negative depends on how good your survey is (no pressure). Has your survey been designed soundly ? Does your survey analysis deliver clear, actionable insights? And do you present your results to the right decision makers? If the answer to all those questions is yes, only then new opportunities and innovative strategies can be created.

Survey analysis refers to the process of analyzing your results from customer (and other) surveys. This can, for example, be Net Promoter Score surveys that you send a few times a year to your customers.

Then you apply the cross tab to look at different attendees to look at female enterprise attendees, female self-employed attendees etc. Just remember that your sample size will be smaller every time you slice the data this way, so check that you still have a valid enough sample size.

For example, look at question 1 and 2. The difference between the two is that the first one returns the volume, whereas in the second one we can look at the volume relating to a particular satisfaction score. If something is very common, it may not affect the score. But if, for example, your Detractors in an NPS survey mention something a lot, that particular theme will be affecting the score in a negative way. These two questions are important to take hand in hand.

For tips on how to analyze results, see below. This is a whole topic in itself, and here are our best tips. For best practice on how to draw conclusions you can find in our post How to get meaningful, actionable insights from customer feedback.

The way to get around this issue is to perform a sample size calculation before starting a survey. Then, you can have a large enough sample size to draw meaningful conclusions, without wasting time and money on sampling more than you really need.

Whichever way you code text, you want to determine which category a comment falls under. In the below example, any comment about friends and family both fall into the second category. Then, you can easily visualize it as a bar chart.

Traditional survey analysis is highly manual, error-prone, and subject to human bias. You may think of this as the most economical solution, but in the long run, it often ends up costing you more (due to time it takes to set up and analyze, human resource, and any errors or bias which result in inaccurate data analysis, leading to faulty interpretation of the data. So, the question is:

On a large scale, software is ideal for analyzing survey results as you can automate the process by analyzing large amounts of data simultaneously. Plus, software has the added benefit of additional tools that add value.

Interpris is another tool from QRS International, where you can import and store free text data directly from platforms such as Survey Monkey and store all your data in one place. It has numerous features, for example automatically detecting and categorizing themes.

There are numerous tools on the market, and they all have different features and benefits. Choosing a tool that is right for you will depend on your needs, the amount of data and the time you have for your project and, of course, budget. The important part to get right is to choose a tool that is reliable and provides you with quick and easy analysis, and flexible enough to adapt to your needs.

Your surveys will reveal what areas in your business need extra support or what creates bottlenecks in your service. Use your surveys as a way of presenting solutions to your audience and getting direct feedback on those solutions in a more consultative way.

As a respondent you want to know your responses count, are reviewed and are making a difference. As an incentive, you can share the results with the participants, in the form of a benchmark, or a measurement that you then report to the participants.

Survey data analysis is the process of extracting meaning from the dataset you've gathered. Using survey analysis techniques, one can find correlations, patterns, trends and other insights that can be useful for businesses to guide their decision making process.

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