Top 5 Survey Data Analysis Tips for Better Insights

Congratulations on finishing your survey with SurveyPlanet. Now that you’ve gathered the responses, it’s time to execute the survey results analysis. Whether it’s been a few weeks or a few months since you last revisited the project, it’s always a good idea to refresh your memory of your goals and purpose.

Look for the most valuable result you are trying to answer and keep this in the back of your mind when analyzing information. In this way, if you come across relevant data, you will recognize it much sooner.

To start analyzing your survey, click on the “export results” button to move the results into your favorite format (Excel, for example). SurveyPlanet Pro offers several other export options as well (PDF, Word, or another file type). These five tips will help you glean better insights from your survey.

1. Review Top Research Questions

The first step to survey-data analysis is reviewing the top research questions. This allows you to calculate results from what you deem as your top questions.

Let’s say you surveyed people who attended a recent charity event. One of your top questions might be: “Would you attend this event next year?” Take a look at your top questions to see how respondents answered. Perhaps you will find that some of the survey questions were poorly worded because they didn’t produce the desired information. If you are wondering what makes a bad survey question, read our blog about it to prevent such problems in future surveys.”

Results will show both percentages and raw numbers, which are the numeric amount of people who answered a certain way, while the percentages reveal the proportion of people who responded that way. Looking at both is an easy way to gain an overview of your top questions.

Once you gather the basic results, you can analyze them further to find correlations, trends, and other comparisons.

Choose the right question type for your survey

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One of the basic distinctions of survey questions is between close-ended and open-ended ones. Close-ended questions provide a set number of answers, while open-ended ones give the respondent the ability to answer in their own words.

Whether you do market research surveys or are examining consumer experience and customer service experiences, you will have to choose one of these question types. There is no correct answer to which is better, though depending on the situation one type can give more insights than the other.

Close-ended questions can be more useful when there will be a large number of examinees since it would be impractical to read 1000 different answers. The general advice is to use close-ended questions when you are performing a quantitative survey, while open-ended questions are more suitable for qualitative surveys.

Find out the difference between quantitative and qualitative surveys.

2. Use Cross-Tabulation and Filtering

Think back to the goal you set for your survey. To assess this goal, analyze different subgroups and compare them to derive conclusions. This way, you can get valuable customer feedback and useful information. For example, let’s say you want your survey about the charity event to compare responses from the various professions represented by the people who attended the event. Use cross-tabulation to see how doctors, lawyers, and nurses responded to the question.

Cross-tabulation gives you insights into how subgroups answer certain questions. It can also help draw insights from the survey by adding context to the numbers, like how different subgroups behave or how different factors influence outcomes.

With this method of survey data analysis, you can begin to learn more about different subgroups. Once enough information is gathered from cross-tabulation, you can then start to look into why subgroups answer a certain way and how to make improvements using this data.

How to analyze survey data in Excel

In addition to cross-tabulation, using an Excel filter option is also useful for narrowing down your data into subgroups. Filtering allows you to view one subgroup’s results and eliminate those of other groups. You can also combine cross-tabulation and filtering to compare and contrast different subgroups. Remember, your sample size changes each time you add or subtract a group from your filter. Keep this in mind when drawing conclusions

Once you’ve evaluated the overall results of your top questions and used filtering and cross-tabulation to evaluate individual groups, it’s time for a deeper dive. One way to evaluate your survey data and/or make a statistical analysis of it is by comparing collected data to that of past surveys.

This will allow you to see if this year’s performance was better or worse if any developing trends are apparent, and how any changes you made this year altered results from previous years.

Going back to our charity event example, if you ran a similar survey in years prior, start by comparing previous answers to current ones. These results will show how satisfaction has changed over the years.

Perhaps your satisfaction level increased 30 percent since last year, but people were 10 percent less satisfied with the dinner this year than two years ago

Comparing past surveys to current ones will give you an idea of trends that are underway. For example, perhaps doctors show a higher satisfaction every year for your charity event, but lawyers are showing a decrease.

Recognizing trends can help you pinpoint problems and use such information to make improvements in the future. You can also compare subgroup data to previous years to see subgroup trends and benchmarks.

Benchmark is one of the basic methods available to perform a successful statistical analysis of a survey. It is a point of reference in a survey, and therefore a vital part of statistical data analysis.

If you don’t have any prior surveys to compare results to, it’s important to start collecting feedback for all future events. This year’s survey will be your benchmark (i.e., starting point or baseline. You will then compare all future surveys against this survey to discover areas of improvement.

Collecting this type of data moving forward gives you longitudinal data analysis. Establishing a benchmark provides a way to make sense of data and work out what different percentages actually mean.

4. Look for Statistical Significance and Perform Statistical Analysis of Survey Data

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Your next tip for survey data analysis is to look for statistical significance. This is a way to determine if you can trust survey answers to help you confidently make future decisions. Looking at the quality of your data will let you know if you can rely on it for statistical significance.

First, determine whether your audience reflects the overall population of people you want to draw conclusions about.

Do they represent the population as a whole, or are they different from your ideal population subsection?

For example, if you want to analyze the opinions of men because they made up 90% of the event’s participants—while only 10% of the survey respondents were male—you should not feel confident in making decisions from such a small sample size.

When the sample size is too small, it can lead to a large margin of error—meaning the results won’t carry much weight. When there’s a high margin of error, you probably don’t want to use the data to inform future decisions.

Determining an average value

Second, if you ask your survey respondents questions with numerical answers, you can use these to determine the average, median, and mode. For example, let’s say one of your survey questions was: “How much did you donate?”

The average amount donated by each participant is the total number divided by the number of responses.

The median is the exact middle point, as if you laid the answers out in numerical order, with the median being the number in the middle.

The mode, on the other hand, shows you the most frequent response, the precise number respondents gave most often. For example, perhaps the majority of respondents donated $10.

Using these different calculations can help you find statistical significance in respondents’ answers. You can then use this information to make confident decisions for improvements or other changes.”

How to statistically analyze survey data?

We know that collecting data is only a first step. The answer to the question on how to execute the statistical analysis of questionnaire data may differ for each type of questionnaire. For example, you can summarize the Likert scale statistical analysis using a median or a mode. Meanwhile, using the mode is probably the most suitable for simple interpretation, but not necessarily the most precise.

There are many statistical techniques you can use when analyzing survey data. It will be necessary to familiarize yourself with statistical terminology and terms such as Independent and dependent variables, linear and multiple regression, etc. This will be further explained in a separate post that we plan for the future.

5. Review Open Text Responses to Learn More

If your survey, in addition to multiple-choice, included open text responses then it’s important to review these manually so you can compare them with other responses.
Open text responses will give you more insight as to why certain subgroups answered a certain way. When drawing conclusions, make sure to keep track of similar answers within each subgroup. This will help you back up your claims and gain more in-depth insight into each question and answer.

Open text responses are the best way to determine the correlation and causation of your other answers. Read our 11 Tips for Creating an Engaging Online Survey to avoid creating an uninteresting survey that can lead to a low response rate and inaccurate answers.

Causation is when one factor causes a direct change in another factor, while correlation is when two variables are different and move at the same time. It’s important to note that just because two variables move at the same time, this doesn’t mean they correlate with each other.

For example, in the winter heaters, blankets, and warm beverages increase in sales. Since winter products are variable dependent on the season, they correlate with each other. This example is not causation because winter products do not cause winter.

Look through the open text responses to find correlations and causations with other answers and draw conclusions

Create Your Own Survey

Now that you know how to complete survey data analysis using our 5 tips, why not create your own survey and start drawing the conclusions that will help you make better decisions?

Sign up for a SurveyPlanet account to gain access to dozens of pre-made questions, beautiful survey themes, and unlimited surveys and responses. Upgrade to a SurveyPlanet Pro account to get your hands on survey-result export tools, question branching, and many other unique tools to enhance your survey creation and analysis experience.

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