It’s hard to obtain feedback for work, school, or personal projects. You need the right demographic and compelling survey questions to derive customer insights. As if that’s not enough, the distribution system you choose must share the survey at the right time.

Now, survey data doesn’t sort, analyze, or report itself. You need help sifting through the results to highlight trends and assess behaviors for marketing, sales, or reports. Turning survey results into a clear analysis isn’t always a straightforward task.

The good news is we’ve collected useful insights. They will help you analyze survey data and present your findings. But first, let’s start with the basics.

What is Survey Data Analysis?

Survey analysis turns raw data into answers. You can use them to improve your business, change perspectives, or prove a phenomenon. It’s also an essential part of survey-based research projects.

There are various data analysis methods available. You can arrange responses into rows and columns. Alternatively, you can discover things you could never work out with statistical methods.

Types of survey data

Survey questions warrant data in different forms. In case you may be wondering how to differentiate between qualitative and quantitative data, here’s a quick guide. While survey data often belong to more than one category, they frequently overlap.

  • Open-ended questions provide text boxes that encourage your respondents to write down their thoughts. Your questions should begin with “describe,” “in your opinion,” “how,” or other phrases.
  • Closed-ended questions limit your respondents’ answers. The structured data prevents them from explaining their answers. They can be drop-down menu items, multiple choice, or rankings.
  • Nominal data exists in categories with no hierarchical relationship. You can’t treat an item as more or less better or worse than the others. You would need multiple-choice questions to produce this type of data.
  • Ordinal data differs from nominal data because it has intrinsic ranks relating to quantity or quality. What does this mean for you? You may discover a respondent’s preference or how strongly they agree or disagree with a statement.
  • Scalar data establishes a scale of your respondents’ ages, test scores, or time. You may receive this data from a drop-down menu.

Why you need to Analyze Survey Data

As mentioned above, survey data exposes you to new information. And that’s just one side of the story. When you analyze survey data, you identify insights that may be overlooked.

You minimize errors, especially if your industry deals in statistics or customer data. Think about it, generating thorough reports come from well-organized and curated data.

You may also experience revenue improvements once you have actionable direction. The type of survey data you choose will shed light on customer or audience attitudes, beliefs, and opinions.

How to Analyze Survey Results

Here’s an overview of how you can analyze survey data and draw useful conclusions from your research.

First, you have to review your research questions. It helps you determine if they align with the goal you desire to accomplish. Secondly, understand the difference between ratio, interval, nominal, and ordinal measurement scales.

Once you break out your data according to sub-groups or titles within your population or sample, you can compare the relationship between variables. Simply put, data is relative. It’s, therefore, necessary to ensure your respondents represent your target audience accurately. When this is done, you consider correlation vs. causation or compare old and new data.

Presenting Survey Data Analysis

In my own experience, graphs or charts specially survey based Liker scale charts are easier on the eyes and easy to understand. However, you have to organize the data in digestible forms for unfamiliar stakeholders.

Creating a data table is a great way of sharing numerical data. With AI, SPSS, or Excel, you can display data after a cross-tabulation analysis.

One of the primary goals of survey data analysis is to weave information together. Here’s why. Data serves as the foundation of your story. Important insight ties back to the foundation to create structures, findings, and conclusions.

Compiling an infographic indeed makes a report easy to read. Making a visual representation with texts presents the early stages of a survey. It focuses on hypotheses, research or survey questions, and how to analyze survey data.

FAQs:

How do I avoid analyzing survey data mistakes?

Turns out that even if you’re an expert researcher, you can miss nuances from qualitative data. Human language and expressions are complex. Analyzing survey data from text to speech or vice versa needs you to factor in positive or negative codes. If you choose to invest in natural language data software, make sure it focuses on sentiment analysis.

How do I maintain data accuracy?

By now, you should know that it’s easy to get sucked in when your data shows what you were expecting. It’s important to use statistics to analyze survey data. Make sure your report is based on reality and not a coincidence.

How do I write a survey report?

Start by explaining the outcome in detail before writing your research summary. Once you have an outline, select your preferred layout. From there, explain your methodology and mention any limitations. You can add appendices if needed.

Wrap up

It’s one thing to collect data. Without a well-thought-out strategic analysis process, you will struggle to reach conclusions that justify your goals. The value of survey data analysis works on measuring results and improving what you offer.

Let me enlighten you on something. When you use AI for your surveys, you simplify the analysis process. The questionnaire is shorter without sacrificing invaluable insight. They also minimize your workload. Follow the tips in this post to craft analyses that support small and aspirational projects.