What Is Survey Data Processing?

You’ve conducted a survey, and now it’s time to present it. You can’t just leave the data as it is because you won’t be able to utilize the data to its maximum potential.

Then what should you do?

You should distribute and design survey data perfectly. Since it’s time-consuming, you should ensure you use the right tools and strategies.

If it’s used correctly, then the return on investment can be extremely high. It helps you get closer to achieving your business goals.

But is there any way to make the analysis effective and productive?

Yes. It’s Survey Data Processing.

It’s a crucial process that you should perform before any further analysis. In this guide, we’ll share what survey data processing is and why it is really important for your business. We’ll also share a step-by-step guide so you can get started today.

So keep reading.

What Is Survey Data Processing?

Survey data processing refers to a systematic method. It is the transformation of raw data collected from surveys into organized, structured, and analyzable information.

Not all survey responses need to be the same. Some might be in different formats, such as missing values, shorter responses, etc. So, if you process the survey data, you fix all the errors in formatting. It ensures that survey data is consistent.

Remember that survey data processing is not the same as data cleaning.

Data cleaning is a step of the data processing phase. However, processing data is not limited to data cleaning. It is a comprehensive process of converting raw data into useful information for business.

Step-by-step Guide For Survey Data Processing

Processing survey data can be an extremely tedious and complex task. But in this guide, we’ll describe how you can get started with survey data processing in simple steps.

Your Priority Should Be Quality Assurance

Many people make a huge mistake by skipping this step and doing it at the last stage. But that’s not good practice.

Quality assurance should be your topmost priority from the beginning.

Imagine you receive all the survey results. Now, you check these results, ensuring they are ready for the next stage.

Remember, the quality of results plays a great role in accurate and effective data processing. It gives you an idea about which changes you might need to make.

Follow a systematic approach to ensure you cover all the possible aspects of quality assurance. Here are some questions that will help you determine whether survey data is relevant.

• Are all respondents answering queries properly?
• Have you reviewed the incorrect information?
• Are there any mismatched data types?
• Is any data or survey column left empty by respondents?
• Are there any data outliers?

Start Data Cleaning Properly

In this step, you clean the data

When cleaning the data, you should focus on five aspects of survey data. It’ll help you in further steps of survey data processing.

How Much Time Is Spent Answering The Survey?

If respondents are quickly answering all the survey questions, then there is a high chance they aren’t interested in the survey. They are not fully engaged and might answer questions without reading them.

You should use survey tools that tell you the average time a respondent is spending on filling out the survey.

Now review all the responses; if any survey is completed in a suspiciously fast time, you might need to remove it.

Is There Any Duplicate Data?

You might find duplicate data (survey responses) for many reasons. The respondent might have clicked on submit more than once. There might be no internet connection, so the page didn’t load properly.

It’s easy to identify duplicate data when you filter all results. By removing them, you’re ensuring that there is no inaccurate data.

Are There Any Outliers?

Outliers are individuals who don’t align with the rest of the responses. Suppose you’re running a survey for a brand whose target audience is men. You get 13 responses; 12 survey respondents are male, but 1 is female.

Then, you should drop the female’s answers.

If you add your responses to a scatter plot, you can easily distinguish data outliers. The regression line indicates the connection between data. So, when you plot data, outliers can be detected.

Do you know how?

Because they are far away from the regression line.

Is Your Data Utterly Nonsense?

Just by the name, you can tell what nonsense data is. It’s the data that makes absolutely no sense as if someone’s just randomly punching the keyboard.

It can be something like: udueheh.

It’s due to errors from the respondent’s end. They might just want to proceed to page 2 of the survey, but completing page 1 is important. So, they randomly added words to skip to the next part of the survey and finished it quickly.

Is There Any Missing Data?

When a respondent is giving a survey and isn’t interested, they want to end it quickly. So, most often, they don’t even bother to give answers to certain questions and entirely skip them.

But the urge to quickly fill out the form isn’t the only reason behind it. There are many other reasons, such as:

• The survey is long.
• The survey has extremely difficult questions.
• The respondent genuinely didn’t want to answer the query.

But it’s not necessary to delete all the data collectively. Let us understand it with this example.

Suppose all the respondents have filled out the questionnaire. But there’s one respondent, let’s call her Ava. Ava forgot to add her email address and contact info. However, all the other answers are relevant to the survey.

Then, should you delete her responses?

No. Because her valuable insights on the survey can be helpful.

Meanwhile, Karen has only added her contact information, but her answers are irrelevant because of the missing data or incorrect responses. It is the type of data you should delete.

Are Responses Inconsistent?

It’s when the answers are contradictory. Suppose you ask someone which fruit they like. They reply it’s Apple. But the next day, you ask the same question, and then they reply Grapes.

There’s a huge contradiction in the responses, which can harm your survey data processing.

You should analyze answers and ensure they are trusted. Otherwise, you might need to delete them.

Focus On Data Transformation

Your data is almost ready, and you must make small tweaks to ensure it’s 100% ready for analysis.

But for this, you need to use techniques. Here are some techniques you should use.

• Aggregation
• Normalization
• Feature selection
• Discreditization
• Concept hierarchy

In aggregation, you combine all the collected data in an organized manner. With normalization, you can ensure your data has no duplicate answers. Thus leading to standardized data.

In feature selection, you identify highly crucial variables for your accurate analysis. These are later used to train ML models. With discreditization, you organize data into groups.

What does that mean?

Suppose your survey respondents’ ages vary between 16,20,49,60 years old. You can segment groups into 16-20 and 49-60 years old. Meanwhile, in concept hierarchy, you add hierarchy to the data.

Data Reduction For Accuracy

If you have a large amount of data, processing can be complex. Moreover, open-ended questions get irrelevant responses. You can remove these to extract only relevant insights.

You should reduce your survey data after considering the goals of the survey. If some fields or queries don’t fulfill survey goals, then you can eliminate them.

But it’s not necessary to remove data. If you think that data is essential, then you should keep it.

Why Is Survey Data Processing Important For Your Business?

You might be wondering if Survey Data Processing is really for my business.

The simple answer is yes. This process has many advantages, and every business should utilize it. Here are some key benefits of survey data processing.

• Data Accuracy: Identifies and rectifies errors, ensuring reliable and trustworthy information.
• Statistical Analysis: Enables quantitative analysis, revealing trends, patterns, and relationships.
• Non-Response Bias Mitigation: Addresses underrepresented groups, enhancing  representativeness.
• Data Visualization: Presents processed data through visualizations for easier comprehension.
• Insight Generation: Transforms raw survey responses into structured data for meaningful analysis.

Ending Thoughts

Survey data processing is a dynamic component of research and informed decision-making processes. Following this step-by-step guide, you can begin survey data processing today.

It helps researchers and organizations harness the full potential of survey data to inform strategies and drive positive outcomes.

But if your survey data is larger than 10,000, doing it manually will overwhelm you. So is there any solution for this?

To solve this problem, you should use tools and software to do this task within minutes. Luminoso uses advanced technology combined with ML and Artificial Intelligence, through which you can easily extract actionable insights.

It runs different text analysis techniques such as sentiment analysis and much more to better understand your audience and yield exceptional results from survey data.