Online Survey Analysis Tool: Tips and Best Practices


Online Survey Analysis Tool: Tips and Best Practices Factor Analysis
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Data are an integral part of making informed decisions in a business. However, converting your survey data into comprehensive insights is not a simple task. An online survey analysis tool can help. 

A survey analysis tool is designed to allow you to collect and automate data analysis using statistical and qualitative analysis methods. It generates insights that are easy to comprehend and can be used to make informed business decisions. 

Online survey analysis tools automate data analysis which adds up to its extensive list of benefits such as increased accuracy, real-time result, data security, time-saving, and cost-effectiveness. 

In this blog, we’ll discuss how to effectively conduct data analysis to leverage the advantages of online survey analysis tools.

What is survey data analysis?

Survey data analysis refers to the process of analyzing data gathered from survey respondents. The process includes using data analysis software to run quantitative and qualitative data analysis based on the data type. The software converts a huge volume of data into comprehensive insights which you can use to inform decision-making. 

There are a number of survey data analysis methods available. For example, cross-tabulation or conjoint analysis are statistical methods to analyze large volumes of quantitative data. On the other hand, we have text and sentiment analysis that breaks down large volumes of textual data into a concise report. 

Before we jump into online survey data analysis tools, let’s understand the different types of data.

What are the two types of survey data?

There are two major categories of data – quantitative and qualitative. When you use closed-ended questions in your survey, you gather quantitative data. At the same time, an open-ended question brings you qualitative data. 

In a survey questionnaire, researchers often use a mix of both question types. For example, an NPS survey includes a rating question (closed-ended question type) and an open-ended question type. The open-ended question helps you gather context on the NPS score selected by the respondent. 

Let’s understand the two data types. 

Quantitative data:

The data you gather using closed-ended questions are numerical in nature. These numerical values are known as quantitative data. NPS, CSAT, and CES surveys are examples of quantitative data as you gather scores using these surveys. 

These are also called structured data that you can easily quantify and code. However, this also means you don’t gain contextual meaning or reasons behind the quantitative value. 

Qualitative data:

The response you receive written in the respondent’s own words is known as textual or qualitative data. Qualitative questions ask respondents to describe or share opinions on a topic. 

A follow-up question asking respondents to describe the reason for their NPS score is an example of a qualitative question.

What are the types of online survey analysis tools?

There are different types of survey analysis tools available to analyze the two types of data. Let’s look at the analysis tools that state-of-the-art survey software such as Voxco offers. 

1. Statistical analysis tools:

Statistical analysis functionalities analyze quantitative or numerical data. The tool uses statistical models and algorithms to find patterns and identify relationships between the variables.  There are many different statistical analytics types, such as cross tabs, factor analysis, linear regressions, and more. 

2. Text analysis tools:

This tool helps you make sense of qualitative or textual data. The tool uses NLP (natural language processing) algorithms to convert textual data into computer language, so it’s easy to identify patterns and themes and uncover hidden insights. 

Sentiment analysis is also a part of the text analysis tool. This tool enables you to identify the emotion behind a respondent’s feedback. 

3. Data visualization tools:

While this tool doesn’t help you analyze the data, it plays a critical role in understanding the data. This tool creates a visual representation of the findings using charts, graphs, and tables to help you understand the insights quickly. 

4. Survey reporting tools:

This online survey analysis tool helps generate customizable reports to provide an overview of the findings. The tool enables you to use interactive filters to create data views on data subsets and bring your data to life. This provides an easy view of the survey insights, which helps users make informed decisions. 

Depending on the need of your research, some tools may be more advantageous to you than others. Let’s say you gather a large amount of qualitative data; you will benefit from using text and sentiment analysis tools. In conclusion, the type of tool you need depends on the type of survey data you gather.

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Tips to effectively analyze survey data using online survey analysis tools

It is important to ensure that you effectively perform survey analysis to extract valuable insights from the survey data. Here are some steps to follow in order to conduct survey analysis productively. 

01. Structure survey questions for easy analysis:

When you design your surveys, make sure the questions are structured to help you gather precise data, which makes analysis easier. Here are some survey design tips on how you can structure the survey questions: 

  • Use closed-ended questions as it provides pre-defined response options which are easy to code and analyze. 
  • Make sure the response scale is consistent throughout the survey. If you use 0 for a negative response and 5 for a positive response, use the same order for other scale questions as well. 
  • Avoid double-barreled questions, as these can lead to unclear responses. 

02. Review your research questions:

Consider the data you have collected in the context of the research question. It is important to review your research question before you begin data analysis. This help you determine if the survey aligns with your purpose. Reviewing the main research question ensures that you have the data you need to make effective decisions. 

03. Clean your collected survey data:

Before you perform data analysis, clean and organize the survey data. Online survey analysis tools automate data cleaning to ensure consistency and accuracy in data. Here’s how it prepares your data for analysis: 

  • The tool removes duplicate responses that can skew the analysis. 
  • It removes invalid data, such as incomplete responses or those that contain nonsensical responses. 
  • It checks for data that falls outside the expected range of response. 
  • The tool then standardizes the data to ensure consistency across the survey data. 
  • Finally, it categorizes the data into relevant groups. For example, if the questions are related to the customer’s product experience, the tool will categorize data by product type. 

04. Use cross-tabs to look for patterns & trends:

Cross-tabulation is an important step in quantitative data analysis. It helps break down data into sub-groups and compare relationships between variables. 

For example, say you want to gauge customers’ satisfaction with different products from your company. You can cross-tabulate and show the satisfaction level for each product type. 

This helps you look for patterns and trends across survey data. You can perform cross-tab in your survey data in multiple ways with multiple sub-groups across different questions. 

05. Consider a qualitative analysis to uncover hidden intent:

Qualitative data can provide contextual insights into your quantitative data. Gather respondents’ unfiltered opinions and use text and sentiment analysis to break them down into meaningful insights. The online survey analysis tools can help map the words used by respondents to positive or negative sentiment. 

With text analysis, you can uncover hidden meanings and identify themes and intent. When you are looking for data analysis software that can analyze qualitative data, make sure it also offers sentiment analysis. 

06. Compare responses across different groups:

To make the most out of the survey data, compare responses across different groups of respondents. You can categorize them based on demographics, product purchase, or some other variable. This will help you identify patterns in behavior, attitude, preferences, and experience across different groups. 

With this insight, you can create targeted marketing and identify groups where tailored outreach efforts can boost conversion. 

Additional read: How to analyze survey data?

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In conclusion;

Online survey analysis tools can provide you with valuable insights that can lead to informed decision-making and drive business growth. The tools help you clean, organize, analyze, and report survey results. 

Survey data analysis is an essential part of research, especially in business. It helps you gain an insider’s view of customers and the market. The tools help you identify patterns, uncover hidden meanings, and discover ways to gain a competitive edge. If you haven’t already tried, we encourage you to try using an online survey analysis tool.


1. What is an online survey analysis tool?

It is software that helps you to analyze data gathered using surveys. The tool offers various data analysis models to analyze qualitative and quantitative data. It can help identify patterns, conduct a statistical and qualitative analysis, and create visual reports. 

2. What type of online survey analysis tools are available?

There are different types of online survey analysis tools available such as statistical analysis tools, text analysis tools, visualization tools, and reporting tools. It’s best to invest in survey software that gives you access to data analysis software with all these capabilities than investing in a separate analysis tool. 

3. How to effectively analyze and interpret survey data?

Here are some steps to follow to analyze and interpret survey data effectively: 

  • Structure survey questions for easy analysis. 
  • Review your research question.
  • Clean and organize data. 
  • Perform crosstabs.
  • Consider qualitative data analysis. 
  • Interpret data across different groups.

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