survey research statistical analysis

How to Analyze Survey Data: Best Practices

Analyzing survey data can be overwhelming, but with the right strategy, you can turn it into a goldmine of insights.  How to analyze survey data?

Our guide provides a step-by-step method to analyze your survey results, and reveal patterns, trends, and findings that inform smart decisions and strategies. 

Ready? Let’s jump right into the topic.

Got survey data, now what? How to analyze survey data

There are twelve steps, each with an actionable checklist, so you won’t miss anything.

Step 1: Define your objectives

Begin by asking, “What do I need to learn from this survey?” 

Your objectives should be clear, specific, and directly linked to what you want to understand. 

For example, if your survey is about customer satisfaction, your objective might be to identify the top factors that influence customer happiness.

Your objectives will shape the way you interpret the data. That’s why it is important. Survey goals determine the questions you ask and how you interpret the responses. Without clear objectives, you might end up with a lot of data but no real insight.

Instead of a broad objective like “understand customer behavior,” aim for something more targeted like “determine the main reasons customers return products.” 

The specificity guides you in selecting the right questions and analyzing the data accurately.

Your actionable checklist:

• Be clear about your survey’s primary purpose.
• Identify the metrics or feedback you want to get.
• Make your objectives align with broader organizational or project goals.
• Think about how the survey findings might affect decision-making.
• Try to be realistic about the insights you can get from the survey.

📚 Read also: Why are customer satisfaction surveys so important ?

Step 2: Clean your data

Here, you transform raw data into clean, usable information. 🙂

Eliminate errors and irrelevant data points from your survey results. So if you find responses that are incomplete or don’t fit with your survey’s purpose, it’s best to remove them.

Cleaning your data is a must for its accuracy. Can you think of analyzing data cluttered with mistakes or irrelevant information? A nightmare, isn’t it? 

Clean data makes you see what is really going on. Then, it’s so much easier to spot valid patterns and identify trends.

On top of that, having clean data simplifies the entire analysis process. The trends you identify are based on accurate and reliable information.

Actionable checklist:

• Remove incomplete or irrelevant responses
• Fix any errors in data entry
• Standardize response formats for consistency
• Check for and handle outliers
• Verify that all data points are correctly categorized

Step 3: Organize data structurally

It’s time to bring some clarity and structure to the collected data!

Arrange your data in a logical order. It might be sequencing responses according to the survey’s flow or grouping similar questions together.

Also, create a clear hierarchy in your data . Does your survey cover multiple topics? You can organize the responses under these topic headings. Navigating through the data is enjoyable, then.

Consider time-based organization if your survey captures data over different periods. Organizing data chronologically can make you spot changes over time, and provide actionable insights in your analysis.

Don’t forget about consistent formatting. It helps analyze data without confusion or errors, particularly when working with large datasets.

• Arrange data in the order of the survey’s questions.
• Group responses by topic for easy access.
• Organize data chronologically if relevant.
• Maintain consistent formatting throughout the dataset.
• Prepare a clean, organized dataset for further analysis steps.

Step 4: Quantitative and qualitative data segregation

You have two main types of data: quantitative and qualitative .

➡️ Quantitative data is numerical, like ratings or ages. 

➡️ Qualitative data includes written responses or opinions.

First, handle quantitative data. Organize this data into categories or groups. If your survey includes age groups, arrange the numerical data accordingly.

Then, focus on qualitative survey data from your survey respondents. Sort the responses into thematic groups. For instance, in a product feedback survey, you might categorize comments under ‘positive feedback,’ ‘negative feedback,’ and ‘suggestions.’

It’s a solid foundation for deeper analysis.

• Categorize numerical data into clear groups.
• Sort text responses into themes.
• Label each category for easy reference.
• Double-check for any misplaced data.
• Organize data in a way that aligns with your analysis goals.

Step 5: Analyze individual responses

The fifth step in your survey data analysis is to analyze open-ended responses and closed ones as well. Here is where the real gold lies in survey data. It’s a window into respondents’ thoughts and experiences.

Read every open-ended response carefully. Answers like these are rich with insights that simple yes/no answers can’t capture. You’re looking for patterns, repeated phrases, or unique points of view.

Next, summarize these insights . You might notice certain words or sentiments cropping up repeatedly. 

• Carefully read each open-ended response.
• Look for common themes or unique insights.
• Summarize key findings from these responses.
• Organize insights into clear categories.
• Use these insights to add depth to your overall survey analysis.

Step 6: Identify patterns and trends

When categorizing the responses and analyzing survey data, you might see trends and patterns among your survey participants. This step turns your data into a story. 

Look across all the responses and start noticing what stands out. Maybe a certain opinion is shared widely among a particular age group, or a specific issue keeps cropping up.

You’re combing through the details to find clues that piece together overarching ideas. These are your findings, the real gems hidden in the data.

It’s where numbers and words start forming clear messages. The trends are what turn your survey data from a collection of responses into valuable insights that can drive decisions and strategies.

• Review data for recurring themes.
• Note patterns that emerge across different groups.
• Analyze responses for common keywords or sentiments.
• Correlate findings with demographic information.
• Document these trends as key insights from your survey.

Source: Storyset

Step 7: Use cross-tabulation

It might be a powerful tool for understanding the relationships in your survey data. Cross-tabulation is comparing two or more variables to see how they interact with each other.

Select a few key demographic data points, like age, gender, or location. Then, pair them with different dependent variables from your survey responses. You might compare age groups with preferences for a particular service or product.

Cross-tabulation helps uncover tendencies that aren’t immediately obvious. It highlights how different demographic groups respond to specific aspects of your survey. 

Let’s say that you examine your data to see the finer details of how different segments of your audience think and feel.

• Choose key demographic data points for comparison.
• Pair these with various survey responses.
• Analyze the intersections to find unique patterns.
• Use these insights to understand how different groups respond.
• Apply this understanding to tailor strategies or products accordingly.

Step 8: Implement statistical analysis

Things can get a bit more technical when implementing statistical analysis. It makes you realize what’s statistically significant. You will see what findings are strong enough to rely on.

Once you find that many respondents prefer a particular product feature, statistical analysis helps you figure out if this preference is a real trend or just a coincidence.

Using statistical tools, you may test for statistical significance. You’ll see whether the results are likely to be true for a larger population, not just the people who took your survey.

Applying statistical analysis to survey data is a necessity. It gives weight to your findings and shows that the conclusions you draw are backed by solid evidence.

• Choose the right statistical methods for your data.
• Test for statistical significance in your findings.
• Interpret the results to see what’s genuinely significant.
• Use these insights to make data-driven decisions.
• Ensure your survey analysis is robust and reliable.

You can also use SurveyLab to get an intelligent analysis of your surveys. And it’s a super intuitive online software tool with plenty of survey templates.

Step 9: Create visual representations

It’s a great way to present survey data and results. When you’ve got a bunch of survey responses, turning them into visuals like pie charts can make the information way more digestible and interesting.

They can show your findings at a glance. A well-made pie chart may instantly convey how your survey respondents are split on a particular question. You take all numbers and responses and transform them into something that anyone can understand quickly.

• Choose the right type of chart for your data.
• Make sure your visuals are easy to read and understand.
• Use colors and labels to clarify your points.
• Keep your design simple and avoid clutter.
• Use these visuals to highlight the most important findings from your survey.

Step 10: Conduct a comparative analysis

It’s one of the survey data analysis methods where you take your current survey findings and compare them with past data, that’s a comparative analysis. It’s possible to spot changes, trends, or consistencies over time.

You look at the same data points across different periods or surveys. 

If your annual customer satisfaction survey shows a shift in opinions from last year, that’s something you want to look into. 

What changed? Why?  

These are the kinds of questions comparative analysis can help answer.

And it’s less likely to miss some tendencies when looking only at one set of survey results in isolation.

• Gather past survey data that are relevant to your current analysis.
• Identify the same data points or survey questions for comparison.
• Analyze any significant differences or similarities.
• Draw insights from how responses have changed or remained consistent.
• Use these insights to add a richer, more informed perspective to your survey report.

Step 11: Draw meaningful conclusions

When you reach the point of drawing conclusions in your survey analysis, you literally put the final pieces of a puzzle together. 

You’ve looked at all the numbers, seen what’s statistically significant, and now it’s time to step back and ask: “What does all this really mean?”

It’s the stage of interpreting the data collected. Think about how the significant trends you’ve identified tie back to your original goals. What story is behind the trends? How do they shed light on the questions you started with?

Conclusions bring closure to your survey analysis and tie your findings back to the real world, giving context and meaning.

• Look at your data in its entirety, considering the bigger picture.
• Focus on insights that are statistically significant.
• Link these insights back to the purpose of your survey.
• Craft conclusions that add depth and understanding to your findings.
• Ensure these conclusions resonate beyond just the numbers, touching on the broader implications of your research.

Step 12: Report findings and take action

After all your hard work analyzing the data, it’s time to put it to use. Create a survey report.

Highlight the most important pieces of information there: significant trends, notable customer feedback, or any surprising discoveries. 

The goal here is to present these findings in a way that’s clear and compelling. 

Your survey report should not only inform but also inspire your audience to make decisions or changes based on what the survey uncovered.

• Summarize the key findings clearly and concisely.
• Include relevant details from customer feedback.
• Make sure your report is easy to read and understand.
• Suggest actionable steps based on your analysis.
• Use the insights to drive meaningful changes or decisions.

How to design the survey so it gives you data that’s easier to analyze

Don’t work harder, work smarter. These tips will help you in data analysis. Maybe here’s a piece of advice that you always overlook, and it may change the way you handle your data for good.

Keep questions clear and concise 

Concise questions prevent respondent fatigue. Long or complex questions can confuse or frustrate people , and it leads to rushed or careless responses, which in turn can muddy your survey analysis.

The goal of each question is to be as clear as possible about what you’re asking. Avoid jargon, double-barreled questions, and overly technical language that might confuse respondents. Each question should focus on one specific topic or idea to avoid ambiguity.

Use a logical flow 

Logical flow is essential for gathering data that’s easy to analyze. Start with broad, general questions and then gradually narrow down to specifics . Respondents may be more comfortable and willing to provide detailed answers further on.

Grouping similar topics together also helps. Once respondents deal with one subject at a time, their answers tend to be more focused and consistent.

You can quickly catch patterns without having to sift through a jumble of unrelated responses.

Limit open-ended questions

They can provide rich qualitative data, but they are harder to analyze in bulk. Use them sparingly and, where possible, change them into closed-ended questions. But be careful, open-ended ones may bring more insights, so think twice before replacing them.

With limited open-ended questions , the tracking data process will be less painful, and the data analysis won’t take long. 

Employ consistent rating scales 

Use uniform scales for rating questions (e.g., 1-5 or 1-10). Consistency in scales across questions makes comparative analysis more straightforward.

On top of that, employing consistent rating scales , like interval scales or ratio scales , makes it easier to track responses and draw conclusions.

Interval scales measure the difference between responses and are ideal for questions with equidistant responses. For instance, a scale from 1 to 5 measuring satisfaction levels, where each step up represents an equal increase in satisfaction.

Ratio scales, on the other hand, not only show the differences between responses but also have a true zero point. Could be useful in questions about frequency or quantity, where ‘0’ indicates ‘none’ or ‘never.’

📚 Read also:  Understanding People’s Opinions with Likert Scale Examples .

Include demographic questions

Demographic questions (age, gender, location, etc.) are imperative for segmenting and give you a broader context for your research. Include them at the beginning of the survey, but remember that gender-related questions might be sensitive for some. Make sure there’s an “I don’t want to answer this question” option.

Pre-test the survey 

Conduct a pilot test of your survey with a small audience before full deployment. This helps in identifying and rectifying any confusing or misleading questions.

Avoid leading or biased questions

Ensure that the questions are neutral and do not lead the respondent towards a particular answer. Biased questions can skew your data and compromise the integrity of your analysis.

Opt for multiple-choice where possible 

Multiple-choice questions are easier to analyze than narrative responses. They provide structured data that can be easily quantified and compared.

Key takeaways

• Set clear goals for your survey to understand what data you need to collect.
• Clean your data – remove errors and irrelevant responses for smoother analysis.
• Organize your survey data well to make it easier to analyze and understand.
• Separate your data into numbers (quantitative) and words (qualitative) for a detailed study.
• Use different methods like charts and comparisons to find trends and draw conclusions.

Data analysis: it’s the primary step in turning your survey responses into clear, actionable insights.

If you want to make the process easier, consider using SurveyLab. It’s user-friendly and helps you get the most out of your surveys. 

Check out SurveyLab for your next project and see the difference it makes. Sign in today !

FAQ on How to Analyze Survey Data

Do you have any questions? Maybe we have already answered it. 

How do you analyze data from a survey?

Categorize and interpret the responses. First, sort the survey data into qualitative and quantitative types. Use data analysis methods to catch key trends and insights. Employ statistical analysis techniques to find statistically significant patterns. Always align your analysis with the original research questions and objectives.

Which method is used to analyze survey results?

Researchers usually combine data analysis methods. They use statistical analysis to understand trends and significance and qualitative methods for open-ended responses. Cross-tabulation is often applied for comparing different data sets, while regression analysis can help understand relationships between variables.

What is the best tool to analyze survey data?

Surveylab offers intelligent analysis , and you can use it for both analysis and survey creation. 

The tool generates survey reports automatically as soon as the first responses are collected. There are useful filters to find all the info you need in seconds. On top of that, exporting the results takes a few clicks.

The tool also provides plenty of survey templates that are customizable, so you don’t have to build a questionnaire from scratch (but you can if you feel like it).

What is the survey data analysis?

It relies on interpreting responses to structured questions using both qualitative and quantitative approaches. Excellent for extracting customer insights, demographic data and determining statistical significance that provides a more accurate picture of your survey results.

How do you manually analyze questionnaire data?

It’s organizing and interpreting responses. For quantitative data, consider calculating numerical trends. For qualitative data, look for themes in open-ended responses. Summarize these findings to answer your research question with clear, actionable insights.

How do you analyze survey data qualitatively?

Qualitative analysis of survey data uses narrative and open-ended responses. Look for common themes and insights that provide depth beyond numerical data. 

How to conduct a quantitative survey analysis?

Focus on statistical analysis of numerical data. Identify trends, calculate statistical significance, and use tools like regression analysis to understand variable relationships. The quantitative method is suited for structured surveys, where each response contributes to statistically significant findings.

Try SurveyLab for free Best survey tool with great features

14 days trial | view complete list of features

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case AskWhy Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Surveys

Survey Analysis: What is, How to Do It + How to Present It?

Surveys are powerful tools for gathering information, but the real magic happens when you analyze and present the data collected. Survey analysis is the bridge between data collection and informed decision-making. You can turn survey data into actionable insights by approaching it systematically and presenting findings thoughtfully.

In this blog, we’ll break down the process of survey analysis into simple steps and explore effective ways to present your findings.

What is survey analysis?

Survey analysis is the process of examining and interpreting data collected through surveys to derive meaningful insights, draw conclusions, and make informed decisions. 

Surveys are a common research method used in various fields, including social sciences, marketing, public opinion research, and business. The analysis of survey data involves organizing, summarizing, and interpreting the responses obtained from survey participants.

Survey analysis is crucial for extracting valuable information from large sets of data, enabling researchers and decision-makers to make data-driven decisions. The choice of analysis methods depends on the type of survey, the research goals, and the nature of the data collected. 

Quantitative surveys typically involve numerical data, while qualitative surveys may include open-ended responses that require more interpretive analysis.

Importance of Survey Analysis in Your Business

Survey analysis plays a crucial role in business for various reasons. It provides valuable insights that can inform decision-making, improve products or services, enhance customer satisfaction, and contribute to overall business success. Here are several reasons highlighting the importance of survey analysis in a business context:

Customer Feedback and Satisfaction

Surveys allow businesses to gather feedback directly from customers. Analyzing this feedback helps understand customer satisfaction levels, identify improvement areas, and tailor products or services to meet customer expectations.

• Market Research

Survey analysis is a fundamental component of market research. Businesses can use surveys to gather information about market trends, customer preferences, and competitive landscapes. This knowledge is crucial for strategic planning, product development, and market competitiveness.

Product and Service Improvement

Businesses can identify trends and specific aspects of their products or services that need improvement by analyzing survey data. This could include features customers desire, areas of dissatisfaction, or suggestions for enhancements. Continuous improvement based on survey insights can lead to increased customer loyalty.

Employee Engagement and Satisfaction

Surveys are valuable tools for assessing employee satisfaction, engagement, and overall workplace experiences. Analyzing employee feedback helps businesses identify areas for improvement, address concerns, and create a positive work environment, leading to increased productivity and employee retention.

Strategic Decision-Making

Survey analysis provides data-driven insights that support strategic decision-making. Whether it’s entering new markets, launching new products, or refining business processes, survey results can guide executives in making informed and strategic choices.

Customer Loyalty and Retention

Understanding the factors that contribute to customer loyalty is critical for businesses. Survey analysis helps identify the key drivers of customer loyalty, enabling businesses to implement strategies to retain existing customers and foster long-term relationships.

Competitive Analysis

Surveys provide insights into how a business compares to its competitors. Analyzing survey data on customer preferences, satisfaction, and perceptions helps businesses benchmark themselves against industry standards and identify areas where they can differentiate and excel.

Types of Survey Analysis

Surveys serve as invaluable tools for gathering information, opinions, and feedback from diverse audiences. However, the real power lies in the data collection and the meticulous analysis that follows. In survey analysis, two main approaches are:

• Quantitative (statistical) analysis and 
• Qualitative analysis. 

Let’s delve into the different types within these two broader categories.

Quantitative Survey Analysis

Quantitative survey analysis is the preferred method when dealing with closed-ended questions that result in numerical data. This strategy uses a variety of statistical tools to extract significant information. Let’s look at five main statistical survey analysis methods.

• Regression Analysis: Understanding the dynamics between variables is crucial for making informed decisions in various fields such as business, economics, and social sciences. Regression analysis serves as a powerful statistical method designed to illuminate these intricate relationships. The primary purpose is to identify how changes in one variable may impact another.
• ANOVA Test: Analysis of Variance (ANOVA) is a statistical method employed to investigate whether there are any statistically significant differences between the means of three or more independent groups. Its primary purpose is to explore variations between these groups and determine if they are more than one would expect due to random chance.
• Conjoint Analysis: Conjoint Analysis is a powerful market research technique designed to unravel the preferences and priorities of customers when evaluating different product or service features. Its primary purpose is to understand how individuals make trade-offs between various attributes, aiding businesses in strategic decision-making.
• T-Test: The T-Test is like a statistical detective that helps us figure out if the difference between two groups is real or just due to chance. Its main job is to tell us whether what we see in our data is significant or could happen randomly.
• Crosstab Analysis: Crosstab Analysis, or Cross-Tabulations, is like a handy magnifying glass for data. It helps us see if relationships or patterns are hiding in our information, especially when dealing with categories. The main goal is to uncover connections between different variables.

Qualitative Analysis

Understanding what customers really think and feel goes beyond just looking at numbers. Qualitative Analysis uses special survey data analysis methods, like Text and Sentiment Analysis, to dig into the words customers use when they share their thoughts. 

It’s like being a detective, trying to uncover hidden meanings in what people say. Let’s take a closer look at Text Analysis and Sentiment Analysis to see how they help us make sense of all those words.

• Text Analysis: Text Analysis is like having a special decoder for customers’ words in open-ended responses. Its main goal is to break down and make sense of all the text, turning it from a sea of words into valuable insights. The process starts by cleaning up the data and removing unnecessary stuff like extra punctuation or common words.

Once the data is tidy, smart computer algorithms come into play. These algorithms are like super detectives that can spot patterns in the text. They help us figure out what customers are talking about, whether it’s their likes, dislikes, or specific challenges.

• Sentiment Analysis: Sentiment Analysis is like figuring out the emotions behind what customers write. It helps us know if their words are happy, sad, or just neutral, giving us a deeper understanding of their experiences. Using special computer tricks, Sentiment Analysis reads the emotions in the text. 

It can tell if customers are excited about something, upset about a problem, or just sharing facts. This helps businesses see beyond the numbers and really understand how customers feel.

Steps to Follow in Your Survey Analysis

Surveys are powerful tools for gathering information and insights from a target audience. However, the true value lies in the effective analysis of survey data. The process may seem daunting, but breaking it down into simple steps can make it more manageable. Here, we’ll guide you through survey analysis, ensuring that you extract meaningful and actionable results.

Step 1: Define Your Objectives

Before diving into survey data analysis, clearly outline your objectives. What specific information are you seeking? Understanding your goals will help you focus on relevant data during the analysis and draw meaningful conclusions.

Step 2: Organize and Clean Your Data

Start by organizing your survey data in a spreadsheet or a statistical software tool. Remove any duplicate or irrelevant survey responses. Check for missing values and decide on a strategy for handling them – whether it’s imputation or excluding incomplete entries. Clean data ensures accurate and reliable analysis.

Step 3: Quantitative vs. Qualitative Data

You need to differentiate between quantitative and qualitative data. Quantitative data involves numerical information, while qualitative data is descriptive. Use different analysis techniques for each type. You might employ statistical methods for quantitative data, while qualitative data may require thematic analysis or coding.

Step 4: Descriptive Statistics

Begin with descriptive statistics to summarize and describe the main features of your data. This includes mean, median, mode, range, and standard deviation measures. Descriptive statistics provide a snapshot of your data’s central tendencies and variations.

Step 5: Visualize Your Data

Create visual representations of your data using charts, graphs, and tables. Visualization helps in understanding patterns, trends, and outliers. Common tools for visualization include bar charts, pie charts, histograms, and scatter plots. Choose the most appropriate visualization method based on your data and objectives.

Step 6: Analyze Subgroups

If applicable, analyze survey data based on different subgroups. This could involve comparing responses across demographics, regions, or any other relevant categorization. Understanding variations within subgroups can unveil valuable insights that might be hidden in overall analyses. 

Step 7: Correlation and Regression Analysis

Explore relationships between different survey variables through correlation and regression analysis. Correlation identifies the strength and direction of relationships, while regression helps predict one variable based on another. These analyses are crucial for understanding the factors influencing certain outcomes.

Step 8: Interpret the Findings

Once you’ve completed your analyses, interpret the findings in the context of your objectives. Clearly articulate what the data is telling you and how it aligns with your initial goals. Be cautious not to overinterpret or draw conclusions unsupported by the data.

Step 9: Communicate Results Effectively

Share your findings in a clear and concise manner. Use visuals, summaries, and key insights to communicate the results to stakeholders or your target audience. A well-structured report or presentation enhances the impact of your survey analysis.

How to Present Survey Insight?

Effectively presenting survey insights is crucial for ensuring that the findings are communicated clearly and resonate with the audience. Here are some key strategies on how to present survey insights:

Use a Graph or Chart

Analyze survey results and use graphs or charts to present them. Graphs and charts visually communicate survey data in an easily digestible manner. Choose a graph type that suits your data and ensures clarity. 

For example, a stacked bar graph might be confusing, so opt for a simpler design like individual bars with a clear key. Consider pie charts, Venn diagrams, line graphs, scatter plots, histograms, or pictograms, depending on the nature of your survey.

Create a Data Table

Tables are effective for presenting numerical data in a structured format. Use software like SPSS or Excel to create a data table focusing on key information. Remove unnecessary details and highlight the percentages or relevant figures. This helps stakeholders quickly grasp the essential findings.

Tell a Story with Data Analysis

Transform your data into a narrative that builds upon itself. Start with foundational data, present key findings as the supporting structure, and conclude with the primary point you want to make. Storytelling helps your audience, regardless of their analytical savviness, understand the context and statistical significance of your survey results.

Make a Visual Presentation

Combine visuals with text to create a comprehensive presentation. Include research questions, hypotheses, survey questions, and methods of analysis. This approach serves different learning styles, appealing to those who prefer visuals, numbers, or words.

Put Together an Infographic

Infographics are ideal for presenting data that needs to be quickly consumed. Create visually appealing and concise graphics that convey key survey insights. Infographics are particularly effective for summarizing complex information engagingly and memorably.

How QuestionPro Help in Survey Analysis and Presenting Insight?

QuestionPro is a user-friendly survey and research platform that not only simplifies the survey creation process but also empowers users with robust tools for analyzing and presenting insights. Let’s explore how QuestionPro makes the journey from data collection to actionable insights a seamless experience.

How QuestionPro Supports Survey Analysis

• Easy Survey Creation: QuestionPro provides a user-friendly interface for creating surveys. From multiple-choice to open-ended questions, the platform accommodates various question types, making survey creation a straightforward process for users of all experience levels.
• Diverse Data Collection: Whether you prefer online surveys, mobile surveys, or email surveys, QuestionPro offers diverse data collection methods . This flexibility ensures that you can connect with your target audience through channels that suit them best.
• Real-Time Analytics: Staying in the loop is crucial during the survey process. QuestionPro offers real-time analytics, allowing you to monitor responses as they come in. This feature enables you to adapt and optimize your survey on the fly.
• Advanced Survey Logic: Irrelevant questions can damage your data. QuestionPro includes advanced survey logic features, ensuring that survey respondents only encounter questions relevant to their previous answers. This not only improves the quality of your data but also enhances the respondent experience.
• Robust Data Analysis Tools: QuestionPro provides a number of tools for analyzing survey data. From filters to cross-tabulations, these tools empower you to explore data comprehensively. It uncovers patterns and correlations that may inform strategic decisions. Here’s a breakdown of the robust data analysis tools offered by QuestionPro:
• Cross-Tabulations: Uncover patterns and correlations within the survey data.
• Trend Analysis: Compare responses across different time periods for insightful analysis.
• Statistical Analysis: Understand central tendencies and variabilities in the data.
• Text Analytics: Gain qualitative insights from respondents’ text data.

How QuestionPro Helps in Presenting Insights

• Customizable Reporting: QuestionPro offers customizable reporting options once the analysis is complete. Tailor your survey report with charts, graphs, and visual elements to effectively convey your findings to different stakeholders.
• Visualization Tools: Transforming raw data into visual representations enhances comprehension. QuestionPro’s visualization tools, including charts and dashboards, make communicating key insights visually and engagingly easy.
• Seamless Integration and Export: QuestionPro integrates seamlessly with various tools and platforms, streamlining your workflow. Export survey data in different formats (e.g., Excel, SPSS) for further analysis or sharing with external stakeholders.
• Collaboration and Sharing: Collaboration is simplified with QuestionPro. Share survey projects and results with team members, facilitating collective decision-making. Customizable sharing options ensure that information is disseminated efficiently.

Final Words

By understanding what survey analysis is, learning how to conduct it effectively, and exploring ways to present your findings, you can ensure that your survey not only collects valuable information but also communicates it clearly and effectively. Whether you are a student, business professional, or researcher, mastering survey analysis can significantly enhance your ability to leverage the power of data.

QuestionPro, a user-friendly survey and research platform, simplifies the entire survey process. From easy survey creation to diverse data collection methods, real-time analytics, advanced survey logic, and robust data analysis tools, QuestionPro provides a seamless experience.

So, uncover the stories hidden in your data with QuestionPro, make informed decisions, and let your surveys be the catalysts for positive change.

LEARN MORE         SIGN UP FREE

MORE LIKE THIS

Life@QuestionPro: Thomas Maiwald-Immer’s Experience

Aug 9, 2024

Top 13 Reporting Tools to Transform Your Data Insights & More

Aug 8, 2024

Employee Satisfaction: How to Boost Your  Workplace Happiness?

Aug 7, 2024

Jotform vs Formstack: Which Form Builder Should You Choose?

Other categories.

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Tuesday CX Thoughts (TCXT)
• Uncategorized
• What’s Coming Up
• Workforce Intelligence

• Voxco Online
• Voxco Panel Management
• Voxco Panel Portal
• Voxco Audience
• Voxco Mobile Offline
• Voxco Dialer Cloud
• Voxco Dialer On-premise
• Voxco TCPA Connect
• Voxco Analytics
• Voxco Text & Sentiment Analysis

• 40+ question types
• Drag-and-drop interface
• Skip logic and branching
• Multi-lingual survey
• Text piping
• Question library
• CSS customization
• White-label surveys
• Customizable ‘Thank You’ page
• Customizable survey theme
• Reminder send-outs
• Survey rewards
• Social media
• Website surveys
• Correlation analysis
• Cross-tabulation analysis
• Trend analysis
• Real-time dashboard
• Customizable report
• Email address validation
• Recaptcha validation
• SSL security

Take a peek at our powerful survey features to design surveys that scale discoveries.

Download feature sheet.

• Hospitality
• Academic Research
• Customer Experience
• Employee Experience
• Product Experience
• Market Research
• Social Research
• Data Analysis

Explore Voxco 

Need to map Voxco’s features & offerings? We can help!

Watch a Demo 

Download Brochures 

Get a Quote

• NPS Calculator
• CES Calculator
• A/B Testing Calculator
• Margin of Error Calculator
• Sample Size Calculator
• CX Strategy & Management Hub
• Market Research Hub
• Patient Experience Hub
• Employee Experience Hub
• NPS Knowledge Hub
• Market Research Guide
• Customer Experience Guide
• Survey Research Guides
• Survey Template Library
• Webinars and Events
• Feature Sheets
• Try a sample survey
• Professional Services

Get exclusive insights into research trends and best practices from top experts! Access Voxco’s ‘State of Research Report 2024 edition’ .

We’ve been avid users of the Voxco platform now for over 20 years. It gives us the flexibility to routinely enhance our survey toolkit and provides our clients with a more robust dataset and story to tell their clients.

VP Innovation & Strategic Partnerships, The Logit Group

• Client Stories
• Voxco Reviews
• Why Voxco Research?
• Careers at Voxco
• Vulnerabilities and Ethical Hacking

Explore Regional Offices

• Survey Software The world’s leading omnichannel survey software
• Online Survey Tools Create sophisticated surveys with ease.
• Mobile Offline Conduct efficient field surveys.

Text Analysis

• Close The Loop
• Automated Translations
• NPS Dashboard
• CATI Manage high volume phone surveys efficiently
• Cloud/On-premise Dialer TCPA compliant Cloud on-premise dialer
• IVR Survey Software Boost productivity with automated call workflows.
• Analytics Analyze survey data with visual dashboards
• Panel Manager Nurture a loyal community of respondents.
• Survey Portal Best-in-class user friendly survey portal.
• Voxco Audience Conduct targeted sample research in hours.
• Predictive Analytics
• Customer 360
• Customer Loyalty
• Fraud & Risk Management
• AI/ML Enablement Services
• Credit Underwriting

Find the best survey software for you! (Along with a checklist to compare platforms)

Get Buyer’s Guide

• 100+ question types
• SMS surveys
• Financial Services
• Banking & Financial Services
• Retail Solution
• Risk Management
• Customer Lifecycle Solutions
• Net Promoter Score
• Customer Behaviour Analytics
• Customer Segmentation
• Data Unification

Explore Voxco 

Watch a Demo 

Download Brochures 

• CX Strategy & Management Hub
• The Voxco Guide to Customer Experience
• Professional services
• Blogs & White papers
• Case Studies

Find the best customer experience platform

Uncover customer pain points, analyze feedback and run successful CX programs with the best CX platform for your team.

Get the Guide Now

VP Innovation & Strategic Partnerships, The Logit Group

• Why Voxco Intelligence?
• Our clients
• Client stories
• Featuresheets

Unveiling the Power of Survey Analysis: Techniques and Insights

• June 1, 2021

Surveys are popular tools used in market research to gather data from the target population. The data is used to gain valuable insights to make informed business decisions. However, simply collecting data won’t help you make effective decisions. 

To make sense of the collected survey data and use it at its full potential, it’s important to perform survey data analysis. Survey data analysis software makes it easy to interpret data and identify patterns and trends.

In this blog, we will look closely at the various types of survey data analysis methods.

What is Survey Analysis?

Survey analysis, as the name suggests, refers to the process of analyzing survey results from customers, employees, or others. It is used to draw conclusions from the data gathered from surveys. 

Leveraging survey data analysis software is a cost-effective method to study customer preferences and behavior. It helps you automatically clean, weigh, and analyze millions of data in no time.

Statistical survey analysis is important as it allows you to draw inferences and generalizations of your whole target audience through the sampling of a small subset of this population. 

Responses and statistical information alone do not benefit a company. It’s the inferences, trends, and patterns that they identify within these responses that help them make better decisions.

What Are the Different Types of Survey Analysis Methods?

There are two types of survey analysis methods: 

• Quantitative or Statistical analysis 
• Qualitative analysis. 

The types of survey analysis methods depend on the type of data collected.

When we use closed-ended questions to gather numerical or quantitative data, we use quantitative analysis. It includes various statistical methods like ANOVA, T-test, regression analysis, and cross-tabs. 

On the other hand, when we use open-ended questions and gather textual feedback, we use qualitative analysis methods. This helps us break down the textual feedback into charts and graphs so that it’s easy to identify hidden patterns. 

Both types of survey analysis methods are important for gaining a comprehensive understanding of the survey data.

See what our clients say about Voxco

See what question types are possible with a sample survey!

5 Types of Statistical Survey Analysis Methods

We have explained five statistical methods you can use to analyze survey data. The following are some popular types of statistical survey analysis methods:

Regression Analysis

Conjoint analysis, crosstab analysis.

Regression analysis includes a set of statistical methods employed in the estimation of the relationships between a dependent variable with one or more independent variable(s). This method of analysis mathematically sorts out which variables have the most impact and the way these variables interact with each other. It is a useful survey analysis method when trying to define the impact of a change in your independent variable. 

Regression analysis is conducted by gathering data on the variables in question. Then, this information is plotted on a graph/chart. Graphs resulting from regression analysis depict a regression curve that reflects the relationship between the variables. 

ANOVA test, or analysis of variance test, is used in amalgamation with regression studies in order to identify the effect of independent variables on dependent variables. It can compare a range of variable groups simultaneously to see if there are any corrections between them.

There are two main types of ANOVA tests:

• One-way ANOVA: Compares means of two or more independent variables to determine whether there is statistical evidence that their associated population means are different.
• Two-way ANOVA: It is an extension of the one-way ANOVA test, and it examines the influence of two different independent variables on one continuous dependent variable. 

There are also variations of the ANOVA test, such as MANOVA. MANOVA, or multivariate analysis of variance, is used to compare multivariate sample means. It helps determine differences between independent groups on more than one continuous dependent variable.

Conjoint analysis is used in market research to determine how people value different attributes of a product or service. This helps organizations understand customer preferences and cater to their needs and wants more effectively. 

This analysis of survey data is a statistical technique that is conducted by breaking a product/service down to its attributes or features. It tests different combinations of these attributes to identify consumer preferences. 

Survey results can then be used to calculate “preference scores.” This score is a numerical value that measures how much each attribute affected the respondent’s choices to purchase the product. 

These preference scores are used to build simulators that can forecast the market share for a set of different products offered to the market. This simulator can model respondent decisions and is used to identify specific features and pricing levels that help forecast potential demand in the competitive market.

T-Test is a statistical test used to compare the mean of two groups of variables. It is usually used when the data sets come from the same population, follow a normal distribution, and may have unknown variances. It is used as a hypothesis-testing tool, which means it allows the testing of assumptions made about certain populations. 

When T-Test is used, the following assumptions are made about the data:

• The scale of measurement applied to the data collected follows a continuous or ordinal scale.
• The data collected is from randomly selected units of the population. It is representative of the total population, as in simple random sampling.
• When the data is plotted, it results in a normal distribution, bell-shaped curve.
• Equal, or homogeneous, variance exists when the standard deviations of the samples are (approximately) equal.

Crosstab analysis, or cross-tabulations, involves the use of data tables that display the results of survey respondents. It’s a survey data analysis tool used for categorical data. It enables the examination of relationships that may not be apparent when analyzing survey responses. Categorical data refers to values that are mutually exclusive of each other. 

Crosstab analysis helps organizations make informed decisions by identifying patterns, correlations, and trends between the study’s parameters. 

Cross tabs help identify relationships between variables. For example, in market research, cross-tab can help identify demographic groups that are likely to demonstrate certain purchase behavior.

Survey organizations around the world have maximized their phone survey ROI with the advanced features, hosting options, seamless telephony integration, and flexible pricing of our CATI software.

2 Types of Qualitative Survey Analysis Methods

Here are two popular survey data analysis tools for qualitative data. Let’s take a look at how these data analysis method helps you make sense of customers’ feedback and uncover insightful data. 

Sentiment Analysis

Text analysis helps you analyze unstructured survey data. It helps you break down textual feedback and identify patterns, themes, and insights in minutes. Text analysis software cleans the data of irrelevant information, such as punctuations or stops words. It then uses machine learning to interpret and analyze data to identify patterns. 

Text analysis enables you to uncover hidden insights and trends in large volumes of textual data. This is particularly beneficial for businesses, as it helps you better to understand customers’ opinions, preferences, pain points, and needs. 

Sentiment analysis is a specific sub-category of text analysis. It focuses on identifying sentiments expressed by respondents in their textual feedback. Sentiment analysis tool uses machine learning to label data into positive, negative, and neutral text based on the language used.

It helps you understand how your customers feel about the brand and its offerings. This type of survey data analysis tool enables you to gather customers’ honest opinions in their own words. Thus, allowing you to gain insight into their perception and identify drivers of customer experience.

[Free Webinar Recording]

Want to know how to increase your survey response rates.

Learn how to meet respondents where they are, drive survey completion while offering a seamless experience, Every Time!

What is software for survey data analysis?

Software for survey data analysis is a type of tool that allows researchers to organize, manage, analyze, and visualize survey data. This software is designed to help researchers make sense of large amounts of survey data quickly and efficiently.

Survey data analysis software, if used efficiently, can be a valuable tool for researchers to analyze survey data and gain insights that can inform decision-making in various fields, including business, healthcare, education, and social sciences.

With survey data analysis software, researchers can create graphs, charts, tables, and other visual representations of survey data to identify patterns, trends, and relationships. They can also use statistical tests to determine if the survey data is significant and draw conclusions based on the findings.

What features should a survey data analysis software have?

When looking for the best survey data analysis software, you should look for the following features:

• Data importing: The software should allow you to import survey data from a variety of sources, including online surveys, mobile-offline surveys, and other data files.
• Data cleaning and validation: The survey data analysis tool should provide the functionality to clean and validate the data. The feature should check for missing values, outliers, and inconsistent responses.
• Data manipulation: The software for survey data analysis should allow you to manipulate the data, including transforming variables, merging datasets, and creating new variables.
• Statistical analysis: The software should provide a range of statistical analyses, including t-tests, ANOVA, regression, chi-square, and cross-tabulations.
• Qualitative analysis: The software should allow you to conduct text analysis and sentiment analysis on a large volume of data. 
• Visualization: It should provide a range of visualization tools, such as tables, graphs, charts, and maps, to help you present your findings.
• Reporting: You should be able to create reports and presentations based on your analysis and survey insights. It should allow you to export reports in multiple formats. 
• Dashboard: The software should allow you to share role-based access with other team members and share data and analysis. So you can work together to create reports and presentations.
• Security and privacy: It should provide secure data storage and privacy protection to ensure that your data is safe.
• User-friendly interface: It should have a user-friendly interface that is easy to navigate. It should be easy for non-technical people to conduct analysis.

Switch to Voxco Telephony System to improve your Call Center Productivity by 400% or more 

How to choose the best software for survey data analysis?

Choosing the best software for survey data analysis depends on several factors, such as the type of data you have, the level of analysis you require, and your personal preferences. 

Here we’ve listed some of the best tips to choose the best software for survey data analysis: 

1. Identify your research needs

Consider what type of analysis you need to conduct with your survey data. Do you need to perform basic descriptive statistics, or do you require more advanced statistical analysis? Knowing your research needs will help you choose software that has the features and tools that meet your requirements.

2. Mind your budget

Some survey data analysis software can be expensive, while others are free or open source. Consider your budget when selecting software and decide how much you are willing to spend.

3. Check the software’s compatibility

Make sure that the software you select is compatible with the operating system and hardware you are using. Some software may require specific system requirements, so ensure that your computer can run the software effectively.

4. Assess user-friendliness

Look for software that is user-friendly and easy to learn. Consider the level of technical expertise you have and how quickly you need to learn the software.

5. Read user reviews  

Look for reviews and ratings of different survey data analysis software to get an idea of what other users think about the software. Reviews can help you determine the strengths and weaknesses of different software options.

6. Take a free trial before you buy

Many software providers offer free trial periods, so take advantage of this and test out different software options to see which one works best for you.

In conclusion, survey analysis is critical in research. Be it statistical analysis or qualitative analysis, it helps you uncover hidden patterns in data. It is important to understand your research goal and select the appropriate analysis method. Additionally, it’s important to ensure that the data is properly collected, centralized, cleaned, and processed before you can analyze it. 

Survey data analysis can be time-consuming and complex. It’s important to leverage the best survey data analysis software to ensure you can automate the entire process.

1. What is the purpose of survey analysis?

The goal of survey data analysis is to interpret data. It helps identify patterns and uncover hidden insights in the survey data. The resulting insights help you identify areas of improvement and make an informed decision to drive business success. 

2. How can you collect survey data?

You can gather survey data using various methods. You can leverage robust online survey tools , phone survey software , or mobile-offline tools to gather data from the target audience anywhere. 

3. How is survey data cleaned and processed?

Software for survey data analysis automatically cleans and processes the collected data and prepares it for analysis. It removes incomplete responses, checks for errors, and converts data into a usable format. 

4. How can you use survey analysis in market research?

Survey analysis is used in market research to identify customer needs, preferences, and pain points. It helps you analyze your products, services, and brand from customers’ perspectives. 

5. What are some challenges in survey analysis?

Incomplete data, sample selection bias, and response bias can cause trouble in survey analysis.

Survey research

An Exhaustive Guide to Different Types of Survey Research Techniques SHARE THE ARTICLE ON Table of Contents In a modern world, where companies run totally

Humanizing Market Research

Humanizing Market Research Process: Connect With Your Audience SHARE THE ARTICLE ON Table of Contents When dealing with products, services, and enterprises, it can be

Types of Sampling : Sampling Methods for social research

Comprehensive Guide on Different Sampling Methods in Social Research Try a free Voxco Online sample survey! Unlock your Sample Survey SHARE THE ARTICLE ON Table

Using Customer Experience Surveys to Design your CX Strategy

Using Customer Experience Surveys to Design your CX Strategy SHARE THE ARTICLE ON CX Strategy & Management Hub Customer Retention can be a challenge at

Conceptual Research

Conceptual Research Try a free Voxco Online sample survey! Unlock your Sample Survey SHARE THE ARTICLE ON Table of Contents What is Conceptual Research Conceptual

Customer Retention Metrics

12 Important Customer Retention Metrics To Track SHARE THE ARTICLE ON Table of Contents As a business, you cannot be happy about losing your customers.

We use cookies in our website to give you the best browsing experience and to tailor advertising. By continuing to use our website, you give us consent to the use of cookies. Read More

Your guide to mastering survey data analysis- Part 1

Reading Time: 7 minutes

Data is potent in driving big change and transformation, but only when it is meaningful. Turning your survey data into clear, meaningful, compelling analysis isn’t always a straightforward task. But first things first, what is survey data? Survey data refers to information collected through a systematic process of asking questions to individuals or groups of people in order to gather insights, opinions, attitudes, behaviors, or characteristics on a particular topic of interest.

Surveys are a common research method used in various fields such as social sciences, market research, psychology, public health, and more. The data collected from surveys can provide valuable insights into trends, preferences, and patterns among a specific population. Because of this, survey data becomes a crucial component of any organization’s strategy.

We’ve collected our experience into survey analysis to put together some good-to-know for starters about survey data components and steps to analyze the results.

Components of survey analytics

1. sampling.

Sampling is like taking a snapshot of a vast landscape. In survey research, data is collected from a subset of the larger population, known as the sample. The art of sampling lies in selecting this subset in a way that reflects the diverse characteristics of the entire population. The choice of the sample, whether random, stratified, or convenience-based, impacts how well the findings can be generalized to the larger population. It’s a critical step in ensuring that the survey’s insights hold weight beyond the sample itself.

Weighting in survey data analysis is a critical step that follows the sampling process. Once a sample is collected, it may not perfectly represent the entire population, and some groups or segments might be underrepresented or overrepresented. Weighting is a statistical technique used to address these imbalances and ensure that the survey results accurately reflect the population being studied.

Example: Imagine you want to conduct a political survey to understand public opinion. The entire population consists of all eligible voters in a country. Instead of surveying every single voter (which would be impractical), you select a sample of 1,000 voters using random sampling. This sample should reflect the country’s diversity in terms of age, gender, geographical regions, and political affiliations, ensuring your findings can be applied to the entire voter population.

2. Questionnaire

The questionnaire is the bridge between researchers and respondents. It’s a carefully constructed tool, comprising a series of questions designed to extract specific information from those participating in the survey. These questions can take on various forms, from the structured nature of multiple-choice to the open-ended canvas of open questions. Likert scales are employed to gauge attitudes, providing a spectrum of responses. The questionnaire’s construction is an art, as the quality of questions directly impacts the quality of the data gathered.

Example: In a customer satisfaction survey for a restaurant, the questionnaire includes questions like “On a scale of 1 to 5, how satisfied are you with the food quality?” (Likert scale), “What dish did you order?” (open-ended), and “Would you recommend us to a friend?” (multiple-choice). These questions are carefully designed to gather specific feedback from diners.

3. Data collection techniques

Just as artists select the right canvas, survey researchers choose the appropriate data collection method. Surveys can be administered through face-to-face interviews, telephone conversations, online interfaces, paper-based questionnaires, or even mobile apps. The choice of method depends on a multitude of factors, including the characteristics of the target population, the nature of the questions, available resources, and the desired depth of insights. It’s here that the survey’s practical journey begins.

Example: If you’re conducting a survey about smartphone usage among teenagers, you might choose to use an online survey via a mobile app. This method is more likely to reach and engage your target audience. In contrast, a face-to-face interview might be suitable for collecting in-depth responses about healthcare experiences from a group of elderly patients.

4. Data analysis

Data analysis for survey data is a pivotal phase in the survey research process, as it transforms collected information into meaningful insights. Survey data often contain a wealth of responses from participants, and the goal of data analysis is to extract, interpret, and communicate the patterns, trends, and insights hidden within the data. One of the key distinctions in survey data analysis is the differentiation between quantitative and qualitative data. Quantitative data involves numerical values and structured responses, making it amenable to statistical analysis. Researchers can compute averages, percentages, correlations, and perform hypothesis testing to uncover statistically significant relationships.

Example: If your survey examines the impact of a new educational program, quantitative analysis can reveal that student scores increased by an average of 15%. Qualitative analysis of open-ended comments might highlight that students particularly appreciated the interactive learning materials.

5. Reporting and presentation

Reporting and presentation are the final and critical steps in the journey of survey data analysis. These steps transform raw data and analysis into a format that can be easily understood and used by stakeholders, whether they are organizational leaders, policymakers, or the general public. In addition to visual aids, it is vital to include context and interpretation in reports and presentations. Simply presenting numbers and graphs is insufficient; the data must be accompanied by explanations, analysis, and recommendations. Providing context helps to explain the significance of the findings and the implications for decision-making. Interpretation bridges the gap between data and actionable insights, while recommendations guide the next steps.

Example: After a survey on workplace satisfaction, you prepare a report with clear charts and graphs that show the overall satisfaction score, broken down by department. The presentation highlights the key findings, making it easy for company leaders to understand the results and take action.

Steps to analyze survey data

Analyzing survey data holds paramount importance as it provides a lens through which to understand and decipher the thoughts, behaviors, and opinions of respondents. This analytical process unveils hidden trends, underlying patterns, and shifts in attitudes, contributing to informed decision-making. The insights derived from survey data lend credibility to hypotheses, validating assumptions and enriching research.

By decoding responses, organizations can identify growth prospects, address challenges, and tailor strategies to suit evolving needs. This analytical journey also fosters personalized engagement, cultivates customer satisfaction, and offers a platform for evaluating the efficacy of interventions. Overall, survey data analysis is the gateway to insights that lead to business growth, innovation, and effective communication.

Here are some steps to undergo while analyzing survey data:

1. Data preparation and cleaning

• Organize the data: Arrange the survey responses in a structured format, with each row representing a respondent and each column representing a question or variable.
• Check for errors: Look for inconsistencies, missing values, and outliers in the data. Decide on a strategy for dealing with missing data, whether it’s imputation or exclusion.
• Code responses: Convert open-ended qualitative responses into numerical or categorical codes for analysis, if necessary.

2. Exploratory Data Analysis (EDA)

• Descriptive statistics: Calculate basic statistics such as mean, median, mode, standard deviation, and range for each variable to understand the central tendency and variability of the data.
• Frequency distributions: Create frequency tables or histograms to visualize the distribution of responses for each categorical variable.
• Cross-tabulations: Examine relationships between different categorical variables by creating cross-tabulation tables and calculating percentages or proportions within each cell.

3. Data transformation

• Recoding variables: Combine or collapse categories to simplify analysis or create new meaningful variables.
• Scaling and standardization: Normalize numerical variables to ensure comparability, especially when using different measurement scales.

4. Hypothesis testing and statistical analysis

• Formulate hypotheses: Based on your research objectives, identify hypotheses that you want to test using the survey data.
• Choose appropriate tests: Select statistical tests based on the type of variables and research questions. Common tests include t-tests, ANOVA, chi-squared tests, correlation analysis, and regression analysis.
• Conduct the analysis: Perform the chosen statistical tests and interpret the results. Determine whether the observed differences or relationships are statistically significant.

5. Segmentation and group comparisons

• Group comparisons: Compare responses across different groups (e.g., demographics, behaviors) to identify variations and patterns.
• Segmentation: Use clustering techniques to identify subgroups within the sample that share similar characteristics or attitudes.

6. Qualitative analysis (if applicable):

• Thematic analysis: For open-ended responses, identify recurring themes or patterns in the qualitative data. Group similar responses into themes and sub-themes.
• Coding: Assign codes to segments of text that represent specific concepts or ideas. Use software tools to facilitate coding and analysis.

7. Visualization

• Charts and graphs: Create visual representations such as bar charts, line graphs, scatter plots, and pie charts to illustrate key findings and relationships.
• Heatmaps and matrices: Use these visuals to display patterns in cross-tabulated data.

8. Interpretation and conclusion

• Interpret results: Analyze the outcomes of your tests and explore what they mean in the context of your research questions.
• Draw conclusions: Summarize the main findings of your analysis and discuss their implications.

9. Results and report

• Document your analysis process, including the steps taken, methods used, and key results.
• Create clear and informative charts, tables, and graphs to include in your report.
• Write an analysis section that explains the results, their significance, and their relevance to your research objectives.

Survey data might be an underrated tool of gaining consumer understanding, but the insights it can provide are invaluable. Survey data is a highly reliable way of knowing not only what your consumers are doing, but why they’re doing it. Survey data allows you to dig deeper than other sources allow, helping you understand far more than what’s on the surface. This enables organizations to design data-driven buyer personas and consumer journey maps that build a foundation for truly cutting-edge personalized marketing.

In the next part of this blog , we cover some best practices to collect data effectively and derive actionable insights from it.

About the author

Jayant Singh is a Senior Data Scientist at Sigmoid. He has a strong expertise in AI/ML algorithms. In his current role, he leverages the data science technology spectrum to solve complex business problems and design customized cutting-edge solutions. His innovative solutions have consistently contributed to success for CPG, Retail and Fintech companies globally.

Featured blogs

How to elevate digital banking experiences for customers with data analytics

3 data and analytics trends shaping the MedTech industry

How agile MDM enables faster time to insights for life sciences

Driving regulatory success in capital markets with data governance

Subscribe to get latest insights, talk to our experts.

Get the best ROI with Sigmoid’s services in data engineering and AI

Suggested readings

Why is data engineering critical to CPG marketing success

Unlocking eCommerce growth for CPG with data and analytics

Striking a balance between data privacy and personalization with marketing analytics

• Scroll to top
• Light Dark Light Dark

Cart review

No products in the cart.

7 Proven Analysis Methods for Survey Data That Will Help You Make Better Choices

• Author Survey Point Team
• Published January 31, 2024

Unlock the power of 7 Proven Analysis Methods for Survey Data That Will Help You Make Better Choices. Discover insights, tips, and benefits for effective survey data analysis. Improve decision-making and business strategies with expert techniques.

Table of Contents

Introduction to Analysis Methods for Survey Data

Survey data provides invaluable insights into customer preferences, product performance, brand perception, and more. However, to unlock these powerful insights, the correct quantitative analysis methods must be applied. Choosing fitting statistical techniques transforms dense survey data into impactful market intelligence that guides critical business decisions. This article will explore 10 highly effective analysis methods for survey data to maximize the value derived from your research.

As surveys continue gaining popularity for market research, the ability to properly analyze the collected questionnaire data becomes ever more important. Fortunately, advances in statistical analysis software provide research teams with all the tools necessary to derive meaningful insights from complex multi-response survey data. Read on to learn key methods to analyze surveys and turn volumes of feedback into clear strategic direction.

Quantitative Analysis Methods for Survey Data 

Simple descriptive statistical techniques offer a good jumping point for quantifying survey responses. Metrics like means, frequencies, ranges, percentages, and cross tabulations summarize results on an aggregate level. They provide a high-level perspective on overall response patterns and trends occurring across various survey questions and answer choices. Descriptive statistics help spot top-level strengths, weaknesses, and differences in a clear and straightforward manner.

Analysis Methods for Survey Data in Research

Sophisticated analytic modeling reveals deeper insights from survey data that impact key research outcomes. Regression analysis quantifies the strength of relationships between survey measures and business performance by defining mathematical connections and predictive scoring models. It statistically determines which survey-based perceptual metrics across brand, product, service, and other categories drive key behaviors like satisfaction, loyalty, word-of-mouth advocacy, purchase intent, and more.

Statistical Analysis of Questionnaire Data 

SPSS, SAS, and similar statistical programs analyze survey data with incredible precision to turn raw questionnaire responses into clear insights. They run complex calculations like cross-tabulations, frequency distributions, cluster analysis, factor analysis, TURF analysis, and discrete choice modeling behind the scenes so analysts can focus purely on interpreting outputs to guide marketing, product, and financial decisions. These software tools simplify extremely detailed analysis that would not be feasible manually.

Questionnaire Data Analysis Example

For example, discrete choice analysis helps estimate sales, market share, and revenue for new products or services under varying scenarios based on survey data. By having consumers in a blind product test repeatedly select their preferred options from a series of choice sets, the algorithm predicts which prototype bundle of features, pricing, and other attributes will garner the strongest market demand. Researchers gain quantitative intelligence to optimize new offerings prior to launch.

How to Analyze Survey Data with Multiple Responses 

Today’s surveys often allow free text responses and selection of multiple answers to enable greater depth of feedback. Volume analysis, text analytics, and linguistic analysis make sense of this complex qualitative data to derive key consumer insights. Sentiment analysis also groups unstructured responses by positive, critical, or neutral tone. By combining these techniques, multilayered insights emerge from survey comments and verbatim responses previously hidden from view.

Survey Data Analysis 

The process of survey analysis includes data cleaning, weighting, coding, tabulation, statistical modeling, text mining, multivariate analysis, predictive analytics, and data visualization to turn raw survey datasets into insightful market intelligence. Leveraging the right combination of these methods helps researchers segment consumers, quantify purchase drivers, map decision journeys, track brand equity metrics across touchpoints, and reveal voice-of-the-customer insights that would otherwise remain unseen.

Survey Analysis Report

Skilled analysts deliver clear, concise survey analysis reporting to showcase key discoveries and trends emerging from processed questionnaire data. Compelling visualizations, insightful observations, and sharp recommendations focused around research goals transform abstract statistical outputs into an impactful business tool. Targeted analysis spotlights competitive advantages to exploit and urgent weaknesses requiring attention. Expert reporting brings quantified customer and market intelligence to life.

In today’s ultracompetitive landscape, accurate market research and consumer insights are more important than ever. While designing the survey questionnaire may be top of mind, applying advanced analysis techniques to collected data best unlocks the true power of survey research. Leverage these 10 methods to maximize the return on your survey investments. Let the above guide spark ideas for how quantitative analytics can help achieve your unique research objectives.

FAQs: Unlocking Survey Data Wisdom

1. How often should I conduct surveys? Survey frequency depends on your goals. Regular check-ins maintain relevance, while strategic projects may require less frequent, in-depth surveys.

2. Can I mix qualitative and quantitative data? Absolutely! Combining both enriches insights. Quantitative provides statistical depth, while qualitative adds context and human nuances.

3. What software is best for survey data analysis? Options abound, but popular choices include SPSS, SAS, and R for robust statistical analysis. For user-friendly interfaces, try SurveyMonkey or Qualtrics.

4. How do I handle missing data in surveys? Treat missing data cautiously. Depending on the extent, consider imputation methods or seek professional advice for unbiased results.

5. Are there any ethical considerations in data analysis? Ethics are paramount. Protect respondent privacy, ensure informed consent, and transparently communicate data use intentions.

6. What are the limitations of cluster analysis? While powerful, cluster analysis has constraints. It assumes homogeneity within clusters, sensitivity to outliers, and the need for careful interpretation.

In conclusion, mastering Analysis Methods for Survey Data is a transformative journey. From descriptive statistics to correlation analysis, each method adds a layer of depth to your understanding. Embrace these techniques, overcome challenges, and watch your decision-making ascend to new heights.

Survey Point Team

• AI & NLP
• Churn & Loyalty
• Customer Experience
• Customer Journeys
• Customer Metrics
• Feedback Analysis
• Product Experience
• Product Updates
• Sentiment Analysis
• Surveys & Feedback Collection
• Text Analytics
• Try Thematic

Welcome to the community

How to analyze survey data: best practices for actionable insights from survey analysis

Just started using a new survey tool ? Collected all of your survey data? Great. Confused about what to do next and how to achieve the optimal survey analysis? Don’t be.

If you’ve ever stared at an Excel sheet filled with thousands of rows of survey data and not known what to do, you’re not alone. Use this post as a guide to lead the way to execute best practice survey analysis.

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.

What is survey analysis?

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.

Why do you need for best in class survey analysis?

Data on its own means nothing without proper analysis. Thus, you need to make sure your survey analysis produces meaningful results that help make decisions that ultimately improve your business.

There are multiple ways of doing this, both manual and through software, which we’ll get to later.

Types of survey data

Data exists as numerical and text data, but for the purpose of this post, we will focus on text responses here.

Close-ended questions

Closed-ended questions can be answered by a simple one-word answer, such as “yes” or “no”. They often consist of pre-populated answers for the respondent to choose from; while an open-ended question asks the respondent to provide feedback in their own words.

Closed-ended questions come in many forms such as multiple choice, drop down and ranking questions.

In this case, they don’t allow the respondent to provide original or spontaneous answers but only choose from a list of pre-selected options. Closed-ended questions are the equivalent of being offered milk or orange juice to drink instead of being asked: “What would you like to drink?”

These types of questions are designed to create data that are easily quantifiable, and easy to code, so they’re final in their nature. They also allow researchers to categorize respondents into groups based on the options they have selected.

Open-ended questions

An open-ended question is the opposite of a closed-ended question. It’s designed to produce a meaningful answer and create rich, qualitative data using the subject’s own knowledge and feelings.

Open-ended questions often begin with words such as “Why” and “How”, or sentences such as “Tell me about…”. Open-ended questions also tend to be more objective and less leading than closed-ended questions.

How to analyze survey data

How do you find meaningful answers and insights in survey responses?

To improve your survey analysis, use the following 5 steps:

• Start with the end in mind – what are your top research questions?
• Filter results by cross-tabulating subgroups
• Interrogate the data
• Analyze your results
• Draw conclusions

1. Check off your top research questions

Go back to your main research questions which you outlined before you started your survey. Don’t have any? You should have set some out when you set a goal for your survey. (More on survey planning below).

A top research question for a business conference could be: “How did the attendees rate the conference overall?”.

The percentages in this example show how many respondents answered a particular way, or rather, how many people gave each answer as a proportion of the number of people who answered the question.

Thus, 60% or your respondents (1098 of those surveyed) are planning to return. This is the majority of people, even though almost a third are not planning to come back. Maybe there’s something you can do to convince the 11% who are not sure yet!

2. Filter results by cross-tabulating subgroups

At the start of your survey, you will have set up goals for what you wanted to achieve and exactly which subgroups you wanted to analyze and compare against each other.

This is the time to go back to those and check how they (for example the subgroups; enterprises, small businesses, self-employed) answered, with regards to attending again next year.

For this, you can cross-tabulate, and show the answers per question for each subgroup.

Here, you can see that most of the enterprises and the self-employed must have liked the conference as they’re wanting to come back, but you might have missed the mark with the small businesses.

By looking at other questions and interrogating the data further, you can hopefully figure out why and address this, so you have more of the small businesses coming back next year.

You can also filter your results based on specific types of respondents, or subgroups. So just look at how one subgroup (women, men) answered the question without comparing.

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.

3. Interrogate the data

Look at your survey questions and really interrogate them. The following are some questions we use for this:

• What are the most common responses to questions X?
• Which responses are affecting/impacting us the most?
• What’s different about this month/this year?
• What did respondents in group Y say?
• Which group of respondents are most affected by issue Z?
• Have customers noticed our efforts in solving issue Z?
• What do people say about Z?

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.

You can also compare different slices of the data, such as two different time periods, or two groups of respondents. Or, look at a particular issue or a theme, and ask questions such as “have customers noticed our efforts in solving a particular issue?”, if you’re conducting a continuous survey over multiple months or years.

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 .

4 best practices for analyzing survey data

Make sure you incorporate these tips in your analysis, to ensure your survey results are successful.

1. Ensure sample size is sufficient

To always make sure you have a sufficient sample size, consider how many people you need to survey in order to get an accurate result.

You most often will not be able to, and shouldn’t for practicality reasons, collect data from all of the people you want to speak to. So you’d take a sample (or subset) of the people of interest and learn what we can from that sample.

Clearly, if you are working with a larger sample size, your results will be more reliable as they will often be more precise. A larger sample size does often equate to needing a bigger budget though.

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.

Consider how much margin of error you’re comfortable working with first, as your sample size is always an estimate of how the overall population think and behave.

2. Statistical significance – and why it matters

How do you know you can “trust” your survey analysis ie. that you can use the answers with confidence as a basis for your decision making? In this regard, the “significant” in statistical significance refers to how accurate your data is. Or rather, that your results are not based on pure chance, but that they are in fact, representative of a sample. If your data has statistical significance, it means that to a large extent, the survey results are meaningful.

It also shows that your respondents “look like” the total population of people about whom you want to draw conclusions.

3. Focus on your insights, not the data

When presenting to your stakeholders, it’s imperative to highlight the insights derived from your data, rather than the data itself.

You’ll do yourself a disservice. Don’t even present the information from the data. Don’t wait for your team to create insights out of the data, you’ll get a better response and better feedback if you are the one that demonstrates the insights to begin with, as it goes beyond just sharing percentages and data breakouts.

4. Complement with other types of data

Don’t stop at the survey data alone. When presenting your insights, to your stakeholders or board, it’s always helpful to use different data points and which might include even personal experiences. If you have personal experience with the topic, use it! If you have qualitative research that supports the data, use it!

So, if you can overlap qualitative research findings with your quantitative data, do so.

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.

3 ways to code open-ended responses

When you analyze open-ended responses, you need to code them. Coding open-ended questions have 3 approaches, here’s a taster:

• Manual coding by someone internally.   If you receive 100-200 responses per month, this is absolutely doable. The big disadvantage here is that there is a high likelihood that whoever codes your text will apply their own biases and simply not notice particular themes, because they subconsciously don’t think it’s important to monitor.
• Outsource to an agency.  You can email the results and they would simply send back coded responses.
• Automating the coding.  You use an algorithm to simulate the work of a professional human coder.

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.

Code frames can also be combined with a sentiment.

Below, we’re inserting the positive and the negative layer under customer service theme.

So, next, you apply this code frame. Below are snippets from a manual coding job commissioned to an agency.

In the first snippet, there’s a code frame. Under code 1, they code “Applied courses”, and under code “2 Degree in English”. In the second snippet, you can see the actual coded data, where each comment has up to 5 codes from the above code frame. You can imagine that it’s actually quite difficult to analyze data presented in this way in Excel, but it’s much easier to do it using software.

The best survey analysis software tools

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:

Do you need software?

When you’re dealing with large amounts of data, it is impossible to manage it all properly manually. Either because there’s simply too much of it or if you’re looking to avoid any bias, or if it’s a long-term study, for example. Then, there is no other option but to use software”

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.

Below we give just a few examples of types of software you could use to analyze survey data. Of course, these are just a few examples to illustrate the types of functions you could employ.

1. Thematic software

As an example, with Thematic’s software solution you can identify trends in sentiment and particular themes. Bias is also avoided as it is a software tool, and it doesn’t over-emphasize or ignore specific comments to come to unquantified conclusions.

Below is an example we’ve taken from the tool, to visualize some of Thematic’s features.

Our visualizations tools show far more detail than word clouds, which are more typically used.

You can see two different slices of data. The blue bars are United Airlines 1 and 2-star reviews, and the orange bars are the 4 and 5-star reviews. It’s a fantastic airline, but you can identify the biggest issue as mentioned most frequently by 1-2 stars reviews, which is their flight delays. But the 4 and 5-star reviews have frequent praise for the friendliness of the airline.

You can find more features, such as Thematic’s Impact tool, Comparison, Dashboard and Themes Editor  here.

If you’re a DIY analyzer, there’s quite a bit you can do in Excel. Clearly, you do not have the sophisticated features of an online software tool, but for simple tasks, it does the trick. You can count different types of feedback (responses) in the survey, calculate percentages of the different responses survey and generate a survey report with the calculated results. For a technical overview, see  this article.

You can also build your own text analytics solution, and rather fast.

How to build a Text Analytics solution in 10 minutes

The following is an excerpt from a blog written by Alyona Medelyan, PhD in Natural Language Processing & Machine Learning.

As she mentions, you can type in a formula, like this one, in Excel to categorize comments into “Billing”, “Pricing” and “Ease of use”:

It can take less than 10 minutes to create this, and the result is so encouraging! But wait…

Everyone loves simplicity. But in this case, simplicity sucks

Various issues can easily crop up with this approach, see the image below:

Out of 7 comments, here only 3 were categorized correctly. “Billing” is actually about “Price”, and three other comments missed additional themes. Would you bet your customer insights on something that’s at best 50 accurate?

Developed by QRS International,  Nvivo  is a tool where you can store, organize, categorize and analyze your data and also create visualisations. Nvivo lets you store and sort data within the platform, automatically sort sentiment, themes and attribute, and exchange data with SPSS for further statistical analysis. There’s a transcription tool for quick transcription of voice data.

It’s a no-frills online tool, great for academics and researchers.

4.  Interpris

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.

Favoured by government agencies and communities, it’s good for employee engagement, public opinion and community engagement surveys.

Other tools worth mentioning (for survey analysis but not open-ended questions) are SurveyMonkey, Tableau and DataCracker.

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.

An idea is to check the list of existing clients of the product, which is often listed on their website. Crucially, you’ll want to test the tool, or at the least, get a demo from the sales team, ideally using your own data so that you can use the time to gather new insights.

A few tips on survey design

Good surveys start with smart survey design. Firstly, you need to plan for survey design success. Here are a few tips:

Our 9 top tips for survey design planning

1. keep it short.

Only include questions that you are actually going to use. You might think there are lots of questions that seem useful, but they can actually negatively affect your survey results. Another reason is that often we ask redundant questions that don’t contribute to the main problem we want to solve. The survey can be as short as three questions.

2. Use open-ended questions first

To avoid enforcing your own assumptions, use open-ended questions first. Often, we start with a few checkboxes or lists, which can be intimidating for survey respondents. An open-ended question feels more inviting and warmer – it makes people feel like you want to hear what they want to say and actually start a conversation. Open-ended questions give you more insightful answers, however, closed questions are easier to respond to, easier to analyze,  but they  do not create rich insights.

The best approach is to use a mix of both types of questions, as It’s more compelling to answer different types of questions for respondents.

3. Use surveys as a way to present solutions

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.

4. Consider your timing

It’s important to think about the timing of your survey. Take into account when your audience is most likely to respond to your survey and give them the opportunity to do it at their leisure, at the time that suits them.

5. Challenge your assumptions

It’s crucial to challenge your assumptions, as it’s very tempting to make assumptions about why things are the way they are. There is usually more than meets the eye about a person’s preferences and background which can affect the scenario.

6. Have multiple survey-writers

To have multiple survey writer can be helpful, as having people read each other’s work and test the questions helps address the fact that most questions can be interpreted in more than one way.

7. Choose your survey questions carefully

When you’re choosing your survey questions, make it really count. Only use those that can make a difference to your end outcomes.

8. Be prepared to report back results and take action

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.

9. What’s in it for them?

Always think about what customers (or survey respondents) want and what’s in it for them. Many businesses don’t actually think about this when they send out their surveys.

If you can nail the “what’s in it for me”, you automatically solve many of the possible issues for the survey, such as whether the respondents have enough incentive or not, or if the survey is consistent enough.

For a good survey design, always ask:

•      What insight am I hoping to get from this question?
•      Is it likely to provide useful answers?

For more pointers on how to design your survey for success, check out our blog on  4 Steps to Customer Survey Design – Everything You Need to Know .

Agi loves writing! She enjoys breaking down complex topics into clear messages that help others. She speaks four languages fluently and has lived in six different countries.

We make it easy to discover the customer and product issues that matter.

Unlock the value of feedback at scale, in one platform. Try it for free now!

• Questions to ask your Feedback Analytics vendor
• How to end customer churn for good
• Scalable analysis of NPS verbatims
• 5 Text analytics approaches
• How to calculate the ROI of CX

Our experts will show you how Thematic works, how to discover pain points and track the ROI of decisions. To access your free trial, book a personal demo today.

Recent posts

Become a qualitative theming pro! Creating a perfect code frame is hard, but thematic analysis software makes the process much easier.

When two major storms wreaked havoc on Auckland and Watercare’s infrastructurem the utility went through a CX crisis. With a massive influx of calls to their support center, Thematic helped them get inisghts from this data to forge a new approach to restore services and satisfaction levels.

Everyone says they want customers to be satisfied, but what are you actually doing to make customers happy? How do you know if you’re on the right track? How do you know if your customer satisfaction efforts make a difference? Why even aim for customer satisfaction at all? We

How to Analysis of Survey Data: Methods & Examples

Varun Saharawat is a seasoned professional in the fields of SEO and content writing. With a profound knowledge of the intricate aspects of these disciplines, Varun has established himself as a valuable asset in the world of digital marketing and online content creation.

Analysis of Survey Data transforms raw data into meaningful insights. By adhering to best practices, you can leverage survey findings to enhance business strategies or inform research outcomes.

Analysis of Survey Data : As a researcher, marketer, or student, have you ever struggled to make sense of all the responses from a survey you administered? You’re not alone – understanding large amounts of survey data can be an overwhelming task. Data, data everywhere – but are you making sense of it all? However, raw survey results don’t always tell the full story – real understanding comes from carefully analyzing your data.

Remember to break down your data, use visual aids, and look for patterns in the responses. These strategies will help you make informed decisions and guide your next steps. But don’t stop here! Keep learning and honing your skills by enrolling in a data analytics course like the one offered by Physics Wallah.

With our experienced instructors and practical approach, you’ll be equipped with the tools and techniques needed to master survey analysis. And as a token of appreciation for being a dedicated reader, use “ READER ” as a coupon code to receive a discounted price for the course.

Table of Contents

Survey Data Analysis Examples

Let’s consider a hypothetical survey about customer satisfaction with a new mobile application. The survey was distributed to 500 users, and we collected both quantitative and qualitative data. Here’s a simplified example of how you might analyze the survey data:

Quantitative Data Analysis:

• Descriptive Statistics : Begin by calculating basic statistics like mean, median, mode, and standard deviation for questions that had numerical responses, such as “On a scale of 1-10, how satisfied are you with the app?”
• Cross-Tabulation : Create cross-tabulation tables to analyze relationships between different variables. For instance, you could cross-tabulate satisfaction levels with the frequency of app usage.
• Regression Analysis : Determine if there’s a correlation between user demographics (like age, location, or occupation) and satisfaction levels. A regression model might help predict satisfaction based on these variables.

Qualitative Data Analysis:

• Thematic Analysis : Manually review open-ended responses to identify recurring themes or sentiments. For instance, common themes might include “ease of use,” “features lacking,” or “customer support.”
• Sentiment Analysis : Use text analytics tools or software to perform sentiment analysis on qualitative responses. This will help categorize feedback as positive, negative, or neutral, providing an overall sentiment score.
• Word Clouds : Generate word clouds to visualize frequently mentioned words or phrases in the qualitative feedback. This gives a quick snapshot of what users are talking about most frequently.

Also Read: Quantitative Data Analysis: Types, Analysis & Examples

How do you Analyze Survey Data? (Effective Ways)

Analyzing survey data is a crucial step in extracting meaningful insights that can drive informed decision-making and strategic planning. While gathering data is essential, interpreting it correctly is equally vital. Here’s a comprehensive guide on how to analyze survey data effectively, utilizing various techniques and best practices.

1) Comprehend the Measurement Scales

Understanding the types of measurement scales—nominal, ordinal, interval, and ratio—is foundational. Each scale serves a unique purpose and requires distinct analytical approaches:

• Nominal Scales : Utilized for qualitative data, nominal scales categorize responses without imposing any order.
• Ordinal Scales : These scales rank responses based on preferences or orders, allowing for comparative analysis.
• Interval Scales : Ideal for capturing responses within a predefined range, facilitating more nuanced analysis.
• Ratio Scales : Similar to interval scales but starting at zero, these scales provide a comprehensive quantitative assessment.

2) Prioritize Quantitative Insights

Initiate your analysis by focusing on quantitative questions that yield numerical data. Metrics like the Net Promoter Score (NPS) can offer insights into customer sentiments and brand loyalty, enabling you to identify brand advocates and areas for improvement.

3) Harness Qualitative Feedback

While quantitative data provides numerical insights, qualitative feedback offers depth and context. Analyze open-ended responses by:

• Creating visual representations to identify common themes or keywords.
• Individualizing responses to understand unique customer perspectives and expectations.

4) Implement Cross-Tabulation Analysis

Cross-tabulation facilitates a deeper understanding of the relationship between variables, especially when targeting specific demographics or segments. By segmenting data based on relevant criteria, such as age or location, you can derive more targeted insights relevant to your objectives.

5) Distinguish Between Correlation and Causation

Avoid conflating correlation with causation, as it can lead to misleading interpretations. Scrutinize data meticulously, considering external factors and variables, to draw accurate conclusions and avoid erroneous assumptions.

6) Benchmark Against Historical Data

Comparing current survey results with past data sets enables you to assess progress, identify trends, and evaluate the effectiveness of implemented strategies. By tracking key metrics over time, you can measure improvements and refine your approach continually.

7) Utilize Industry Benchmarks

Benchmarking against industry standards provides context and perspective, allowing you to gauge your performance relative to competitors and market leaders. Aligning your survey results with industry benchmarks ensures realistic goals and actionable insights.

8) Mitigate Inaccurate or Incomplete Responses

Addressing incomplete or inaccurate survey responses is crucial for maintaining data integrity. Identify and categorize inattentive respondents, such as speeders, straight-liners, slackers, and imposters, to filter out unreliable data and enhance the validity of your analysis.

Analyzing survey data is a multifaceted process that necessitates a structured approach, incorporating both quantitative and qualitative methods. By understanding measurement scales, prioritizing actionable insights, leveraging analytical techniques like cross-tabulation, and benchmarking against relevant benchmarks, organizations can derive meaningful insights to inform decision-making, optimize strategies, and drive continuous improvement.

Statistical Analysis of Survey Data

Statistical analysis of survey data involves employing various statistical techniques to analyze and interpret the collected survey responses. This analytical process aims to uncover patterns, trends, relationships, and insights from the data, enabling organizations to make informed decisions, optimize strategies, and address specific research objectives. Here’s an overview of the statistical analysis techniques commonly used in survey data analysis:

1) Descriptive Statistics:

Descriptive statistics provide a summary of the main aspects of the survey data, including measures of central tendency (mean, median, mode), variability (standard deviation, variance, range), and distribution (skewness, kurtosis). These statistics offer an initial understanding of the data’s characteristics, such as the average response, variability among responses, and distribution patterns.

2) Inferential Statistics:

Inferential statistics enable researchers to generalize findings from a sample to a larger population, assess relationships between variables, and test hypotheses. Common inferential statistical tests include:

• T-tests: Used to compare the means of two groups or assess differences between two sets of data.
• ANOVA (Analysis of Variance): Employed to compare means across multiple groups simultaneously.
• Chi-Square Test: Applied to examine the association between categorical variables and determine if observed frequencies differ significantly from expected frequencies.
• Regression Analysis: Used to identify and quantify relationships between a dependent variable and one or more independent variables, predicting the outcome based on predictor variables.

3) Correlation Analysis:

Correlation analysis assesses the strength and direction of the relationship between two continuous variables. The Pearson correlation coefficient measures the linear relationship between variables, ranging from -1 (negative correlation) to 1 (positive correlation), with 0 indicating no correlation.

4) Factor Analysis:

Factor analysis is a multivariate statistical technique used to identify underlying relationships between observed variables, uncover latent variables or factors, and reduce data dimensionality. By grouping related variables into distinct factors, researchers can simplify complex data structures and identify underlying patterns or constructs.

5) Cluster Analysis:

Cluster analysis categorizes survey respondents or variables into distinct groups or clusters based on similarities within groups and differences between groups. This technique helps segment the target population, identify distinct respondent profiles, or group similar survey items, facilitating more targeted and personalized strategies.

6) Regression Modeling:

Regression modeling encompasses various regression techniques, including linear regression, logistic regression, and multiple regression, to predict or explain the relationship between dependent and independent variables. By evaluating the impact of predictor variables on the outcome variable, organizations can identify key drivers, assess relationships, and develop predictive models.

Also Read: Analysis vs. Analytics: How Are They Different?

Analysis of Survey Data in Research

Analysis of survey data in research is a critical component that involves examining, interpreting, and making sense of the collected survey responses. It enables researchers to derive meaningful insights, identify patterns, trends, and relationships, and draw valid conclusions to address research objectives or hypotheses effectively. Here’s a comprehensive overview of the analysis of survey data in research:

1) Data Preparation:

Before conducting any analysis, researchers must prepare the survey data by cleaning, organizing, and coding the responses. This involves:

• Data Cleaning: Identifying and addressing missing, incomplete, or erroneous responses to ensure data accuracy and reliability.
• Data Transformation: Converting raw survey data into a format suitable for analysis, such as numerical coding, categorization, or scaling.
• Variable Identification: Defining variables, distinguishing between independent and dependent variables, and categorizing variables based on their type (e.g., nominal, ordinal, interval, ratio).

2) Descriptive Analysis:

Descriptive analysis involves summarizing and describing the main features of the survey data using:

• Measures of Central Tendency: Calculating mean, median, and mode to determine the average or typical response.
• Measures of Dispersion: Assessing variability using standard deviation, variance, and range to understand the spread or dispersion of responses.
• Frequency Distributions: Creating frequency tables, histograms, or bar charts to display the distribution of categorical or continuous variables.

3) Inferential Analysis:

Inferential analysis focuses on making predictions, generalizing findings, or testing hypotheses based on the survey sample data. Common inferential techniques include:

• Hypothesis Testing: Using statistical tests such as t-tests, ANOVA, chi-square tests, or regression analysis to test research hypotheses, assess differences between groups, or determine associations between variables.
• Confidence Intervals: Estimating the range within which population parameters (e.g., means, proportions) are likely to fall based on sample data.

4) Correlation and Regression Analysis:

Correlation and regression analysis help researchers understand relationships between variables, predict outcomes, and identify key predictors:

• Correlation Analysis: Using correlation coefficients to assess the strength and direction of relationships between two or more continuous variables.
• Regression Analysis: Developing predictive models to explain the relationship between dependent and independent variables, identify significant predictors, and predict outcomes based on predictor variables.

5) Factor and Cluster Analysis:

Factor and cluster analysis are advanced techniques used to identify underlying patterns, group variables or respondents, and reduce data complexity:

• Factor Analysis: Identifying latent variables or underlying constructs, reducing data dimensionality, and uncovering patterns or relationships between observed variables.
• Cluster Analysis: Segmenting respondents or variables into distinct groups based on similarities, facilitating targeted analysis, and understanding respondent segments or patterns.

Survey Data Analysis Methods

Survey data analysis serves as a critical step in understanding the collected information, drawing meaningful insights, and making informed decisions. By employing specific methods tailored to the type and structure of the survey data, researchers and analysts can effectively interpret and leverage the information gathered. Here’s a detailed exploration of various survey data analysis methods:

1) Statistical Analysis:

Statistical analysis stands as a cornerstone in survey data analysis, offering rigorous methods to examine relationships, differences, and patterns within the data. Key statistical techniques include:

• Regression Analysis: Assessing the relationship between dependent and independent variables to predict outcomes or understand associations.
• T-Test: Comparing means between two groups to determine if there are significant differences.
• Analysis of Variance (ANOVA): Evaluating differences in means across multiple groups or categories.
• Cluster Analysis: Identifying distinct groups or clusters within the data based on similarities.
• Factor Analysis: Uncovering underlying relationships between observed variables by identifying latent factors or constructs.
• Conjoint Analysis: Analyzing respondent preferences and trade-offs among different attributes or features.

2) Measurement Scales Understanding:

Recognizing the measurement scales of survey questions forms a foundational aspect of data analysis . Different scales, including nominal, ordinal, interval, and ratio scales, dictate the type of statistical tests and analyses appropriate for the data, ensuring accurate and meaningful interpretation.

3) Quantitative Questions Analysis:

Initiating the analysis with quantitative questions facilitates establishing numerical trends, patterns, and relationships within the data. By prioritizing quantitative analysis, researchers can quantify responses, calculate descriptive statistics, and derive statistical inferences to address research objectives effectively.

4) Visualization Tools:

Visual representation of survey data plays a pivotal role in conveying insights, identifying trends, and communicating findings to stakeholders. Utilizing visualization tools such as pie charts, Venn diagrams, line graphs, scatter plots, histograms, and pictograms enhances data interpretation, fosters comprehension, and facilitates decision-making processes.

5) Popular Methods Utilization:

Embracing popular methods specific to survey data analysis ensures comprehensive insights extraction. By leveraging the nine most recognized methods for survey data analysis, researchers can navigate the complexities of data interpretation, uncover hidden patterns, validate research hypotheses, and inform strategic decisions effectively.

Also Read: Learning Path to Become a Data Analyst in 2024

How to Present Survey Data

Presenting survey data in a coherent, compelling, and easily digestible manner is crucial for conveying insights, fostering understanding, and driving informed decision-making among stakeholders. By employing various methods tailored to the nature and complexity of the survey data, you can effectively communicate findings and facilitate meaningful discussions. Here’s an in-depth exploration of how to present survey data:

1) Graphical Representation:

Graphs stand as a cornerstone in presenting survey data due to their ability to simplify complex information and facilitate visual interpretation. Depending on the nature of your data, consider utilizing the following graphical representations:

• Pie Charts: Ideal for illustrating proportions and percentages, pie charts offer a clear visualization of categorical data distribution.
• Venn Diagrams: Useful for showcasing overlaps or intersections between different data sets or categories.
• Scatter Plots: Effective for displaying relationships and correlations between two variables, facilitating trend identification.
• Histograms: Perfect for representing frequency distributions and identifying data distribution patterns.
• Pictograms: Employ visuals or icons to represent data quantities, making data more relatable and engaging.

Ensure selecting the most appropriate graph type that aligns with your data characteristics and resonates with your target audience’s preferences and comprehension levels.

2) Data Tables:

Data tables serve as a structured and systematic approach to presenting numerical survey data. By leveraging tools like Excel, you can organize, categorize, and display quantitative data in a tabular format, enhancing clarity, and facilitating comparative analysis. Ensure incorporating relevant headers, footnotes, and annotations to provide context and facilitate interpretation.

3) Interactive Presentations:

Crafting interactive presentations enables you to amalgamate textual and graphical data, fostering engagement and facilitating comprehensive understanding. Begin by outlining the research objectives, methodology, and hypothesis, followed by systematically presenting survey findings, insights, and implications. Utilize visuals, animations, and infographics to enhance engagement, convey key messages, and facilitate interactive discussions.

4) Infographics:

Infographics emerge as a potent tool for presenting survey data in a visually appealing, concise, and easily consumable format. By transforming survey results into compelling visuals, statistics, and narratives, infographics enhance information retention, facilitate comprehension, and augment the aesthetic appeal of your presentations. Consider incorporating color coding, icons, and concise text to convey key findings, trends, and insights succinctly.

5) Comprehensive Reports:

For investor meetings, shareholder discussions, or detailed presentations, comprehensive reports serve as an invaluable tool for presenting survey data. While incorporating graphs, tables, and infographics, reports provide an in-depth analysis, interpretation, and contextualization of survey findings.

Ensure structuring your report systematically, including an executive summary, methodology, findings, discussions, conclusions, and recommendations. Facilitate accessibility by incorporating a table of contents, appendices, and references, ensuring stakeholders can delve deeper into specific sections or data points as required.

Common Mistakes in Analysis of Survey Data and How to Avoid Them

Analyzing survey data is a pivotal step in extracting valuable insights that can drive informed decisions, shape strategies, and inform future research endeavors. However, several common pitfalls can compromise the accuracy, reliability, and validity of your findings. Recognizing these challenges and implementing strategies to mitigate them is crucial for ensuring robust and actionable survey data analysis. Here’s a comprehensive exploration of these common mistakes and how to navigate them effectively:

1) Premature Interpretation of Results:

Common Mistake: Succumbing to confirmation bias by hastily interpreting survey results that align with preconceived notions or expectations without ensuring statistical significance.

Mitigation Strategy: Prioritize a rigorous statistical analysis approach to ascertain the validity, reliability, and significance of your findings. Emphasize the importance of a sufficiently large sample size to minimize the likelihood of skewed or coincidental results. Adopt a systematic and unbiased approach to data interpretation, emphasizing objectivity, and evidence-based conclusions.

2) Misinterpreting Correlation as Causation:

Common Mistake: Conflating correlation with causation, attributing causative relationships between variables solely based on observed correlations without considering potential confounding variables or underlying mechanisms.

Mitigation Strategy: Exercise caution and critical thinking when interpreting relationships between variables. Emphasize the importance of exploring underlying factors, mechanisms, and variables that may influence observed correlations. Encourage a comprehensive and nuanced analysis that considers potential confounders, alternative explanations, and causal pathways, ensuring accurate and informed interpretations.

3) Overlooking Nuances in Qualitative Natural Language Data:

Common Mistake: Oversimplifying the analysis of qualitative survey data, such as speech or text responses, by relying solely on superficial categorizations or failing to capture the richness, context, and intricacies of human language.

Mitigation Strategy: Leverage advanced AI solutions and machine learning algorithms capable of sophisticated sentiment analysis, contextual understanding, and nuanced interpretation of qualitative data. Prioritize tools that emulate human-like comprehension, considering context, emotion, intent, and conversational dynamics. Foster a multidimensional approach to qualitative data analysis, emphasizing depth, richness, and comprehensive understanding to extract meaningful insights effectively.

Also Read: AI and Predictive Analytics: Examples, Tools, Uses, Ai Vs Predictive Analytics

Tools for Analysis of Survey Data

Analyzing survey data requires specialized tools that can efficiently process, visualize, and interpret the collected information. Here are some commonly used tools for the analysis of survey data:

If you still feel overwhelmed or want to enhance your skills further, we highly recommend enrolling in the Data Analytics course offered by Physics Wallah . Their comprehensive syllabus covers all aspects of survey data analysis and is taught by experienced professionals who are passionate about imparting their knowledge. And as a token of appreciation for being a reader of this blog post, use the “READER” coupon code to avail yourself of a special discount on the course fee.

For Latest Tech Related Information, Join Our Official Free Telegram Group : PW Skills Telegram Group

Analysis of Survey Data FAQs

What is the survey method of data analysis.

The survey method of data analysis involves collecting structured information from respondents through questionnaires or interviews. Once gathered, this data undergoes systematic examination to extract insights, trends, or patterns that can inform decision-making or research objectives.

What is the best tool to analyze survey data?

Several tools can effectively analyze survey data based on specific needs, such as SPSS, Qualtrics, SurveyMonkey, and Microsoft Excel. The "best" tool often depends on the complexity of the survey, required analytical techniques, user expertise, and desired output formats.

What is the purpose of survey analysis?

The purpose of survey analysis is to interpret collected data to understand respondent opinions, behaviors, preferences, or attitudes. By analyzing survey results, organizations or researchers can derive insights, make informed decisions, assess trends, identify patterns, and address research objectives or business challenges effectively.

What is the primary objective of analyzing survey data?

The primary objective of analyzing survey data is to extract valuable insights, patterns, and trends from the collected responses. This analysis aids in understanding respondent behaviors, preferences, opinions, and perceptions, enabling organizations to make informed decisions, shape strategies, and inform future initiatives effectively.

What are the key steps involved in analyzing survey data?

The key steps involved in analyzing survey data encompass data cleaning and preparation, defining objectives and research questions, selecting appropriate analytical techniques, conducting statistical analyses (e.g., regression analysis, t-tests, ANOVA), interpreting findings, and communicating results effectively.

• AI and Data Analytics: Tools, Uses, Importance, Salary, and more!

AI and Data analytics are gaining a lot of traction in 2024. They both are very important in today’s business…

• What Is A Trusted Analytics Platform?

A trusted analytics platform refers to a software infrastructure or system that helps organizations to securely and effectively analyze large…

• Top 6 Big Data Courses To Consider

Big Data Courses play a crucial role in equipping you with the essential knowledge and skills required for starting a…

Related Articles

• The Best Business Intelligence Software in 2024
• Analysis of Algorithm in Data Structure
• Your Guide to Data Analytics Certification Program
• What are Data Analysis Tools?
• Best 5 Unique Strategies to Use Artificial Intelligence Data Analytics
• Best Courses For Data Analytics: Top 10 Courses For Your Career in Trend
• Data Analysis: Importance, Types, Methods of Data Analytics

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• J Adv Pract Oncol
• v.6(2); Mar-Apr 2015

Understanding and Evaluating Survey Research

A variety of methodologic approaches exist for individuals interested in conducting research. Selection of a research approach depends on a number of factors, including the purpose of the research, the type of research questions to be answered, and the availability of resources. The purpose of this article is to describe survey research as one approach to the conduct of research so that the reader can critically evaluate the appropriateness of the conclusions from studies employing survey research.

SURVEY RESEARCH

Survey research is defined as "the collection of information from a sample of individuals through their responses to questions" ( Check & Schutt, 2012, p. 160 ). This type of research allows for a variety of methods to recruit participants, collect data, and utilize various methods of instrumentation. Survey research can use quantitative research strategies (e.g., using questionnaires with numerically rated items), qualitative research strategies (e.g., using open-ended questions), or both strategies (i.e., mixed methods). As it is often used to describe and explore human behavior, surveys are therefore frequently used in social and psychological research ( Singleton & Straits, 2009 ).

Information has been obtained from individuals and groups through the use of survey research for decades. It can range from asking a few targeted questions of individuals on a street corner to obtain information related to behaviors and preferences, to a more rigorous study using multiple valid and reliable instruments. Common examples of less rigorous surveys include marketing or political surveys of consumer patterns and public opinion polls.

Survey research has historically included large population-based data collection. The primary purpose of this type of survey research was to obtain information describing characteristics of a large sample of individuals of interest relatively quickly. Large census surveys obtaining information reflecting demographic and personal characteristics and consumer feedback surveys are prime examples. These surveys were often provided through the mail and were intended to describe demographic characteristics of individuals or obtain opinions on which to base programs or products for a population or group.

More recently, survey research has developed into a rigorous approach to research, with scientifically tested strategies detailing who to include (representative sample), what and how to distribute (survey method), and when to initiate the survey and follow up with nonresponders (reducing nonresponse error), in order to ensure a high-quality research process and outcome. Currently, the term "survey" can reflect a range of research aims, sampling and recruitment strategies, data collection instruments, and methods of survey administration.

Given this range of options in the conduct of survey research, it is imperative for the consumer/reader of survey research to understand the potential for bias in survey research as well as the tested techniques for reducing bias, in order to draw appropriate conclusions about the information reported in this manner. Common types of error in research, along with the sources of error and strategies for reducing error as described throughout this article, are summarized in the Table .

Sources of Error in Survey Research and Strategies to Reduce Error

The goal of sampling strategies in survey research is to obtain a sufficient sample that is representative of the population of interest. It is often not feasible to collect data from an entire population of interest (e.g., all individuals with lung cancer); therefore, a subset of the population or sample is used to estimate the population responses (e.g., individuals with lung cancer currently receiving treatment). A large random sample increases the likelihood that the responses from the sample will accurately reflect the entire population. In order to accurately draw conclusions about the population, the sample must include individuals with characteristics similar to the population.

It is therefore necessary to correctly identify the population of interest (e.g., individuals with lung cancer currently receiving treatment vs. all individuals with lung cancer). The sample will ideally include individuals who reflect the intended population in terms of all characteristics of the population (e.g., sex, socioeconomic characteristics, symptom experience) and contain a similar distribution of individuals with those characteristics. As discussed by Mady Stovall beginning on page 162, Fujimori et al. ( 2014 ), for example, were interested in the population of oncologists. The authors obtained a sample of oncologists from two hospitals in Japan. These participants may or may not have similar characteristics to all oncologists in Japan.

Participant recruitment strategies can affect the adequacy and representativeness of the sample obtained. Using diverse recruitment strategies can help improve the size of the sample and help ensure adequate coverage of the intended population. For example, if a survey researcher intends to obtain a sample of individuals with breast cancer representative of all individuals with breast cancer in the United States, the researcher would want to use recruitment strategies that would recruit both women and men, individuals from rural and urban settings, individuals receiving and not receiving active treatment, and so on. Because of the difficulty in obtaining samples representative of a large population, researchers may focus the population of interest to a subset of individuals (e.g., women with stage III or IV breast cancer). Large census surveys require extremely large samples to adequately represent the characteristics of the population because they are intended to represent the entire population.

DATA COLLECTION METHODS

Survey research may use a variety of data collection methods with the most common being questionnaires and interviews. Questionnaires may be self-administered or administered by a professional, may be administered individually or in a group, and typically include a series of items reflecting the research aims. Questionnaires may include demographic questions in addition to valid and reliable research instruments ( Costanzo, Stawski, Ryff, Coe, & Almeida, 2012 ; DuBenske et al., 2014 ; Ponto, Ellington, Mellon, & Beck, 2010 ). It is helpful to the reader when authors describe the contents of the survey questionnaire so that the reader can interpret and evaluate the potential for errors of validity (e.g., items or instruments that do not measure what they are intended to measure) and reliability (e.g., items or instruments that do not measure a construct consistently). Helpful examples of articles that describe the survey instruments exist in the literature ( Buerhaus et al., 2012 ).

Questionnaires may be in paper form and mailed to participants, delivered in an electronic format via email or an Internet-based program such as SurveyMonkey, or a combination of both, giving the participant the option to choose which method is preferred ( Ponto et al., 2010 ). Using a combination of methods of survey administration can help to ensure better sample coverage (i.e., all individuals in the population having a chance of inclusion in the sample) therefore reducing coverage error ( Dillman, Smyth, & Christian, 2014 ; Singleton & Straits, 2009 ). For example, if a researcher were to only use an Internet-delivered questionnaire, individuals without access to a computer would be excluded from participation. Self-administered mailed, group, or Internet-based questionnaires are relatively low cost and practical for a large sample ( Check & Schutt, 2012 ).

Dillman et al. ( 2014 ) have described and tested a tailored design method for survey research. Improving the visual appeal and graphics of surveys by using a font size appropriate for the respondents, ordering items logically without creating unintended response bias, and arranging items clearly on each page can increase the response rate to electronic questionnaires. Attending to these and other issues in electronic questionnaires can help reduce measurement error (i.e., lack of validity or reliability) and help ensure a better response rate.

Conducting interviews is another approach to data collection used in survey research. Interviews may be conducted by phone, computer, or in person and have the benefit of visually identifying the nonverbal response(s) of the interviewee and subsequently being able to clarify the intended question. An interviewer can use probing comments to obtain more information about a question or topic and can request clarification of an unclear response ( Singleton & Straits, 2009 ). Interviews can be costly and time intensive, and therefore are relatively impractical for large samples.

Some authors advocate for using mixed methods for survey research when no one method is adequate to address the planned research aims, to reduce the potential for measurement and non-response error, and to better tailor the study methods to the intended sample ( Dillman et al., 2014 ; Singleton & Straits, 2009 ). For example, a mixed methods survey research approach may begin with distributing a questionnaire and following up with telephone interviews to clarify unclear survey responses ( Singleton & Straits, 2009 ). Mixed methods might also be used when visual or auditory deficits preclude an individual from completing a questionnaire or participating in an interview.

FUJIMORI ET AL.: SURVEY RESEARCH

Fujimori et al. ( 2014 ) described the use of survey research in a study of the effect of communication skills training for oncologists on oncologist and patient outcomes (e.g., oncologist’s performance and confidence and patient’s distress, satisfaction, and trust). A sample of 30 oncologists from two hospitals was obtained and though the authors provided a power analysis concluding an adequate number of oncologist participants to detect differences between baseline and follow-up scores, the conclusions of the study may not be generalizable to a broader population of oncologists. Oncologists were randomized to either an intervention group (i.e., communication skills training) or a control group (i.e., no training).

Fujimori et al. ( 2014 ) chose a quantitative approach to collect data from oncologist and patient participants regarding the study outcome variables. Self-report numeric ratings were used to measure oncologist confidence and patient distress, satisfaction, and trust. Oncologist confidence was measured using two instruments each using 10-point Likert rating scales. The Hospital Anxiety and Depression Scale (HADS) was used to measure patient distress and has demonstrated validity and reliability in a number of populations including individuals with cancer ( Bjelland, Dahl, Haug, & Neckelmann, 2002 ). Patient satisfaction and trust were measured using 0 to 10 numeric rating scales. Numeric observer ratings were used to measure oncologist performance of communication skills based on a videotaped interaction with a standardized patient. Participants completed the same questionnaires at baseline and follow-up.

The authors clearly describe what data were collected from all participants. Providing additional information about the manner in which questionnaires were distributed (i.e., electronic, mail), the setting in which data were collected (e.g., home, clinic), and the design of the survey instruments (e.g., visual appeal, format, content, arrangement of items) would assist the reader in drawing conclusions about the potential for measurement and nonresponse error. The authors describe conducting a follow-up phone call or mail inquiry for nonresponders, using the Dillman et al. ( 2014 ) tailored design for survey research follow-up may have reduced nonresponse error.

CONCLUSIONS

Survey research is a useful and legitimate approach to research that has clear benefits in helping to describe and explore variables and constructs of interest. Survey research, like all research, has the potential for a variety of sources of error, but several strategies exist to reduce the potential for error. Advanced practitioners aware of the potential sources of error and strategies to improve survey research can better determine how and whether the conclusions from a survey research study apply to practice.

The author has no potential conflicts of interest to disclose.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

The Beginner's Guide to Statistical Analysis | 5 Steps & Examples

Statistical analysis means investigating trends, patterns, and relationships using quantitative data . It is an important research tool used by scientists, governments, businesses, and other organizations.

To draw valid conclusions, statistical analysis requires careful planning from the very start of the research process . You need to specify your hypotheses and make decisions about your research design, sample size, and sampling procedure.

After collecting data from your sample, you can organize and summarize the data using descriptive statistics . Then, you can use inferential statistics to formally test hypotheses and make estimates about the population. Finally, you can interpret and generalize your findings.

This article is a practical introduction to statistical analysis for students and researchers. We’ll walk you through the steps using two research examples. The first investigates a potential cause-and-effect relationship, while the second investigates a potential correlation between variables.

Table of contents

Step 1: write your hypotheses and plan your research design, step 2: collect data from a sample, step 3: summarize your data with descriptive statistics, step 4: test hypotheses or make estimates with inferential statistics, step 5: interpret your results, other interesting articles.

To collect valid data for statistical analysis, you first need to specify your hypotheses and plan out your research design.

Writing statistical hypotheses

The goal of research is often to investigate a relationship between variables within a population . You start with a prediction, and use statistical analysis to test that prediction.

A statistical hypothesis is a formal way of writing a prediction about a population. Every research prediction is rephrased into null and alternative hypotheses that can be tested using sample data.

While the null hypothesis always predicts no effect or no relationship between variables, the alternative hypothesis states your research prediction of an effect or relationship.

• Null hypothesis: A 5-minute meditation exercise will have no effect on math test scores in teenagers.
• Alternative hypothesis: A 5-minute meditation exercise will improve math test scores in teenagers.
• Null hypothesis: Parental income and GPA have no relationship with each other in college students.
• Alternative hypothesis: Parental income and GPA are positively correlated in college students.

Planning your research design

A research design is your overall strategy for data collection and analysis. It determines the statistical tests you can use to test your hypothesis later on.

First, decide whether your research will use a descriptive, correlational, or experimental design. Experiments directly influence variables, whereas descriptive and correlational studies only measure variables.

• In an experimental design , you can assess a cause-and-effect relationship (e.g., the effect of meditation on test scores) using statistical tests of comparison or regression.
• In a correlational design , you can explore relationships between variables (e.g., parental income and GPA) without any assumption of causality using correlation coefficients and significance tests.
• In a descriptive design , you can study the characteristics of a population or phenomenon (e.g., the prevalence of anxiety in U.S. college students) using statistical tests to draw inferences from sample data.

Your research design also concerns whether you’ll compare participants at the group level or individual level, or both.

• In a between-subjects design , you compare the group-level outcomes of participants who have been exposed to different treatments (e.g., those who performed a meditation exercise vs those who didn’t).
• In a within-subjects design , you compare repeated measures from participants who have participated in all treatments of a study (e.g., scores from before and after performing a meditation exercise).
• In a mixed (factorial) design , one variable is altered between subjects and another is altered within subjects (e.g., pretest and posttest scores from participants who either did or didn’t do a meditation exercise).
• Experimental
• Correlational

First, you’ll take baseline test scores from participants. Then, your participants will undergo a 5-minute meditation exercise. Finally, you’ll record participants’ scores from a second math test.

In this experiment, the independent variable is the 5-minute meditation exercise, and the dependent variable is the math test score from before and after the intervention. Example: Correlational research design In a correlational study, you test whether there is a relationship between parental income and GPA in graduating college students. To collect your data, you will ask participants to fill in a survey and self-report their parents’ incomes and their own GPA.

Measuring variables

When planning a research design, you should operationalize your variables and decide exactly how you will measure them.

For statistical analysis, it’s important to consider the level of measurement of your variables, which tells you what kind of data they contain:

• Categorical data represents groupings. These may be nominal (e.g., gender) or ordinal (e.g. level of language ability).
• Quantitative data represents amounts. These may be on an interval scale (e.g. test score) or a ratio scale (e.g. age).

Many variables can be measured at different levels of precision. For example, age data can be quantitative (8 years old) or categorical (young). If a variable is coded numerically (e.g., level of agreement from 1–5), it doesn’t automatically mean that it’s quantitative instead of categorical.

Identifying the measurement level is important for choosing appropriate statistics and hypothesis tests. For example, you can calculate a mean score with quantitative data, but not with categorical data.

In a research study, along with measures of your variables of interest, you’ll often collect data on relevant participant characteristics.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

In most cases, it’s too difficult or expensive to collect data from every member of the population you’re interested in studying. Instead, you’ll collect data from a sample.

Statistical analysis allows you to apply your findings beyond your own sample as long as you use appropriate sampling procedures . You should aim for a sample that is representative of the population.

Sampling for statistical analysis

There are two main approaches to selecting a sample.

• Probability sampling: every member of the population has a chance of being selected for the study through random selection.
• Non-probability sampling: some members of the population are more likely than others to be selected for the study because of criteria such as convenience or voluntary self-selection.

In theory, for highly generalizable findings, you should use a probability sampling method. Random selection reduces several types of research bias , like sampling bias , and ensures that data from your sample is actually typical of the population. Parametric tests can be used to make strong statistical inferences when data are collected using probability sampling.

But in practice, it’s rarely possible to gather the ideal sample. While non-probability samples are more likely to at risk for biases like self-selection bias , they are much easier to recruit and collect data from. Non-parametric tests are more appropriate for non-probability samples, but they result in weaker inferences about the population.

If you want to use parametric tests for non-probability samples, you have to make the case that:

• your sample is representative of the population you’re generalizing your findings to.
• your sample lacks systematic bias.

Keep in mind that external validity means that you can only generalize your conclusions to others who share the characteristics of your sample. For instance, results from Western, Educated, Industrialized, Rich and Democratic samples (e.g., college students in the US) aren’t automatically applicable to all non-WEIRD populations.

If you apply parametric tests to data from non-probability samples, be sure to elaborate on the limitations of how far your results can be generalized in your discussion section .

Create an appropriate sampling procedure

Based on the resources available for your research, decide on how you’ll recruit participants.

• Will you have resources to advertise your study widely, including outside of your university setting?
• Will you have the means to recruit a diverse sample that represents a broad population?
• Do you have time to contact and follow up with members of hard-to-reach groups?

Your participants are self-selected by their schools. Although you’re using a non-probability sample, you aim for a diverse and representative sample. Example: Sampling (correlational study) Your main population of interest is male college students in the US. Using social media advertising, you recruit senior-year male college students from a smaller subpopulation: seven universities in the Boston area.

Calculate sufficient sample size

Before recruiting participants, decide on your sample size either by looking at other studies in your field or using statistics. A sample that’s too small may be unrepresentative of the sample, while a sample that’s too large will be more costly than necessary.

There are many sample size calculators online. Different formulas are used depending on whether you have subgroups or how rigorous your study should be (e.g., in clinical research). As a rule of thumb, a minimum of 30 units or more per subgroup is necessary.

To use these calculators, you have to understand and input these key components:

• Significance level (alpha): the risk of rejecting a true null hypothesis that you are willing to take, usually set at 5%.
• Statistical power : the probability of your study detecting an effect of a certain size if there is one, usually 80% or higher.
• Expected effect size : a standardized indication of how large the expected result of your study will be, usually based on other similar studies.
• Population standard deviation: an estimate of the population parameter based on a previous study or a pilot study of your own.

Once you’ve collected all of your data, you can inspect them and calculate descriptive statistics that summarize them.

Inspect your data

There are various ways to inspect your data, including the following:

• Organizing data from each variable in frequency distribution tables .
• Displaying data from a key variable in a bar chart to view the distribution of responses.
• Visualizing the relationship between two variables using a scatter plot .

By visualizing your data in tables and graphs, you can assess whether your data follow a skewed or normal distribution and whether there are any outliers or missing data.

A normal distribution means that your data are symmetrically distributed around a center where most values lie, with the values tapering off at the tail ends.

In contrast, a skewed distribution is asymmetric and has more values on one end than the other. The shape of the distribution is important to keep in mind because only some descriptive statistics should be used with skewed distributions.

Extreme outliers can also produce misleading statistics, so you may need a systematic approach to dealing with these values.

Calculate measures of central tendency

Measures of central tendency describe where most of the values in a data set lie. Three main measures of central tendency are often reported:

• Mode : the most popular response or value in the data set.
• Median : the value in the exact middle of the data set when ordered from low to high.
• Mean : the sum of all values divided by the number of values.

However, depending on the shape of the distribution and level of measurement, only one or two of these measures may be appropriate. For example, many demographic characteristics can only be described using the mode or proportions, while a variable like reaction time may not have a mode at all.

Calculate measures of variability

Measures of variability tell you how spread out the values in a data set are. Four main measures of variability are often reported:

• Range : the highest value minus the lowest value of the data set.
• Interquartile range : the range of the middle half of the data set.
• Standard deviation : the average distance between each value in your data set and the mean.
• Variance : the square of the standard deviation.

Once again, the shape of the distribution and level of measurement should guide your choice of variability statistics. The interquartile range is the best measure for skewed distributions, while standard deviation and variance provide the best information for normal distributions.

Using your table, you should check whether the units of the descriptive statistics are comparable for pretest and posttest scores. For example, are the variance levels similar across the groups? Are there any extreme values? If there are, you may need to identify and remove extreme outliers in your data set or transform your data before performing a statistical test.

From this table, we can see that the mean score increased after the meditation exercise, and the variances of the two scores are comparable. Next, we can perform a statistical test to find out if this improvement in test scores is statistically significant in the population. Example: Descriptive statistics (correlational study) After collecting data from 653 students, you tabulate descriptive statistics for annual parental income and GPA.

It’s important to check whether you have a broad range of data points. If you don’t, your data may be skewed towards some groups more than others (e.g., high academic achievers), and only limited inferences can be made about a relationship.

A number that describes a sample is called a statistic , while a number describing a population is called a parameter . Using inferential statistics , you can make conclusions about population parameters based on sample statistics.

Researchers often use two main methods (simultaneously) to make inferences in statistics.

• Estimation: calculating population parameters based on sample statistics.
• Hypothesis testing: a formal process for testing research predictions about the population using samples.

You can make two types of estimates of population parameters from sample statistics:

• A point estimate : a value that represents your best guess of the exact parameter.
• An interval estimate : a range of values that represent your best guess of where the parameter lies.

If your aim is to infer and report population characteristics from sample data, it’s best to use both point and interval estimates in your paper.

You can consider a sample statistic a point estimate for the population parameter when you have a representative sample (e.g., in a wide public opinion poll, the proportion of a sample that supports the current government is taken as the population proportion of government supporters).

There’s always error involved in estimation, so you should also provide a confidence interval as an interval estimate to show the variability around a point estimate.

A confidence interval uses the standard error and the z score from the standard normal distribution to convey where you’d generally expect to find the population parameter most of the time.

Hypothesis testing

Using data from a sample, you can test hypotheses about relationships between variables in the population. Hypothesis testing starts with the assumption that the null hypothesis is true in the population, and you use statistical tests to assess whether the null hypothesis can be rejected or not.

Statistical tests determine where your sample data would lie on an expected distribution of sample data if the null hypothesis were true. These tests give two main outputs:

• A test statistic tells you how much your data differs from the null hypothesis of the test.
• A p value tells you the likelihood of obtaining your results if the null hypothesis is actually true in the population.

Statistical tests come in three main varieties:

• Comparison tests assess group differences in outcomes.
• Regression tests assess cause-and-effect relationships between variables.
• Correlation tests assess relationships between variables without assuming causation.

Your choice of statistical test depends on your research questions, research design, sampling method, and data characteristics.

Parametric tests

Parametric tests make powerful inferences about the population based on sample data. But to use them, some assumptions must be met, and only some types of variables can be used. If your data violate these assumptions, you can perform appropriate data transformations or use alternative non-parametric tests instead.

A regression models the extent to which changes in a predictor variable results in changes in outcome variable(s).

• A simple linear regression includes one predictor variable and one outcome variable.
• A multiple linear regression includes two or more predictor variables and one outcome variable.

Comparison tests usually compare the means of groups. These may be the means of different groups within a sample (e.g., a treatment and control group), the means of one sample group taken at different times (e.g., pretest and posttest scores), or a sample mean and a population mean.

• A t test is for exactly 1 or 2 groups when the sample is small (30 or less).
• A z test is for exactly 1 or 2 groups when the sample is large.
• An ANOVA is for 3 or more groups.

The z and t tests have subtypes based on the number and types of samples and the hypotheses:

• If you have only one sample that you want to compare to a population mean, use a one-sample test .
• If you have paired measurements (within-subjects design), use a dependent (paired) samples test .
• If you have completely separate measurements from two unmatched groups (between-subjects design), use an independent (unpaired) samples test .
• If you expect a difference between groups in a specific direction, use a one-tailed test .
• If you don’t have any expectations for the direction of a difference between groups, use a two-tailed test .

The only parametric correlation test is Pearson’s r . The correlation coefficient ( r ) tells you the strength of a linear relationship between two quantitative variables.

However, to test whether the correlation in the sample is strong enough to be important in the population, you also need to perform a significance test of the correlation coefficient, usually a t test, to obtain a p value. This test uses your sample size to calculate how much the correlation coefficient differs from zero in the population.

You use a dependent-samples, one-tailed t test to assess whether the meditation exercise significantly improved math test scores. The test gives you:

• a t value (test statistic) of 3.00
• a p value of 0.0028

Although Pearson’s r is a test statistic, it doesn’t tell you anything about how significant the correlation is in the population. You also need to test whether this sample correlation coefficient is large enough to demonstrate a correlation in the population.

A t test can also determine how significantly a correlation coefficient differs from zero based on sample size. Since you expect a positive correlation between parental income and GPA, you use a one-sample, one-tailed t test. The t test gives you:

• a t value of 3.08
• a p value of 0.001

Prevent plagiarism. Run a free check.

The final step of statistical analysis is interpreting your results.

Statistical significance

In hypothesis testing, statistical significance is the main criterion for forming conclusions. You compare your p value to a set significance level (usually 0.05) to decide whether your results are statistically significant or non-significant.

Statistically significant results are considered unlikely to have arisen solely due to chance. There is only a very low chance of such a result occurring if the null hypothesis is true in the population.

This means that you believe the meditation intervention, rather than random factors, directly caused the increase in test scores. Example: Interpret your results (correlational study) You compare your p value of 0.001 to your significance threshold of 0.05. With a p value under this threshold, you can reject the null hypothesis. This indicates a statistically significant correlation between parental income and GPA in male college students.

Note that correlation doesn’t always mean causation, because there are often many underlying factors contributing to a complex variable like GPA. Even if one variable is related to another, this may be because of a third variable influencing both of them, or indirect links between the two variables.

Effect size

A statistically significant result doesn’t necessarily mean that there are important real life applications or clinical outcomes for a finding.

In contrast, the effect size indicates the practical significance of your results. It’s important to report effect sizes along with your inferential statistics for a complete picture of your results. You should also report interval estimates of effect sizes if you’re writing an APA style paper .

With a Cohen’s d of 0.72, there’s medium to high practical significance to your finding that the meditation exercise improved test scores. Example: Effect size (correlational study) To determine the effect size of the correlation coefficient, you compare your Pearson’s r value to Cohen’s effect size criteria.

Decision errors

Type I and Type II errors are mistakes made in research conclusions. A Type I error means rejecting the null hypothesis when it’s actually true, while a Type II error means failing to reject the null hypothesis when it’s false.

You can aim to minimize the risk of these errors by selecting an optimal significance level and ensuring high power . However, there’s a trade-off between the two errors, so a fine balance is necessary.

Frequentist versus Bayesian statistics

Traditionally, frequentist statistics emphasizes null hypothesis significance testing and always starts with the assumption of a true null hypothesis.

However, Bayesian statistics has grown in popularity as an alternative approach in the last few decades. In this approach, you use previous research to continually update your hypotheses based on your expectations and observations.

Bayes factor compares the relative strength of evidence for the null versus the alternative hypothesis rather than making a conclusion about rejecting the null hypothesis or not.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval

Methodology

• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hostile attribution bias
• Affect heuristic

Is this article helpful?

Other students also liked.

• Descriptive Statistics | Definitions, Types, Examples
• Inferential Statistics | An Easy Introduction & Examples
• Choosing the Right Statistical Test | Types & Examples

More interesting articles

• Akaike Information Criterion | When & How to Use It (Example)
• An Easy Introduction to Statistical Significance (With Examples)
• An Introduction to t Tests | Definitions, Formula and Examples
• ANOVA in R | A Complete Step-by-Step Guide with Examples
• Central Limit Theorem | Formula, Definition & Examples
• Central Tendency | Understanding the Mean, Median & Mode
• Chi-Square (Χ²) Distributions | Definition & Examples
• Chi-Square (Χ²) Table | Examples & Downloadable Table
• Chi-Square (Χ²) Tests | Types, Formula & Examples
• Chi-Square Goodness of Fit Test | Formula, Guide & Examples
• Chi-Square Test of Independence | Formula, Guide & Examples
• Coefficient of Determination (R²) | Calculation & Interpretation
• Correlation Coefficient | Types, Formulas & Examples
• Frequency Distribution | Tables, Types & Examples
• How to Calculate Standard Deviation (Guide) | Calculator & Examples
• How to Calculate Variance | Calculator, Analysis & Examples
• How to Find Degrees of Freedom | Definition & Formula
• How to Find Interquartile Range (IQR) | Calculator & Examples
• How to Find Outliers | 4 Ways with Examples & Explanation
• How to Find the Geometric Mean | Calculator & Formula
• How to Find the Mean | Definition, Examples & Calculator
• How to Find the Median | Definition, Examples & Calculator
• How to Find the Mode | Definition, Examples & Calculator
• How to Find the Range of a Data Set | Calculator & Formula
• Hypothesis Testing | A Step-by-Step Guide with Easy Examples
• Interval Data and How to Analyze It | Definitions & Examples
• Levels of Measurement | Nominal, Ordinal, Interval and Ratio
• Linear Regression in R | A Step-by-Step Guide & Examples
• Missing Data | Types, Explanation, & Imputation
• Multiple Linear Regression | A Quick Guide (Examples)
• Nominal Data | Definition, Examples, Data Collection & Analysis
• Normal Distribution | Examples, Formulas, & Uses
• Null and Alternative Hypotheses | Definitions & Examples
• One-way ANOVA | When and How to Use It (With Examples)
• Ordinal Data | Definition, Examples, Data Collection & Analysis
• Parameter vs Statistic | Definitions, Differences & Examples
• Pearson Correlation Coefficient (r) | Guide & Examples
• Poisson Distributions | Definition, Formula & Examples
• Probability Distribution | Formula, Types, & Examples
• Quartiles & Quantiles | Calculation, Definition & Interpretation
• Ratio Scales | Definition, Examples, & Data Analysis
• Simple Linear Regression | An Easy Introduction & Examples
• Skewness | Definition, Examples & Formula
• Statistical Power and Why It Matters | A Simple Introduction
• Student's t Table (Free Download) | Guide & Examples
• T-distribution: What it is and how to use it
• Test statistics | Definition, Interpretation, and Examples
• The Standard Normal Distribution | Calculator, Examples & Uses
• Two-Way ANOVA | Examples & When To Use It
• Type I & Type II Errors | Differences, Examples, Visualizations
• Understanding Confidence Intervals | Easy Examples & Formulas
• Understanding P values | Definition and Examples
• Variability | Calculating Range, IQR, Variance, Standard Deviation
• What is Effect Size and Why Does It Matter? (Examples)
• What Is Kurtosis? | Definition, Examples & Formula
• What Is Standard Error? | How to Calculate (Guide with Examples)

What is your plagiarism score?

• Customer Feedback Analytics
• Integrations
• Data Sources
• Marketing Intelligence
• Sentiment Analysis
• Ticket Driver Report
• AI Chatbot Preparation
• For Voice of Customer Teams
• For Product Operations Teams
• For Customer Support Teams
• For Customer Experience Teams
• Our Story and Team
• Customer Reviews
• Partner Program
• Start for Free
• Request Demo

How to analyze survey data: A step-by-step guide

The best insights you can get for your business are obtained through customer surveys. They go beyond your sales revenue and goals and look deeper into what your customers like and dislike about your products, services, and brand as a whole.

The act of collecting customer satisfaction survey results is the first step. Next, you need to understand how to analyze, summarize, and present this survey data to provide actionable insights you can use to make changes to scale and grow your business.

In this step-by-step guide, you’ll learn how to analyze survey data, including our 8-step process for performing a detailed survey data analysis:

Step 1: Start with your end goal

Decide your data collection method, determine sample size, distribute survey, step 3: remove incomplete data, step 4: check if data is statistically significant, how do you evaluate a survey, what statistical analysis should i use for questionnaires, survey analysis for quantitative data, which methods are used to analyze survey results, coding qualitative data, turn non-binary responses into binary responses, how do you present survey data, how do you tabulate survey results, correlation vs. causation, create a narrative, step 8: summarize and present findings, how do you summarize survey results, using machine learning for survey data analysis.

Unlike a good book, always start at the end when planning a satisfaction survey. This will help ensure that when you get to the feedback analysis stage, you’re likely to have the data you need.

Your survey goals could include:

• To better understand your brand recognition and trust in the marketplace
• To see if newly launched products or features are useful
• To plan your product lifecycle based on understanding user needs and wants
• To create a benchmark for future business growth

Once you have your goal, you can write questions that relate to that goal.

Step 2: Conduct your survey

How you write and distribute your survey will impact the data you have to analyze when it’s done:

There are many types of surveys you can use to collect your data. Your choice will depend on your end goal (which you created in step 1) and the available resources to analyze collected data. If you use a machine learning method for survey analysis, you have more options, but if you plan to do manual survey analysis, you’ll want to choose your data collection method carefully:

• Interview-style : You have a standard list of survey questions verbally given to the respondents. You have someone writing down or recording their answers through video or audio.
• Focus groups and observations : Focus groups work well for observing how people interact with a product in addition to answering specific questions about it. With focus groups, the respondents’ answers could be influenced by hearing the responses of others, so keep that in mind when analyzing your data. Regardless, the qualitative observational data it provides can be quite valuable if that relates to your survey goals.
• Written survey : You can also send paper or online surveys. A benefit of online surveys is that your data is already entered into a spreadsheet or downloadable data set, so you’ll save time inputting data into a computer for data analysis.

Any feedback is good, but if you’re surveying to understand the larger picture, you want your sample respondent size to represent the whole. For example, if you have 100 customers but only survey 5 of them, their responses may not be an accurate representation of how the majority of customers feel.

You can do complex calculations (such as Slovin’s formula ) to determine your exact sample size, but an industry average is usually a maximum of 10% of your population.

Then, take an educated guess to determine how likely people are to do your survey. Do you have a vocal, engaged group willing to provide feedback and expect most people to respond? Or do you feel uptake will be lower at a 10% response rate? Take that and send out enough surveys, so you will likely get your 10% response rate.

When you have designed your survey and determined how many people you need to send it to, it’s time to begin distributing or administering your survey. For the highest response rate and most accurate responses, ensure it’s clearly written, and there is no ambiguity in the questions.

Online surveys are ideal because the data doesn’t need manual digitization. Your survey questions and answers are already in the computer. It’s much easier to manage online surveys rather than paper ones when you can.

Unless it’s an ongoing survey (like one that’s sent with customers’ digital purchase receipts), have a clear deadline for collecting responses.

Once you’ve collected all your data, it’s time to put it into a format to make it easy to do your survey analysis. Often this means two parts:

• Entering quantitative data into a spreadsheet
• Coding qualitative data so it’s more easily summarized and interpreted.

Then, review your data and determine if any incomplete or irrelevant data can be excluded from your final survey report.

For example: Let’s say you send a survey to 100 people. You ask people if they feel the price of your product is fair with a yes/no response. If 50 people leave that question blank (unanswered), but 40 people say “yes,” and 10 say “no,” you can’t say 40% (of 100 people) answered yes. Instead, it’s actually 40 of 50 people who responded positively, which is 80%. In this case, a non-response doesn’t automatically indicate either of the options so it’s considered incomplete data and shouldn’t be considered in your totals or calculation as either “yes” or “no.”

When sharing this data, you should distinguish that 80% (of 50 respondents) responded this way. Or, you must clarify that 10% said no, 40% said yes, and 50% were undecided or non-responsive.

TIP: If you’ve put this data into a spreadsheet, you can filter out non-responses by writing the following formula into your cell:

=COUNTBLANK(first_cell:last_cell)

“First cell” and “last cell” represent the range of data you want to check. The result will calculate the total number of empty cells (aka non-responses), so you can subtract them from your sums and averages for that question.

This step may be optional, depending on the goals of your survey. We know that any data is helpful, whether it’s statistically significant or not; however, if you want to get the general sentiment or opinion of your entire audience, ensure you’ve collected enough data so it’s representative of the whole.

If it’s not, you can still get some valuable data, especially if you asked qualitative questions.

Step 5: Analyze and evaluate data

When you’re confident you have enough information to satisfy your goal, it’s time to analyze your survey data.

At its core, there are two ways to analyze survey data: Manually or by using algorithms and machine learning . Both will use the following types of data analysis on your survey results.

Your choice of data analysis methods will depend on the data you have collected and what you hope to learn from the data. Here are two popular methods of analyzing your statistical survey data:

Regression analysis : This type of statistical analysis looks at the relationship between two or more variables in your survey data. It looks at how one variable affects (or influences) another variable.

For example, you may wish to know what factors contributed to someone being satisfied (or dissatisfied with their purchase of your product:

• By looking at other dependent variables, you may see that those who were happy with the purchase were more likely to note that they loved the extra product support they received.
• By looking at those who were unsatisfied with the product, respondents also often mentioned that they liked its packaging but said using the device operation was confusing.

This is a great way to start developing a narrative around your data and learn how to use it to inform business decisions based on how your customers (or survey respondents) feel.

Longitudinal analysis: When analyzing survey data from a single data collection source (like a survey), you get a good picture of your current state. And, examining the same data over a longer time helps you identify trends and growth (or recession). This is called a longitudinal analysis: how responses to specific questions change.

To do this ethically, you must use the same questions in more than one survey distribution. This works excellently for surveys after an annual event or conference. It can help you see which events were perceived by attendees as successful. If you do quarterly surveys to measure customers’ satisfaction with your product, you may find that people enjoy your product more in the summer than in the winter.

With this information, you can better understand trends in your niche and use that information to uncover the reasons behind those ebbs and flows. When you know what influences these trends, you can use your survey data analysis to inform business decisions to help predict them and prepare accordingly.

Analyzing survey data that contains quantitative data (numerically based data) is often quicker and easier to understand and draw conclusions. There are several ways to collect this numerical data for survey analysis, including:

The nominal scale: Respondents choose the best response from a list of given labels. These questions ask people to “choose from the following,” then follow with a list of unrelated items for the respondent. The responses can be limited to one answer per question or can allow for multiple answers. You can then use the data to discover which the most popular answers were, based on how many people selected that option:

When analyzing this data you simply add up each response to get your totals.

Ordinal scale asks people to rank their value or agreement with a given statement or question : Usually, these are ranked on a scale of 1-10 but could be 1-5 or a similar ranked sequence of responses:

When analyzing this type of survey data, you can calculate the percentage of people who responded to each answer.

Interval scales are questions in which the responses include a scale with equal distances (known as intervals) between each value. There is no true zero in this type of question. A basic example of an interval scale question is:

As you can see in the above example, each of these represents the different levels of education, in order. Based on the responses to this question, you can calculate the average education level of your respondents.

Ratio scales are similar to interval scales but have a true zero . It’s a form of quantitative data. Here is an example:

You can see that each age group is the same (9 years).

When analyzing survey results, you can also do three other simple calculations to analyze the data:

• Mode : Mode determines the most common answer from your survey respondents. You can calculate the mode of any numerical dataset in Excel by typing =MODE() and then select the cells that contain the data you are trying to summarize.
• Mean : Mean is the statistical way to say average. This mean value will give you the “typical” response to a question. You can calculate the mean average of any numerical dataset in Excel by typing =Average() and then select the cells that contain the data you are trying to summarize.
• NPS : If you are doing a Net Promoter Score (NPS) survey , you can calculate your Net Promoter Score (NPS) from your data. To calculate your NPS score , you use the following formula.

👉 Learn more: How To Do Insightful Customer Review Analysis

Survey analysis for qualitative data

Analyzing qualitative (non-numerical) data is much different and often more time-consuming than quantitative data. When reviewing qualitative survey data, there are two ways to get valuable insights from this data:

You can measure customer satisfaction using sentiments. This means drawing conclusions about your respondents’ answers and expressing their sentiments. It can be as simple as a positive or negative sentiment or multiple levels of like or dislike. It’s about categorizing the answers to open-ended questions into themes (in this case, emotional responses).

For example, you can interpret the response “I love your product” as positive sentiment and “The price was too high” as a negative sentiment.

From this, you turn the response to open-ended questions into an answer that can be analyzed numerically (i.e., the percentage of people who are happy with the product).

Coding qualitative data means assigning categories or values to each written or observed response. These values can then be added and averaged to determine an accurate overall representation of each area of your business you are analyzing.

Once you have all your qualitative data quantified, you can calculate and begin drawing conclusions about the data.

Often your survey will ask yes/no questions or questions where there are only two options. To assist with your data analysis, do a find-and-replace in your spreadsheet to turn these options into binary responses (1s and 0s).

For example, if you ask, “Did you buy this product online?” in your spreadsheet, turn all “yes” responses to a 1 and all “no” answers to 0. With this data, you can easily calculate the SUM of that column to indicate how many people said yes to that question.

Step 6: Compare data to benchmarks or historical data

Once you’ve got your survey data in a readily accessible format, it’s time to compare survey results to industry benchmarks or historical data from your organization. You can skip this step if you don’t have access to any benchmarks. With this information, you can get a snapshot of your progress in certain areas of your business.

For example, for the past two years, you’ve sent every customer a brief customer satisfaction survey with their purchase receipt. One year ago you presented a big report to senior management about where people were dissatisfied with your product offering. Now, you can take the data from last year and compare it side-by-side with your data from two years ago to see if your scores have improved. This can also point out areas of decline.

If your company doesn’t have comparable data to analyze your current survey data against, you can look for any industry benchmarks available. For example, your financial services company subscribes to an industry association that did its own study and found that 25-30% of all customers in the industry generally become repeat customers. With this data in hand, you can compare it to your returning customer score to see how you compare.

When presenting current survey data with past survey data, be sure to note which are the recent scores. Then you can visually illustrate your findings via graphs, charts, or narrative.

Using cross-tabulation and filters, you can better analyze and compare your results. For example, if you’ve sent out a survey to three key audiences, and you want to compare how they answered each question, you can tabulate your responses to make it easier to see your results:

Q: Would you attend this community event again?

If you analyzed individual questions, 51% of all respondents said they would attend this event again. Looking at this result, it appears that the event was not very successful if your goal was for people to return to future events; however, diving into the data deeper, you can see that 80% of moms loved the event. This means that if you cater your next event more towards moms, you could expect higher overall average satisfaction ratings next time.

You can also use data analysis to look at the relationship or influence between survey questions to see if they are correlations or causations.

A correlation is when two variables move together . They do not influence each other but move up or down in similar paths. They are caused by a third factor which may or may not be something your survey will uncover.

A simple example of this would be sales. When you sell more mittens, you usually sell more scarves. This is influenced by the season (not something your survey will bring to your attention as it wasn’t a survey question). The result is knowing that your business typically sells more of these products when the weather gets colder (the third variable)

Causation is when one variable affects the other . For example, your data may show that when your sales of alcohol go up, guest satisfaction also goes up. If you had no other data points, you could assume that the more people drink, the more they enjoy their time at your event.

Step 7: Explain results with qualitative data

Going beyond just the numerical data, you can use your qualitative data to help explain your results in your survey report. This enables you to build a narrative (story) around the data to better understand what it’s telling you to get more actionable information.

Knowing people are satisfied with your product is a good first metric to learn. The next step is to use qualitative data to find out what they liked about it and what they don’t like, if it exists.

Explaining your results by telling a story can be a helpful way to present your findings, and qualitative data can help you build this story.

A comprehensive survey data analysis can help you create narratives better to understand areas of your business, including:

• Your target demographics or customer personas
• Your marketing funnel
• Your customer journey

Many people find that writing a survey report of your findings is easiest by creating a PowerPoint deck with numbers and stats. We recommend a mix of data and insights (aka what the data means).

A basic example is the data may tell you that 75% of customers don’t plan to renew their subscription when it expires. While this is an important stat, also share what that means: 75% of customers don’t intend to continue because of your recently announced price increase.

When summarizing your findings, try not to jump to conclusions. Look for multiple pieces of information to back up your assumptions before calling them a fact. For example, if 75% of people don’t plan to renew their subscription, and you also see a decrease in annual household income levels, it’s not necessarily a safe bet that household income is the reason (or the only reason) for the non-subscription renewals. If you acted on this assumption, you might miss vital data and the actual causation.

The actual reason people are not subscribing is more likely linked to the fact that your help desk has reported an increase in customer service inquiries in the past six months. Based on survey data, your customers are unhappy about your upcoming price increase. It’s more likely that these two factors are contributing to the low renewal rates. Finding the right causation between your data points is critical to know how to act.

Finding the right relationships and making the most accurate conclusions can be easier when you use machine learning and AI to analyze data from surveys and other connected systems (like helpdesk support tickets).

Once you have the data you want to share with your team, prepare a summarized version of the results and the raw data. This will help the “number nerds” who like the numbers and those who want the actionable insights or narrative.

Here are a few ways to summarize your survey results:

Visuals (Charts and graphs) : To present one to three survey questions, show them in a chart or graph. Avoid stuffing too much data into your charts, and always summarize the key findings from each chart.

Infographics : Infographics are great for those who process data best when it’s presented visually. Infographics also make for great sharing on social media if that’s something you’re planning to do with your results.

Compare with benchmarks and historical data : Create ways to compare your current survey data with either industry benchmarks, data from competitors, or your historical data. Use this to share what’s changed and predict future trends and performance.

Sentiment analysis : Knowing how your customers feel about your brand is critical. You can summarize your findings by customer sentiment to understand who your happy customers are and what makes them happy.

Use storytelling : Consider weaving your data and analysis into a narrative when sharing your results. Here’s an example:

“Our survey found that before customers bought XYZ Software, they spent too much time and energy manually analyzing their survey results. Most (80%) were referred to our company by someone already a customer. 

Once becoming a customer, these referred customers recorded fewer support ticket requests than other new customers, likely because they could troubleshoot any problems and get their answer from the person who referred them. However, nearly 92% of customers noted having issues reading the UI due to the color combination we’ve used. We predict that if we change the font color from yellow to a dark gray/black, we can increase customer satisfaction on the App.”

Manually analyzing your survey data can provide some great insights, but you can get more actionable insights using machine learning and AI. With a platform like Idiomatic helping you process and analyze survey data you:

• Spend less employee time doing manual survey analysis.
• Pull data from other systems (like attendance and registration systems, helpdesk systems, and customer databases) to look for additional correlations or causations in your data.
• Quickly analyze open-ended feedback into actionable insights

Idiomatic is an AI-driven customer intelligence platform that can help you make sense of your survey data by generating a survey report with real-time actionable insights. You get analyst-quality insights without waiting days or weeks for staff members to comb through your data. We help you unlock the “why” in your customer feedback from survey responses.

Here’s a video that shows how we do it.

Request a demo to learn more about how we can help you make sense of your survey results.

Request a demo

Chris Martinez

Co-Chief Executive Officer | Growth

Chris is obsessed with pushing Idiomatic to move faster in providing value to customers. Prior to Idiomatic, he co-founded Glow (15+ Million users, 40 countries). He has a BS in Math and Computer Science, a JD, and an MBA from Stanford. Outside of work, he can typically be found cooking, playing basketball (or really any other sport), or traveling with his wife and three children. His favorite quote is “fear is the mind-killer” from the novel Dune.

Survey Data Analysis: How to Analyze Survey Results

Running a customer feedback survey seems simple enough; you come up with a few questions, blast them out to everyone on your email lists, and get a bunch of data points to work with.

Though that process covers the basics, it’s a lot more difficult when you’re trying to make sure that the data you get is actually useful. This means data that is un-skewed, unbiased, and that you can draw meaningful conclusions from. You don’t have to be a data wizard in order to conduct accurate survey data analysis.

Below are simple steps to take before and after running a survey to improve the validity of your survey results for better customer experience analysis .

What is Survey Data Analysis?

Survey data analysis is the process of drawing conclusions from what you’ve gathered. Whether this is hard percentages, qualitative statements, or something in the middle, going through your data and identifying text or sentimental patterns can help you figure out wider takeaways for the general population the data represents.

For example, if you’re a restaurant running a customer feedback survey and you notice a pattern of people complaining that their food delivery is cold, you’ll probably be able to better understand why delivery orders have been less frequent.

Why is it Important to Conduct Survey Analysis?

Survey analysis is important because it allows you to draw broader conclusions about your audience. You can’t ask every single person what they think about your company and implement changes to suit every individual.

Numbers on their own are meaningless, it’s the trends and patterns you uncover that allow you to make meaningful decisions.

Note though, you can increase survey open and response rates by having engaging survey email subject lines .

Key Survey Analysis Variables

Though there are countless variables you could be measuring in your surveys, most of them can be categorized into the following two types. The intersection of these two types of variables is usually where the most valuable insights come from.

1. Demographic Data

Demographic data encompasses the specific characteristics of a given population. Examples of demographic data include: age, gender, location, income, and language along with many other traits that can be used to define a set of respondents.

For example, you could find that women aged 50+ give the most positive feedback for a certain product, and therefore shift your marketing tactics to target this type of audience with ads pushing that product.

2. Psychographic Data

Psychographic data can be a little more difficult to pin down, as it is typically qualitative vs demographic data which is quantitative.

Psychographic data includes a person’s opinions, feelings, and interests about different things in the world, and can be used to determine how they will respond to products and marketing efforts.

Two people may have identical demographic characteristics but very different psychographics, meaning they probably shouldn’t be placed in the same customer segment.

How to Collect Survey Data

The best survey data analysis in the world isn’t going to help you if the data you collect is inherently flawed.

A customer’s experience of a survey is a part of how they experience your business - it can be the definition of an easy win or you can shoot yourself in the foot. Below we outline best practices for gathering survey data to make sure it’s accurate and usable.

1. How to Ask

The first component in gathering accurate survey data is to formulate your questions in a specific way. We break down the key things to think about ahead of time:

Consistent Metrics: You will want to use consistent methods for measuring responses across your surveys. This goes for both surveys separated by time and by segment. For example, a survey sent out six months ago to men ages 20 - 30 should have similar metrics as a survey sent out one year ago to men aged 40+. This way, you can track changes over time and across different touchpoints.

Different Descriptors: Following up on the previous point, you’ll obviously need to word your survey questions differently depending on who you’re asking and what you’re asking them about. The important thing is to make sure that each question you ask can be boiled down to reveal the same consistent metrics across your data.

Numerical Scales: Whenever possible, respondents should be able to answer your question using some sort of scale vs a “yes or no” or “thumbs up/thumbs down” response. This could take the form of a 1 - 10 ranking, a “strongly agree to strongly disagree” scale, or anything else that makes sense. When it comes down to it, binary responses like “yes or no” don’t provide you very much insight, where numerical scales allow for greater depth and analysis.

Freeform vs Multiple-Choice: Using freeform or multiple-choice answers depends on the insights you are looking to gain. In general, free form questions offer more insight on the product and customer experience. Many respondents won’t be motivated enough to answer a long form multiple choice service to fully explain their thoughts. It's better to let the user explain what's on their mind using Open-ended questions , when implemented correctly, allow you to capture a much richer level of insight than multiple choice surveys whilst dramatically reducing the overall length of surveys.

Open vs Closed Questions: When asking for free form feedback, the question you ask should be open-ended, unless you have a very specific reason to ask a closed-ended question. This is because you risk shoe-horning your respondents into giving feedback that may not be accurate. For example, an open question such as “Please tell us about your experience” allows for a wide variety of responses, vs a closed question such as “What did you enjoy about your experience?” This closed-ended question assumes that the respondent did enjoy their experience, which may not actually be the case. It’s important to note that these types of questions will also need to be coded so that they match the metrics of all your other questions and can be compared equally.

Other Common Pitfalls: In addition to the points above, there are a number of other traps you can fall into when structuring your survey that could lead to biased responses. These include: asking leading questions, surveys that are too long, over-surveying people, and starting with an already-biased audience.

Take a look at our full guide on avoiding survey response bias to learn much more about how to properly write your survey questions.

2. When to Ask

Now that you know how to ask your survey questions, it’s important to understand the best points in the customer journey for when to survey your customers.

Key Milestone:  When a customer hits a key milestone in the customer journey, this presents a great opportunity to gain some valuable feedback about what your company is doing right. For example, after a customer initially signs up you could ask them what led them to choose your company. Or, if a customer is approaching their one-year membership renewal, you can check in with them to make sure everything is going well now that you’ve had a relationship for a while.

Falling Out of the Journey: If a customer cancels their subscription, hasn’t purchased a new product in several months, or some other type of customer churn, you should check in with a customer feedback survey to see if you can gather any insights on what they’re unhappy with. Take a look at our guide to improving customer retention and loyalty.

Customer Service Contact: After someone contacts your organisation, be it through live chat service,  a customer service call, or on social media, you may want to gather their feedback about how and if their issue was resolved. Not only will this help you understand why they needed to contact customer support in the first place, but can also identify any shortcomings in your customer service strategy. Read through our piece on social CX to learn more about what brands need to be doing to help customers on social media.

Interaction Without Conversion: This is the classic “abandoned cart” scenario where a customer spends time interacting with your business, but doesn’t actually convert. This can apply to more than failure to purchase a product or subscription, encompassing class or webinar sign-ups, transitioning from a free to a paid product, or whatever your specific conversion metrics are. Sending surveys at this point in the journey can help you understand why the customer didn’t convert, as well as remind them that the product or service is still waiting for them.

It’s important to note that whenever possible, surveys should be sent automatically based on a set of specific triggers, coordinated with the actions above. Having automated surveys can not only make things much easier for your customer service team, but also help to make sure that you’re not over-surveying the same individuals or sending them repeated surveys. Time-based rules and logic can be used as simple ways to avoid annoying customers and to only send surveys at mutually-exclusive touchpoints. Chattermill’s automated customer feedback workflows can help make this process easier.

How to Analyze Survey Data

We’ve finally gotten through all the essential preparation for gathering survey data, so what do we do when we actually have it? Below are five simple steps that any team can take to make sure they’re getting the most from their survey analysis.

1. What Do You Want to Know?

If you’re running a feedback survey, you should at least have some idea of what you’re hoping to get out of your data analysis.

Are you trying to find out why customers are leaving at a certain point in the customer journey, or which products appeal to a certain demographic?

Just like with any research project, you should come up with a set of research questions and a corresponding set of “theses” as answers to those questions.

Then, you can investigate each “thesis” accordingly to either confirm or augment your understanding of that research question.

2. What Do the Variables Tell You?

Now, it’s time to dig in and see what the intersection of demographic and psychographic data points have to tell you.

This is the fun part, where you get to see which cross-sections of people are reacting positively or negatively to your questions. Just remember, each time you drill down further into the data, you’re making the sample size smaller.

Women aged 30-32, who make more than $50K/year and love horseback riding, are going to comprise a small percentage of your overall population.

This is why it’s so important to make sure each data set you’re analyzing remains statistically significant (we’ll get to that later).

For example, say you asked the question “What do you think of our new chatbot?” Right off the bat, you notice a large percentage of answers that are coded as negative responses.

Then, you drill down into the negative responses, and find that 90% of the negative responders are age 65+.

Looking even further, you see that 95% of negative respondents, age 65+, make an income of over $200K. After making sure that this slice of your data is still statistically significant, you can determine that rich older customers don’t like your new chatbot.                                                

3. Which Patterns Stand Out?

When you dig into the cross-sections of different variables, you will start seeing patterns. These demographic and psychographic patterns are the key to survey data analysis.

When you identify an overall theme or trend within the data, and confirm that the sample you’re looking at remains statistically significant, you can apply that pattern with reasonable confidence to the rest of your audience to draw broader conclusions.

You will want to start looking for patterns that relate to your original research questions and theses, to see if you’re able to confirm any of your initial thoughts.

After that, you can start looking for other high-level patterns, and then drill down further into each pattern to see if you can glean more in-depth insights. Here are a few questions you can use to get started if you’re not quite sure what to look for in your big data set:

• What themes stand out right away? For example, a larger-than-expected amount of negative or positive responses to something.
• How do the responses of young people compare to older people? From men to women? From wealthy to poor? From location to location?
• What are respondents saying about your new product/service?
• Which responses are surprising you? Can you drill down further to figure out any patterns?
• What is the most common positive feedback? What is the most common negative feedback?

Once you go through these questions and start identifying high-level themes within the data, you’ll naturally be able to tell where you need to do more digging, or if you need to run another survey on a specific topic or demographic to gather more data.

4. Use AI to Scale Survey Data Analysis

Identifying themes at scale can be tough when you begin to grow your customer base. So adding AI into your arsenal of tools can be make a huge impact on your data analysis capabilities.

Try out our insight tagger below with a sample comment from one of your CX or support surveys and get a taste of our theme and sentiment analytics used by Uber, Transferwise, HelloFresh and many big name brands.

5. Is the Data Reliable?

Using raw data with no refinement can lead you to make assumptions about your general customer population that may not actually be accurate.

For that reason, it’s essential to make sure that the data you’ve collected can be used to make assumptions about the wider population. This is known as statistical significance .

In addition to making sure your segment is statistically significant, you should also keep in mind the demographic spread of your surveyed sample and how it compares to your total customer population.

For example, if your customers are spread mostly equally across income levels, but your sample mainly includes households making six figures, your sample is obviously not going to be an accurate representation of the whole when it comes to income level.                                                

There are a number of basic statistical terms and calculations that you should understand in order to make sure your survey data is reliable.

We’ve broken down each equation below, and have provided additional resources for each as well.

You should note that the calculators linked below are mainly useful for understanding the equations themselves, and large-scale analysis will likely need to be done with more complex tools.

Variance: As you might have guessed, variance calculates how widely the data points vary from one another. To find the variance, subtract the mean from each data point, then take each of those numbers and square them. Then, find the mean of all those squared differences.

Standard Deviation: Standard deviation calculates how spread out the data points are from the average. To find standard deviation, take the square root of the variance .

Z-Score: The Z-score shows how many standard deviations (from -3 to 3) a number is from the mean . Z-score is calculated by taking the number, subtracting the mean, and dividing by the standard deviation.

Confidence Level: The confidence level is a percentage indicating how certain you are that your results could be replicated. The industry standard for confidence level is typically 95% or greater. This means that you are confident that 95% of the time, your results could be replicated, and 5% of the time they would not be replicated. For simplicity’s sake, you can easily research your desired confidence level and its corresponding Z-score .

Margin of Error: Margin of error is calculated using the Z-score for your desired confidence level . The equation is the standard deviation , divided by the square root of the sample size , multiplied by the desired Z-score . For example, if your results show that 30% of users don’t like your company’s new branding, and your margin of error comes out to +/- 6% (based on a 95% confidence level), it could actually be anywhere from 24% - 36% of customers who don’t like the new branding.

Statistical Significance: Statistical significance is the likelihood that a result did not happen due to chance. Calculating statistical significance involves setting a null hypothesis (assuming no relationship between the things you’re comparing) and an alternative hypothesis (trying to prove a relationship between the things you’re comparing). Then, you use the variables above to determine if your hypothesis meets the 5% or less threshold (corresponding to the 95% confidence level). There are a number of complicated ways to calculate statistical significance, but luckily many calculators and free A/B tests are available to help you understand the process.

6. What Do You Do With the Results?

You’ve carefully constructed your survey to avoid response bias, sent it out and gathered feedback, identified patterns and insights within the data and ensured it’s statistically significant, so now what?

Now, you compile your takeaways and generate hypotheses as to how you can address these issues. Your new hypotheses may or may not be the same as your original “theses.” Maybe you were able to back up your original theories with data, or you discovered that the causes of a particular response are actually nothing like what you originally thought.

Collaborate and communicate across teams to validate your hypotheses. See if customer service reps have noticed similar complaints on client phone calls, or if social media managers deal with the same issues on their end. It’s important for all teams to contribute to these ideas, as an obvious fix to one team may be problematic for another.

Once your teams are aligned, you can start implementing changes that address your survey feedback.

Perhaps you found out that your younger audience found frequent email contact annoying, so you scale back your email marketing targeting that demographic. Or maybe your data revealed that enterprise-level clients found a particular feature of your software difficult to use, so you work on updating that feature based on their feedback.

After you’ve implemented these changes, you can re-survey your audience to see if your changes have been successful, and to learn about any other issues that need to be addressed.

And, so on and so forth! The value of survey data analysis is that you’ll always be able to work on improving your offerings and measuring results in order to stay ahead of the competition. By using AI to conduct your surveys, you can simplify this process by sending and analyzing data at scale.

AI allows questionnaires to be shorter without sacrificing valuable insight or bogging down your employees with endless work. ‍

Book a Chattermill Demo to learn about how our Customer Feedback Platform can help inform your survey analysis strategy .

Related articles.

Video Panel: How top brands use AI to impact customer experience

We were joined by more than 35 customers, partners, and friends who wanted to make sure that their choice of AI drives their business outcomes.

How NOT to ask for feedback - a real-life example from a £2B+ brand

We examine one of the best examples of how NOT to design a survey

Experience-Led Growth (XLG): The business strategy for sustainable and profitable growth

See chattermill in action.

Understand the voice of your customers in realtime with Customer Feedback Analytics from Chattermill.

Filter by Keywords

10 Best Survey Analysis Software in 2024

Praburam Srinivasan

Growth Marketing Manager

August 7, 2024

Start using ClickUp today

• Manage all your work in one place
• Collaborate with your team
• Use ClickUp for FREE—forever

Surveys are great for understanding customer sentiments, doing market research, or checking in on your team’s job satisfaction .

But running the survey is just the beginning. The real challenge is diving into the unstructured data and pulling out actionable insights. 

As the customer base and team size grew at ClickUp, we realized we needed powerful software to make sense of all the survey data. Doing it manually wasn’t cutting it—especially with large volumes of data.

So, we set out to find the perfect tool.

After testing dozens of survey data analysis platforms, we’ve narrowed down our top 10 favorites. These tools have been incredibly useful for our team, and I hope they’ll be just as helpful for you!

What Should You Look for in Survey Analysis Software? 

1. clickup—best for visualizing survey data, 2. surveysparrow—best for css customization , 3. survicate—best for product feedback surveys, 4. thematic—best for chat analysis with llms, 5. tableau—best for ai-assisted real-time data insights , 6. surveymonkey—best for detailed survey templates, 7. sentisum—best for support ticket analysis, 8. displayr—best for data cleaning , 9. crunch.io—best for quick survey data analysis, 10. q research software—best for automated updates , centralize survey creation and data analysis with clickup.

These are the key features I prioritize in a survey data analysis platform:

• Break insight silos: The tool should make survey data easy to understand for everyone in the team, irrespective of their technical expertise
• Advanced analytics : Features for segmentation, cross-tabulation, and trend analysis are must-haves for my workflow
• Customization options : It’s great to have a variety of graphs, charts, and customizable project dashboards to visualize data the way I want
• Integration capabilities : The software needs to seamlessly connect with the existing tools in my tech stack, such as Excel, CRM system, or BI platforms
• Survey customization : The tool must let me create complex surveys with various question types, branching logic, and customizable design options
• Support and Training : Having responsive customer support and comprehensive training resources would be an advantage. It’s reassuring to know there’s help available so my team and I can make the most of the software

The 10 Best Survey Analysis Software to Use in 2024

Keeping the above criteria in mind, here are our top picks:

ClickUp has two powerful tools to optimize the survey analysis process: the ClickUp Form View for creating customizable surveys and the ClickUp Dashboards for visualizing the survey data more clearly.  

The Form View helps me create custom forms to collect feedback from multiple sources—employees, customers, prospects, and other stakeholders. I can turn this feedback into trackable tasks in ClickUp and add it directly to my workflow. For instance, if a customer complains of a bug, I’ll create a task for our dev team to look into it on priority. 

What I love the most about the Form View is its flexibility. It’s not a static, cookie-cutter survey form. It understands conditional logic and dynamically updates the questions based on responses. It’s seamless to personalize the questions and capture relevant data. 

Now, moving on to the next step, I use ClickUp Dashboards to extract insights from the survey data. Custom cards, such as Pie Chart cards, Bar Chart cards, and Calculation chart cards (available in the Business plan and above), help me visualize task data the way I want. 

Here’s a quick glimpse of which cards help me track which metrics: 

These are just a few examples from the SaaS industry, which heavily revolves around marketing, sales, and customer support. There are plenty of other cards to help professionals from other industries, such as finance, quality assurance, healthcare, construction, and the list goes on. 

If I need specific information or insight related to the surveys, I use ClickUp Brain , ClickUp’s built-in, trusted AI co-pilot. It scans all the dashboards in my Workspace and gives me an answer instantly.

ClickUp best features

• Organize survey data (such as responses, participant information, and survey history) in a centralized repository
• Automate survey distribution based on predefined criteria (demographics, past interactions) and reach the right target audience at the right time
• Personalize survey campaigns based on participants’ preferences or feedback history and get meaningful responses
• Generate detailed reports on survey results, trends, and key metrics 
• Track participant engagement levels for surveys, monitor response rates and analyze completion times

ClickUp limitations

• The mobile app is yet to offer all the functionalities available in the web version 

ClickUp pricing

• Free Forever
• Unlimited : $7/month per user
• Business : $12/month per user
• Enterprise : Contact for pricing
• ClickUp Brain: Add to any paid plan for $7 per Workspace member per month

ClickUp ratings and reviews

• G2: 4.7/5 (9,000+ reviews)
• Capterra: 4.6/5 (4,000+ reviews)

Also read: 10 Best Dashboard Software (Reviews, Features, & Pricing)

I love to add a branded touch to marketing materials, so I was instantly drawn in by SurveySparrow’s CSS customizations. In addition to creating detailed surveys, I could personalize them using contact parameters and custom variables.  

I also liked the ease of sharing surveys—the tool gave me the option to create a direct web link for email or SMS, generate a scannable QR code, or embed the link as a widget on our website to collect customer feedback directly. 

SurveySparrow best features 

• Automate recurring surveys and reminder emails for partial responders and non-responders
• Host surveys from a white-labeled website 
• Receive survey reports via email at regular intervals 
• Export survey data in the SPSS (Statistical Package for the Social Sciences) format to conduct detailed statistical analysis

SurveySparrow limitations 

• The tool isn’t mobile-friendly 

SurveySparrow pricing 

• Basic: $19 (billed annually) 
• Starter: $39 (billed annually) 
• Business: $79 (billed annually) 
• Professional: $249 (billed annually) 
• CX Basic: $199 (billed annually) 
• NPS + CX Suite: Custom pricing

Pricing details sourced from G2  

SurveySparrow ratings 

• G2: 4.4/5 (1,900+ reviews) 
• Capterra: 4.4/5 (100+ reviews)

Survicate supports survey creation for multiple channels (email, website, in-app, etc.), but what stood out for me was the ability to create in-product surveys. 

Our team tried to collect feedback on specific features with event-triggered surveys (such as asking about the download experience after a user downloads a free template or enquiring about the import experience after they try importing raw data from a third-party tool). 

It worked well, I must say. Since the survey was specific and contextual, it also allowed us to understand the unique pain points of our customers (for instance, issues with importing/exporting for specific tools).

Survicate best features 

• Create branded surveys with an intuitive editor
• Collect continuous bug reports 
• Receive alerts on MS Teams/Slack and collaborate internally to resolve recurring issues 
• Connect Survicate with the existing tech stack using Zapier 

Survicate limitations 

• There’s no option to aggregate multiple survey results 

Survicate pricing 

• Business: $99/month 
• Scale: $299/month

Survicate ratings 

• G2: 4.6/5 (100+ reviews) 
• Capterra: 4.5/5 (20+ reviews) 

Also read: 10 Best Customer Feedback Tools  

While email surveys are useful to understand customer pain points, they usually have a low response rate. Thematic’s survey analysis software addresses this problem. 

It uses large language models (LLMs) to analyze chat interactions and predict customer satisfaction levels. I liked this thoughtful feature—it allowed me to accurately measure customer satisfaction (CSAT) scores for customers who had previously contacted our support team, even if they didn’t participate in our customer survey. 

Thematic best features 

• Automatically analyze data from phone calls, support chats, emails, and texts
• Discover the topics/themes driving call volume and average handle time (AHT) to serve your customers more efficiently
• Identify new and emerging issues in real-time with Thematic’s AI without waiting for survey results
• Generate reports with just a few clicks and share issues with stakeholders

Thematic limitations 

• The initial setup feels jittery 

Thematic pricing 

• Starter: $2000/month with unlimited viewing seats 
• Enterprise: Custom pricing 

Thematic ratings 

• G2: 4.8/5 (30+ reviews) 
• Capterra: Not enough reviews 

I loved Tableau Pulse, which offers personalized AI-assisted insights on your desired metrics. We ‘followed’ some metrics from our customer surveys, such as customer satisfaction (CSAT), net promoter score (NPS), churn rate, and customer lifetime value. 

With Pulse, we could get a summary of the key changes in those metrics. With just a single click, it even helped me dig deeper. I could conduct advanced statistical analysis and identify the exact change in numbers and spot outliers or anomalies. It also generated an FAQ-like section for each metric to help me understand the insights better. 

Tableau best features 

• Stay on top of key metrics from both mobile and desktop 
• Visually analyze survey data and turn it into shareable interactive reports 
• Filter survey items based on demographic profiles 
• Integrate seamlessly with Salesforce or Microsoft Office

Tableau limitations 

• Doesn’t auto-refresh reports based on new changes 
• The expensive pricing model makes it suitable only for bigger companies 

Tableau pricing

• Viewer: $15/month per user 
• Creator: $42/month per user 
• Explorer : $70/month per user 

Pricing details sourced from G2

Tableau ratings 

• G2: 4.4/5 (2,000+ reviews) 
• Capterra: 4.5/5 (2,000+ reviews) 

As our business collects feedback from multiple touchpoints, the customer success team must create different types of forms tailored for each channel. While exploring SurveyMonkey, I came across its template library and was mighty impressed! 

From collecting website user experience feedback, contact information, and event registration details to feedback on events and meetings, SurveyMonkey has a template for every purpose. We could also customize the survey themes for a more branded feel. 

It’s helpful for companies just getting started with surveys or large teams that want to streamline their multi-channel feedback collection. 

SurveyMonkey best features 

• Create AI-assisted surveys quickly with SurveyMonkey Genius 
• Collect segmented feedback with branching and conditional questions
• Create, edit, and review surveys with team members
• Perform trend analysis to understand how answers to specific questions change over time 

SurveyMonkey limitations 

• Pricey for small businesses 

SurveyMonkey pricing 

• Team Advantage: $25/month per user (annual billing only) 
• Team Premier: $75/month per user (annual billing only) 

Pricing details sourced from Capterra

SurveyMonkey ratings 

• G2: 4.4/5 (22,000+ reviews) 
• Capterra: 4.6/5 (10,000+ reviews) 

Also read: 10 SurveyMonkey Alternatives and Competitors in 2024  

SentiSum uses natural language processing (NLP) to analyze customer calls, chats, emails, and survey results. The tool’s advanced AI digs deep into support tickets and understands customer sentiments.

While testing the tool, we could unify our multiple feedback channels and auto-tag tickets to the granular level. The best part is that I could ask SentiSum any question about our customer experience, and it would offer a quick, meaningful answer based on existing data. It’s even quicker than creating ad-hoc reports!

SentiSum best features 

• Connect customers’ reason for contact with CSAT and NPS 
• Use seamless speech analytics to extract insights from calls 
• Auto-prioritize tickets based on topic and sentiment 
• Turn social media comments and negative reviews into support tickets 

SentiSum limitations

• AI can sometimes miscategorize tickets due to inconsistencies in spoken/written language, so it’s important to be cautious 

SentiSum pricing 

• Contact for pricing

SentiSum ratings 

• G2: Not enough reviews 
• Capterra: Not enough reviews

Data cleaning plays an important role in ensuring survey analysis reports are reliable and free from errors or missing values. With Displayr, I could either clean my survey data manually down to the variables or automate the entire process.  

Manual cleaning works when the volume of work is less, but auto data tidying was the best fit for our workflow. Displayr recognized all types of data, from texts, numerics, and grids, to multiple responses. It automatically detected the data type and identified issues such as missing labels, flatlining, or small categories that required merging. 

Displayr best features 

• Create meaningful data stories using customizable brand kits and templates
• Clean up confusing variable names into concise labels with Displayr AI 
• Sort textual and open-ended survey responses into cohesive groups 
• Visualize survey data with 100+ chart types, including donut charts, pictographs, and palm trees 

Displayr limitations 

• Takes a long time to load/refresh 

Displayr pricing 

Data Stories

• Free (suitable for students and other users with basic requirements) 
• Professional: $40/month per user (billed annually) 

Displayr 

• Professional: $255/month per user (billed annually) 
• Enterprise: Custom pricing

Displayr ratings 

• Capterra: 4.8/5 (20+ reviews) 

When sorting through complex data points, I often need to create specific filters to locate the information I seek. With Crunch, I could quickly create and apply a new filter—it took me only a few seconds. 

Another feature that stood out for me was seamless exporting. I exported my survey data offline with Excel to present to my team, and the experience was quite smooth. The tool also allows online sharing/presenting. I tried my hands at it and could create an interactive dashboard in a few minutes. It didn’t need any coding experience. This makes it one of the good data visualization tools for non-technical teams. 

Crunch.io best features 

• Collaborate with team members to analyze survey data
• Organize questions into logical folders 
• Extract insights from data with subsecond response time 
• Customize visualization types 

Crunch.io limitations 

• Has a slight learning curve 

Crunch.io pricing 

• Not available 

Crunch.io ratings 

Also read: 10 Employee Survey Software Tools for HR Teams in 2024 

Some of our market surveys involve multiple phases. Sometimes, I finish all of my analysis and reporting (on the phases completed so far) only to realize that I have to do it all over again when I revise the old data with newly acquired survey results. 

With Q, I could automate this entire process. I didn’t have to work on the analysis from scratch because of a minor data revision—it saved a lot of time for me and my team!

Q Research Software’s best features 

• Clean data and create tables without coding knowledge  
• Generate large, complex data reports quickly 
• Get access to complete R language support 
• Seamless integration with MS Excel and MS PowerPoint 

Q Research Software limitations 

• Occasional lagging 

Q Research Software pricing 

• Standard License: $2379/year
• Transferable License: $7347/year 

Q Research Software ratings 

• G2: 4.7/5 (30+ reviews) 

The main idea behind survey analysis software is to know your stakeholders better and make improvements based on feedback loops . The survey tools I’ve included in this list are great at what they do—analyzing survey data—but not all can balance survey creation and analysis equally or cater to multiple industries. 

This is where ClickUp has a clear advantage over the others. The platform empowers you to ask open-ended and intentional questions for gathering qualitative data, visualize your data in a flexible canvas for effective sentiment analysis, make data-driven decisions, and generate valuable insights from survey data to hit your  business goals and objectives consistently. 

Get started with ClickUp today!

Questions? Comments? Visit our Help Center for support.

Receive the latest WriteClick Newsletter updates.

Thanks for subscribing to our blog!

Please enter a valid email

• Free training & 24-hour support
• Serious about security & privacy
• 99.99% uptime the last 12 months

Root out friction in every digital experience, super-charge conversion rates, and optimize digital self-service

Uncover insights from any interaction, deliver AI-powered agent coaching, and reduce cost to serve

Increase revenue and loyalty with real-time insights and recommendations delivered to teams on the ground

Know how your people feel and empower managers to improve employee engagement, productivity, and retention

Take action in the moments that matter most along the employee journey and drive bottom line growth

Whatever they’re are saying, wherever they’re saying it, know exactly what’s going on with your people

Get faster, richer insights with qual and quant tools that make powerful market research available to everyone

Run concept tests, pricing studies, prototyping + more with fast, powerful studies designed by UX research experts

Track your brand performance 24/7 and act quickly to respond to opportunities and challenges in your market

Explore the platform powering Experience Management

• Free Account
• Product Demos
• For Digital
• For Customer Care
• For Human Resources
• For Researchers
• Financial Services
• All Industries

Popular Use Cases

• Customer Experience
• Employee Experience
• Employee Exit Interviews
• Net Promoter Score
• Voice of Customer
• Customer Success Hub
• Product Documentation
• Training & Certification
• XM Institute
• Popular Resources
• Customer Stories
• Artificial Intelligence
• Market Research
• Partnerships
• Marketplace

The annual gathering of the experience leaders at the world’s iconic brands building breakthrough business results, live in Sydney.

• English/AU & NZ
• Español/Europa
• Español/América Latina
• Português Brasileiro
• REQUEST DEMO
• Experience Management
• Survey Analysis & Reporting

Try Qualtrics for free

How to analyse survey data: best practices, tips and tools.

20 min read Data can do beautiful things, but turning your survey results into clear, compelling analysis isn’t always a straightforward task. We’ve collected our tips for survey analysis along with a beginner’s guide to survey data and analysis tools.

Data can do beautiful things, but turning your survey responses into clear, compelling analysis isn’t always a straightforward task. We’ve collected our tips for survey analysis along with a beginner’s guide to survey data and analysis tools.

What is survey data analysis?

Survey analysis is the process of turning the raw material of your survey data into insights and answers you can use to improve things for your business. It’s an essential part of doing survey-based research .

There are a huge number of survey data analysis methods available, from simple cross-tabulation , where data from your survey responses is arranged into rows and columns that make it easier to understand, to statistical methods for survey data analysis which tell you things you could never work out on your own, such as whether the results you’re seeing have statistical significance.

Get your free Qualtrics account now

Types of survey data

Different kinds of survey questions yield data in different forms. Here’s a quick guide to a few of them. Often, survey data will belong to more than one of these categories as they frequently overlap.

Quantitative data vs. qualitative data

What’s the difference between qualitative data and quantitative data?

• Quantitative data, aka numerical data, involves numerical values and quantities. An example of quantitative data would be the number of times a customer has visited a location, the temperature of a city or the scores achieved in an NPS survey .
• Qualitative data is information that isn’t numerical. It may be verbal or visual, or consist of spoken audio or video. It’s more likely to be descriptive or subjective, although it doesn’t have to be. Qualitative data highlights the “why” behind the what.

Image Source: Intellispot

Closed-ended questions

These are questions with a limited range of responses. They could be a ‘yes’ or ‘no’ question such as ‘do you live in Portland, OR?’. Closed-ended questions can also take the form of multiple-choice, ranking, or drop-down menu items. Respondents can’t qualify their choice between the options or explain why they chose which one they did.

This type of question produces structured data that is easy to sort, code and quantify since the responses will fit into a limited number of ‘buckets’. However, its simplicity means you lose out on some of the finer details that respondents could have provided.

Natural language data (open-ended questions)

Answers written in the respondent’s own words are also a form of survey data. This type of response is usually given in open field (text box) question formats. Questions might begin with ‘how,’ ‘why,’ ‘describe…’ or other conversational phrases that encourage the respondent to open up.

This type 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.

Categorical (nominal) data

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 colours (red v. blue), genders (male v female) or brand names (Chrysler v Mitsubishi).

Multiple choice questions often produce this kind of data (though not always).

Ordinal data

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.

Likert scales and ranking scales often serve up this kind of data.

Scalar data

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.)

You might get this kind of data from a drop-down or sliding scale question format, among others.

Conduct survey research with your free Qualtrics account

The type of data you receive affects the kind of survey results analysis you’ll be doing, so it’s very important to consider the type of survey data you will end up with when you’re writing your survey questions and designing survey flows .

Steps to analyse your survey data

Here’s an overview of how you can analyse survey data, identify trends and hopefully draw meaningful conclusions from your research.

1.   Review your research questions

Research questions are the underlying questions your survey seeks to answer. Research questions are not the same as the questions in your questionnaire, although they may cover similar ground.

It’s important to review your research questions before you analyse your survey data to determine if it aligns with what you want to accomplish and find out from your data.

2.   Cross-tabulate your data

Cross-tabulation is a valuable step in sifting through your data and uncovering its meaning. When you cross-tabulate, you’re breaking out your data according to the sub-groups within your research population or your sample, and comparing the relationship between one variable and another. The table you produce will give you an overall picture of how responses vary among your subgroups.

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.

1.   Review and investigate your results

Put your results in context – how have things changed since the last time you researched these kinds of questions? Do your findings tie in to changes in your market or other research done within your company?

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 polarised responses? Were there any questions with very skewed data? This could be a clue to issues with survey design .

2.   Use statistical analysis to check your findings

Statistics give you certainty (or as close to it as you can get) about the results of your survey. Statistical tools like T-test, regression and ANOVA help you make sure that the results you’re seeing have statistical significance and aren’t just there by chance.

Statistical tools can also help you determine which aspects of your data are most important, and what kinds of relationships – if any – they have with one another.

Become a market research expert with our Market Research Expert Reading List

Benchmarking your survey data

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.

Using consistent types of data and methods of analysis means you can use your initial results as a benchmark for future research . What’s changed year-on-year? Has your survey data followed a steady rise, performed a sudden leap or fallen incrementally? Over time, all these questions become answerable when you listen regularly and analyse your data consistently.

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 benefit of data analysis over time is that you can compare your results with other people’s, provided you are using the same measurements and metrics. A classic example is NPS (Net Promoter Score) , which has become a standard measurement of customer experience that companies typically track over time.

Success Toolkit eBook: Rethink and reinvent your market research

How to present survey results

Most data isn’t very friendly to the human eye or brain in its raw form. Survey data analysis helps you turn your data into something that’s accessible, intuitive, and even interesting to a wide range of people.

1.   Make it visual

You can present data in a visual form, such as a chart or graph, or put it into a tabular form so it’s easy for people to see the relationships between variables in your crosstab analysis. Choose a graphic format that best suits your data type and clearly shows the results to the untrained eye. There are plenty of options, including linear graphs, bar graphs, Venn diagrams, word clouds and pie charts. If time and budget allows, you can create an infographic or animation.

2.   Keep language human

You can express discoveries in plain language, for example, in phrases like “customers in the USA consistently preferred potato chips to corn chips.” Adding direct quotes from your natural language data (provided respondents have consented to this) can add immediacy and illustrate your points.

3.   Tell the story of your research

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.

Turning your Data into Storytelling: Download your free eBook

4.   Include your insights

As well as presenting your data in terms of numbers and proportions, always be sure to share the insights it has produced too. Insights come when you apply knowledge and ideas to the data in the survey, which means they’re often more striking and easier to grasp than the data by itself. Insights may take the form of a recommended action , or examine how two different data points are connected.

Related Reading: Maximise your research ROI with our eBook

Common mistakes in analysing data and how to avoid them

1.   being too quick to interpret survey results.

It’s easy to get carried away when the data seems to show the results you were expecting or confirms a hypothesis you started with. This is why it’s so important to use statistics to make sure your survey report is statistically significant, i.e. based on reality, not a coincidence. Remember that a skewed or coincidental result becomes more likely with a smaller sample size.

2.   Treating correlation like causation

You may have heard the phrase “correlation is not causation” before. It’s well-known for a reason: mistaking a link between two independent variables as a causal relationship between them is a common pitfall in research. Results can correlate without one having a direct effect on the other.

An example is when there is another common variable involved that isn’t measured and acts as a kind of missing link between the correlated variables. Sales of sunscreen might go up in line with the number of ice-creams sold at the beach, but it’s not because there’s something about ice-cream that makes people more vulnerable to getting sunburned. It’s because a third variable – sunshine – affects both sunscreen use and ice-cream sales.

3.   Missing the nuances in qualitative natural language data

Human language is complex, and analysing survey data in the form of speech or text isn’t as straightforward as mapping vocabulary items to positive or negative codes. The latest AI solutions go further, uncovering meaning, emotion and intent within human language.

Trusting your rich qualitative data to an AI’s interpretation means relying on the software’s ability to understand language in the way a human would, taking into account things like context and conversational dynamics. If you’re investing in software to analyse natural language data in your surveys, make sure it’s capable of sentiment analysis that uses machine learning to get a deeper understanding of what survey respondents are trying to tell you.

Free eBook – Go Beyond Surveys: How to Use Multiple Listening Channels

Tools for survey analysis

If you’re planning to run an ongoing data insights program (and we recommend that you do), it’s important to have tools on hand that make it easy and efficient to perform your research and extract valuable insights from the results.

It’s even better if those tools help you to share your findings with the right people, at the right time, in a format that works for them. Here are a few attributes to look for in a survey analysis software platform.

• Easy to use (for non-experts) Look for software that demands minimal training or expertise, and you’ll save time and effort while maximising the number of people who can pitch in on your experience management program . User-friendly drag-and-drop interfaces, straightforward menus, and automated data analysis are all worth looking out for.
• Works on any platform Don’t restrict your team to a single place where software is located on a few terminals. Instead, choose a cloud-based platform that’s optimised for mobile, desktop, tablet and more.
• Integrates with your existing setup Stand-alone analysis tools create additional work you shouldn’t have to do. Why export, convert, paste and print out when you can use a software tool that plugs straight into your existing systems via API?
• Incorporates statistical analysis Choose a system that gives you the tools to not just process and present your data, but refine your survey results using statistical tools that generate deep insights and future predictions with just a few clicks.
• Comes with first-class support The best survey data tool is one that scales with you and adapts to your goals and growth. A large part of that is having an expert team on call to answer questions, propose bespoke solutions, and help you get the most out of the service you’ve paid for.

Stay up to date with our Market Research Global Trends Report

Tips from the team at Qualtrics

We’ve run more than a few survey research programs in our time, and we have some tips to share that you may not find in the average survey data analysis guide. Here are some innovative ways to help make sure your survey analysis hits the mark, grabs attention, and provokes change.

Write the headlines

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:

• The #1 concern that travellers have with staying at our hotel is X
• X% of visitors to our showroom want to be approached by a salesperson within the first 10 minutes
• Diners are X% more likely to choose our new lunch menu than our old one

You may even want to sketch out mock charts that show how the data will look in your results. If you “write” the results first, those results become a guide to help you design questions that ensure you get the data you want.

Gut Data Gut

We live in a data-driven society. Marketing is a data-driven business function. But don’t be afraid to overlap qualitative research findings onto your quantitative data . Don’t be hesitant to apply what you know in your gut with what you know from the data.

This is called “Gut Data Gut”. Check your gut, check your data, and check your gut. If you have personal experience with the research topic, use it! If you have qualitative research that supports the data, use it!

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.

Write a mock press release to encourage taking action

One of the biggest challenges of research is acting on it . This is sometimes called the “Knowing / Doing Gap” where an organisation has a difficult time implementing truths they know.

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.

After six months, gather your team and read the press release together to see how well you executed change based on the research.

Focus your research findings

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.

Package your survey results analysis findings in 5 ways:

• A 1-page executive summary with key insights
• A 1-page stat sheet that ticks off the top supporting stats
• A shareable slide deck with data visuals that can be understood as a stand-alone or by being presented in person
• Live dashboards with all the survey data that allow team members to filter the data and dig in as deeply as they want on a DIY basis
• The Mock Press Release (mentioned above)

Improve your market research with tips from our eBook: 3 Benefits of Research Platforms

How to analyse survey data

Reporting on survey results will prove the value of your work. Learn more about statistical analysis types or jump into an analysis type below to see our favourite tools of the trade:

• Conjoint Analysis
• CrossTab Analysis
• Cluster Analysis
• Factor Analysis
• Analysis of Variance (ANOVA)

eBook: 5 Practices that Improve the Business Impact of Research

Related resources

Analysis & Reporting

Sentiment Analysis 20 min read

Thematic analysis 11 min read, predictive analytics 19 min read, descriptive statistics 15 min read, statistical significance calculator 18 min read, data analysis 29 min read, regression analysis 19 min read, request demo.

Ready to learn more about Qualtrics?

ORIGINAL RESEARCH article

Examining child schooling/care location and child temperament as predictors of restaurant-related behaviors during the covid-19 pandemic: findings from a nationally representative survey.

• 1 Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, United States
• 2 School of Environmental and Biological Sciences, Rutgers, The State University of New Jersey, New Brunswick, NJ, United States
• 3 Center for Ingestive Behavior Research, University at Buffalo, Buffalo, NY, United States
• 4 School of Public Health and Health Professions, University at Buffalo, Buffalo, NY, United States

Purpose: Emerging research highlights impacts of the COVID-19 pandemic on U.S. families, including changes in eating behavior and increased child body mass index. Aims of the present study were to examine whether child temperament and at-home vs. out-of-home childcare/school predicted families’ restaurant-related behaviors during the pandemic. Examining energy balance-related behaviors, like restaurant patronage, during the pandemic can help better understand lasting impacts on child health behaviors and health outcomes.

Methods: An online survey was administered to U.S. parents with a 4-to-8-year-old child in October 2020 (n = 1,000). Linear and logistic regression examined whether child temperament and at home vs. out-of-home childcare/school predicted: (1) the frequency the child consumed restaurant meals (take-out, delivery, dine-in), (2) who chose the child’s restaurant meal, and (3) parent-reported reasons for the child’s meal choice. Income, education, employment, race/ethnicity, and regional COVID-19 restrictions were tested as covariates.

Results: Parents with children higher on negative affectivity reported more frequent restaurant use in-person ( p  < 0.05) and via delivery ( p  < 0.05) compared to parents of children lower on negativity. Child negativity was also linked with parent-reported reasons for children’s restaurant meal choices. Parents of children receiving at-home childcare/schooling used delivery services less frequently than those receiving out-of-home care or schooling ( p  < 0.01).

Conclusion: These findings suggest that individual and family factors may impact restaurant use and the meal selection process for children using restaurants during and beyond the COVID-19 era. Continued examination of individual differences in the impacts of the COVID-19 pandemic can facilitate intervention and policy approaches that fit with different families’ needs.

1 Introduction

In March 2020, restrictive measures were put in place in the United States (U.S.) to slow the spread of the SARS-CoV-2 virus. As businesses and schools closed, families’ lives were disrupted with many parents providing child-care and schooling at home, often in combination with remote work ( 1 , 2 ). Emerging research highlights potential lasting impacts of these well-intended but drastic changes to family routines, including on children’s diets and obesity risk ( 3 ). Similarly, a recent review identified continued impacts of the COVID-19 pandemic on various aspects of children’s health and well-being and also acknowledged that impacts may vary by individual factors ( 4 ).

Before the pandemic, children in the U.S. were commonly consuming energy dense and nutrient poor foods ( 5 ), and restaurants were a normative eating context for families. While there have been some efforts to improve the nutritional quality of foods offered to children at restaurants, restaurant meals still tend to be higher in calories and lower in nutritional quality than meals prepared at home ( 6 ), highlighting child restaurant use and restaurant meal selection as health-related behaviors of interest. Pre-pandemic, over one-third of children in the U.S. typically consumed fast food on a given day and, on average, about one-third of their daily energy intake came from fast food and quick and full-service restaurants ( 7 , 8 ). However, there is emerging evidence that the pandemic may have impacted where and how Americans obtain their food, with increased home cooking and reduced restaurant use ( 9 – 12 ) overall, and some initial evidence of variability by sociodemographics. Our recent study found that about half of children were eating restaurant food at least 2–3 times per month in Fall 2020, with lower use and lower perceived safety of restaurants among some sociodemographic groups ( 9 ). For example, parents with lower education levels and lower income reported less take-out and delivery ( 9 ). There is a need for additional research to better understand inter-individual variability in children’s restaurant use and food selection during COVID-19. Whether children are spending their days at home vs. out-of-home childcare or school, as well as child temperament, have been linked with eating behavior generally ( 13 – 15 ) and may predict restaurant-related behaviors during the pandemic; however this has not been studied yet.

In 2019, only 3% of children were homeschooled while the remaining 97% of the 50 million children enrolled in primary or secondary education in the U.S. attended school in-person ( 16 ). Therefore, children receiving all of their schooling/care at home during the COVID-19 pandemic can be considered to be indicative of the aforementioned major shifts to children’s contexts and routines during this time ( 9 ). Brazendale et al. ( 3 ) highlight ways in which these drastic shifts in structure and routines are likely to impact “obesogenic” behaviors, including dietary intake. With children spending more time at home, the amount and types of food in the home become even more influential for children’s overall dietary intake ( 17 ). During the pandemic, families were buying a greater quantity of food for their home, including more high-calorie snack foods, desserts, and sweets, as well as nonperishable processed foods ( 15 ). Several studies have compared child behaviors during the pandemic to behaviors in summer months when changes in child eating, physical activity and routines lead to weight gain ( 18 ). During the pandemic, it was found that parents were more concerned about their child’s weight, and food insecure families were more impacted by school closures as school meals provide children with daily meals and necessary nutrients ( 2 , 15 ). Children spending more time at home could impact food security, food acquisition, and food preparation.

Families’ restaurant-related behaviors may also differ by child temperament, or differences in reactivity and self-regulation ( 19 ). Such differences in children’s behavioral styles may evoke different types of feeding behaviors from caregivers and affect child eating behaviors and weight trajectories ( 14 , 20 ). A recent study found that child negative affectivity was associated with less parent responsiveness which in turn resulted in poorer mealtime structure and quality ( 21 ). Additional research has shown a relationship between child temperament and weight-related outcomes specifically negative affectivity where higher levels of negative affectivity in young children is predictive of binge eating, emotional eating, stress-induced eating and obesity later in life ( 14 , 22 ). Negative affectivity may also predict child eating styles such as “picky eating” because children with greater reactivity may limit their exposure to new foods and display more negative reactions to trying new tastes ( 22 ). This is important because selective eating places children at risk for both poor nutrition and poor eating habits. Additionally, parents of children higher in temperamental negativity were more likely to use instrumental and emotional feeding methods ( 22 ). Instrumental feeding is defined as rewarding a child with food for desired behaviors, and emotional feeding is the use food to soothe or distract a child even if they are not hungry. Both instrumental and emotional feeding have been associated with a higher body mass index as well as unhealthy food choices ( 22 ). Since parent feeding and child eating behaviors may differ by child temperament ( 22 ), it is possible that restaurant-related behaviors may also differ by child temperament during COVID-19.

Taken together, prior research suggests consuming food from restaurants is linked with children’s diet quality and overall energy intake, and that variability exists in the extent to which families consumed food from restaurants during COVID-19. As such, the primary goal of this research was to understand the extent to which school/care context and child temperament predicted variability in restaurant-related behaviors during COVID-19. Outcomes of interest were: (1) restaurant frequency (how often the child had food from restaurants via take-out, delivery or dining in), (2) factors driving children’s restaurant meal choices, and (3) whether or not the parent played a role in choosing the child’s meal. It was hypothesized that parents with children higher on temperamental negativity and children attending school or receiving care at home would use take-out and delivery from restaurants more frequently during COVID-19. It was predicted that the use of restaurants in-person would generally be low and would not differ based on the aforementioned factors. There were no a priori hypotheses linking temperament and child-care/schooling with the other restaurant-related outcomes of interest.

Study procedures are described in more detail and published elsewhere ( 9 ). This was an observational, cross-sectional survey study. Invitations to participate in this one-time online survey were sent to a stratified random sample using Harris Poll Online opt-in panel which includes millions of respondents that have agreed to participate in survey research.

2.1 Participants

Participants that identified as U.S. residents over the age of 18 years old with at least one 4-to-8-year-old child ( n  = 1,000) were invited to participate. To be eligible for the survey, participants needed to be English-speaking, be a parent/caregiver 18 years of age or older with at least one 4-to-8-year-old child, and have internet access. Demographic and contextual variables are reported in Table 1 .

Table 1 . Participant characteristics for study sample, weighted to be representative of parents with 4-8-year-old child(ren) in the U.S. ( n  = 1000).

2.2 Procedures

Survey questions were fielded in October 2020, as part of a larger study designed to examine how families with 4-to-8-year-old children use restaurants (e.g., frequency of take-out/delivery, meal ordering behaviors) during the COVID-19 pandemic. The survey was created by researchers at the University at Buffalo. Researchers commissioned Harris Interactive (New York) to distribute the survey and incorporate sampling weights based on age, sex, race/ethnicity, education, income, region, marital status, household size and number of children under 18 years of age, so results would be representative of parents of 4-to-8-year-old children in the U.S. Invitations for the Harris Poll Online panel were emailed to a stratified random sample, and respondents were invited to participate in the study with a password-protected email invitation. For parents/caregivers with multiple 4-to-8-year-old children, participants were asked to answer the survey questions about their child with the most recent birthday. Study procedures were reviewed and approved as exempt by the University at Buffalo Institutional Review Board.

2.3 Measures

2.3.1 participant demographics and context.

Parents reported on their and their families’ demographics including their age, gender, height, weight, marital status, highest level of education, household income, employment status, race/ethnicity and whether the household received any government benefits (e.g., SNAP or Medicaid). A brief two-item screen ( 23 ) was adapted and administered to identify households at risk for food insecurity. Items assess how often the household ‘worried whether food would run out before we got money to buy more’ and how often ‘the food that we bought just did not last and we did not have money to get more’, with response options of: often, sometimes and never. These questions were modified to ask participants about their experiences during the last two months versus the original screen which asks participants about the last 12 months to capture experiences during the pandemic.

Parents were asked questions about the extent of current COVID-19-related protection measures in their town/city, including if mask wearing was mandated and whether there were restaurant-related restrictions. Additionally, children’s schooling and care location in the last week was also assessed (i.e., in-person elementary school, virtual elementary school, home school, and/or in-or out-of-home non-parental child-care).

2.3.2 Child temperament

The Negative Affectivity subscale of the Child Behavior Questionnaire-Very Short Form (CBQ-VSF) was used to assess children’s temperamental negativity ( 24 ). Negative Affectivity includes the temperament dimensions of Sadness, Fear, Anger/Frustration, Discomfort, and negative loadings for Falling Reactivity/Soothability. The CBQ-VSF is a reliable and valid parent-report measure of child temperament and includes statements such as “When angry about something, s/he tends to stay upset for 10 min or longer.” Items were rated on a 7-point scale ranging from 1 (extremely untrue of your child) to 7 (extremely true of your child) ( 24 ). Parents were also given a not applicable response option for when the child had not been observed in the situation that was described.

2.3.3 Current food acquisition and eating behaviors

Parents completed questions adapted from measures used in previous research, detailing their use of restaurants during the past 2 months ( 25 ). Questions included the frequency that the child consumed food from restaurants in-person, via take-out, and via delivery, with response options including: never, once a month or less, 2–3 times a month, once a week, 2–3 times a week, and 4 or more times a week. This question was asked three times to capture each of the three different restaurant contexts: dine-in, take-out, or delivery. For each context, parents also indicated who typically selected the child’s meal (i.e., child, parent and child together, parent, another adult), the most important (1) to least important (7) reasons for the child’s meal choice (e.g., taste, nutrition, habit), and how safe they felt it was to obtain food from a restaurant (i.e., very unsafe, somewhat unsafe, somewhat safe, very safe). These questions were all asked in the context of the past 2 months.

2.4 Statistical analysis

We used descriptive statistics to examine frequencies (categorical variables) or means and standard deviations (continuous variables) for all variables of interest. Distributions were also assessed for normality. All analyses incorporated sampling weights, so that results were representative of U.S. parents with 4-to-8-year-old children. Sampling weights were based on parent age, sex, race and ethnicity, education, income, region, marital status, household size, and number of children under 18 years. Linear regression examined whether child negative affectivity and at-home vs. out-of-home childcare/school predicted the frequency the child consumed restaurant meals and the parent-reported reasons for the child’s meal choice, each of which was analyzed in the context of take-out, delivery, and dining in. Analyses of reasons for the meal choice were restricted to those parents who reported some role in that decision ( n  = 413 for dine-in, n  = 562 for take-out, n  = 538 for delivery), since those uninvolved in the decision may not know the reasoning that went into the decision. Analyses of reasons were also narrowed to the top three reasons reported for each dining context. Diagnostic plots were used to assess model assumptions. Generally, these plots were satisfactory, but to be conservative, these models were also repeated as ordinal logistic regressions, and the nature of the results was similar.

Logistic regression was used to examine whether child negative affectivity and at-home vs. out-of-home childcare/school predicted who chose the child’s meal in the context of take-out, delivery, and dining in. The following variables were considered covariates in the aforementioned regression models: income, education, employment, race/ethnicity and the level of COVID-19-related restrictions in the participant’s town. Backwards deletion was used to remove covariates that were not statistically significant predictors in each model.

3.1 Demographics and context

A majority of parents reported that their child was attending school virtually (47%) or being homeschooled (31%), 21% of parents reported that their child was attending school in-person, and 19% reported that they were receiving child-care outside of the home/school. The average score on the negative affectivity subscale was 4.0 (SEM = 0.04) (possible range 1–7). Participant demographics are shown in Table 1 .

A majority of parents reported eating home-cooked meals more often than before the pandemic (64%), while 22% reported no change from before the pandemic. Parents reported that during the past 2 months, 27% of children were dining-in at restaurants least once a week, while 37% had restaurant food via take-out and 34% via delivery at least once per week. Over half of parents were involved in deciding what their child ordered to eat from restaurants, by either making the meal decision on their own or together with their child. When ranking reasons for choosing the child’s meal, parents who played a role in the decision (46.7%) reported that taste was the most important reason followed by nutrition. Complete descriptive statistics on these food acquisition and eating behaviors are reported in Table 2 .

Table 2 . Parent-reported restaurant use and ordering for their 4-to-8-year-old-child and perceived restaurant safety ( n  = 1000*).

3.2 School/childcare status as a predictor of restaurant-related behaviors

Children who received all of their childcare/schooling at home consumed delivery food from restaurants less frequently than children who were receiving some out-of-home childcare or schooling ( p  < 0.01). Childcare/school status was not a significant predictor of the how often the child had food from restaurants via dine-in or take-out, though the nature of these relationships were consistent with delivery ( p  = 0.06 for both) ( Table 3 ). Childcare/school at home was not predictive of who chooses the child’s meal for dine-in ( p  = 0.50), take-out ( p  = 0.15) or delivery ( p  = 0.15).

Table 3 . Regression results for the frequency of restaurant use and who chose the child’s meal in childcare/school status analysis.

Parents who reported that their child was receiving all of their care and/or schooling at home rated nutrition as more important when rank ordering reasons for selecting the child’s restaurant meal for dine-in ( p  < 0.001) and delivery ( p  < 0.0001) compared to those with children attending school or childcare outside of the home ( Table 4 ).

Table 4 . Regression results for the parent-reported reasons for selecting a child’s meal in child care/school location analysis.

3.3 Child temperament as a predictor of restaurant-related behaviors

Parents whose children were higher on negative affectivity reported more frequent child use of restaurants in-person ( p  < 0.05) and for delivery ( p  < 0.05) than parents with children lower on negative affectivity ( Table 5 ). Child negativity did not significantly predict the frequency of getting take-out meals from restaurants or who selected the child’s meal for dine-in, take-out or delivery.

Table 5 . Regression results for the frequency of restaurant use and who chose the child’s meal in child temperament analysis.

When rank ordering reasons for selecting the child’s restaurant meal, parents whose children were higher on negative affectivity rated taste as a more important reason for child meal selection when ordering restaurant food for delivery ( p  < 0.01), compared to those with children lower on negativity affectivity ( Table 6 ). Full results for school/childcare status and temperament as predictors of the child’s frequency of restaurant use and reasons for meal selection are in Tables 3 – 6 .

Table 6 . Regression results for the parent-reported reasons for selecting a child’s meal in child temperament analysis.

3.4 Sociodemographics as a predictor of restaurant-related behaviors

Sociodemographic covariates were significant in many of the models, as shown in Tables 3 – 6 , and generally showed that parents with higher income reported more frequent use of restaurants for dine-in and delivery and employed parents reported more frequent use of restaurants for dine-in only. Participants living in an area with more COVID-19-related restrictions reported more frequent use of restaurants for delivery, while non-Hispanic Asian participants reported less frequent use of restaurants for delivery. Sociodemographic covariates were significant when looking at who chose the child’s meal, with parents reporting higher education being more likely to play a role in the child’s meal choice for take-out and delivery.

4 Discussion

The present study aimed to better understand the extent to which school/care context and child temperament predicted variability in restaurant-related behaviors during COVID-19. While sociodemographics generally predicted more variability in restaurant-related behaviors than child temperament or childcare/school status, the latter factors were linked with some restaurant behaviors. Both child temperament and childcare/schooling location predicted frequency of restaurant use. Children higher on negative affectivity used restaurants in-person and for delivery more frequently than children with lower negative affectivity, and children receiving childcare/schooling at home used delivery services less frequently than those receiving out-of-home care or schooling. Parents with children higher on negativity rated taste as a more important reason when ordering a delivery meal, which is consistent with previous research findings ( 25 ). Overall, these findings suggest that during the early stages of the COVID-19 pandemic, these individual and family factors may have impacted the frequency of restaurant patronage across different modes of restaurant use, as well as the process of selecting children’s restaurant meals. Given prior research suggesting that the quality of children’s dietary intake declines with more consumption from restaurants, as well as evidence of increases in childhood obesity after the start of the pandemic ( 26 ), it is important to continue to build our understanding of factors predicting variability in children’s restaurant-related behaviors and to consider next steps for further understanding the potential health implications of these links.

In the present analysis, child negativity and child care/school status predicted the child’s frequency of restaurant use, with higher negativity linked to more frequent dining in-person at restaurants and via delivery and at-home childcare/schooling linked to less delivery from restaurants. Prior research has shown that parents of children higher on temperamental negativity were more likely to use instrumental and emotional feeding methods to soothe or distract the child ( 22 ). In the context of COVID-19, recent findings have shown that COVID-19-related life changes were positively associated with mothers rewarding their child’s behavior with food ( 27 ). It is possible that, in the face of challenging parent–child interactions, in-person dining as well as delivery may be avenues toward ordering favorite foods to reward the child, soothe the child or get them to engage in desired behaviors. Additionally, due to the possible frustrations and boredom of being at home, families may be more likely to dine-in or pick up food from restaurants. This may be especially true for families with children receiving childcare/schooling at home. However, food acquisition from restaurants using take-out and delivery is understudied both during COVID-19 and in general. Since the start of the pandemic there has been an increase in the use of takeout and delivery from restaurants with 53% of adults saying takeout and delivery is now essential, and 68% noting that they are more likely to use takeout or delivery than before the pandemic ( 28 , 29 ). The Deloitte restaurants trends report, which surveyed restaurant customers in 2019, 2020, 2021 and 2023, found that the use of takeout and delivery services may be here to stay with 69% of customers in 2023 reporting that they got take-out or delivery at the same rate or more frequently when compared to before the pandemic ( 30 , 31 ). In addition, the 2019 report found that only 18% of respondents noted that they order takeout/delivery at least once a week while the 2021 report found that 61% of respondents noted delivery/takeout use at least once a week ( 30 , 31 ). This trend in takeout/delivery use highlights the need for further research into these different modes of restaurant use and implications for future interventions in restaurant contexts.

Children spending more time at home could also impact food insecurity and food acquisition as parents are potentially providing more meals for their children at home. For food insecure families, there is evidence of parents encouraging children to share and eat food with others, as well as child reports of eating less desirable foods because there were no other options ( 32 ). The present survey data show that the majority of children were attending school virtually or being homeschooled at the time of data collection, and parents also reported that they were working from home more and taking on a larger role in the care and instruction of their children ( 9 ). In the present analysis, parents who reported that their child was receiving care or attending school at home rated nutrition as a more important factor in selecting a restaurant meal, compared to those with children attending school or childcare outside of the home. This may suggest that with COVID-19 restrictions and parents spending more time at home, restaurants were being used to fill the role of regular family meals instead of being used for convenience.

Higher child negativity was also predictive of taste being ranked as a more important reason for selection of the child’s delivery meal, in comparison to families with children lower on negativity. This may support prior research findings that negative affectivity predicts child eating styles such as “picky eating” where children limit their exposure to new foods and trying new tastes ( 22 ). This also supports research that shows parents of children who are high in negativity are more likely to feed their child sweet foods and caloric drinks, similar to the foods that tend to characterize children’s menus at restaurants ( 14 , 22 ). Understanding the interplay between temperament and different aspects of children’s food selection and eating behavior is important given prior research suggesting that aspects of temperament like negative affectivity may be linked to children’s obesity risk ( 33 ). Overall, taste has been a key reason for children’s restaurant meal choices outside the context of COVID-19, and future research can examine whether factors motivating meal choices in restaurants differ by child temperament beyond the acute phase of the COVID-19 pandemic ( 25 ).

When examining the results from these analyses, the models’ R 2 values varied, but generally the amount of variance explained by these models was modest. Future research can continue to examine the extent to which these variables are relevant predictors of restaurant-related and other eating behaviors during and beyond the COVID-19 era. Generally, sociodemographics predicted more variability in restaurant-related behaviors than child temperament or childcare/school status ( 9 ). It was found that more highly educated and employed parents, as well as parents who reported living in an area with more COVID-19-related restrictions, were all predictive of taste being a more important reason for selecting their child’s meal. Higher income was predictive of ranking nutrition as an important reason for choosing the child’s meal. Families of high socioeconomic status had access to more resources to navigate changes brought about by COVID-19 restrictions, perhaps allowing them to prioritize some of the same reasons for meal choices that have been observed in restaurant research prior to COVID-19 ( 34 ). Therefore, families of higher socioeconomic status may continue to view restaurant meals as a “treat” and order more tasty meals or have the ability to order more nutritious meals for their children compared to families of lower socioeconomic status. Overall, variability in these restaurant-related behaviors by sociodemographics highlights the need for further research to inform interventions for those who may be at greatest risk of continued impacts of the COVID-19 pandemic.

Limitations of the present study include the use of a self-report survey measure which may have social desirability bias. Minor modifications were made to some existing survey items as well, for example changing the time frame to the past 2 months, to fit with the aims of the present study. The present study examined individual differences of restaurant-related behaviors but did not collect information about the meals ordered at restaurants. Therefore, health differences are not entirely known, and future studies can work to address this. A strength of this study was the use of sampling weights to create a nationally representative sample of U.S. families with 4-to-8-year-old children. This study was also conducted in October 2020, when many of the initial COVID-19 protection measures and restrictions were relaxed or lifted. Therefore, these findings may highlight longer-term impacts of the COVID-19 pandemic on children’s eating and health as well as individual and family factors which were shown to impact children’s restaurant use and meal selection during COVID-19. The use of restaurant take-out and delivery is understudied both during COVID-19 and in general. These findings suggest the need for additional research, examining the frequency and health implications of these different modes of restaurant use during the COVID-19 era and beyond, and considering the potential for health-related interventions in these contexts.

The impacts of the COVID-19 pandemic on families’ routines and health behaviors may continue beyond the COVID-19 era and may vary by child and family factors. Our prior research demonstrated some variability in restaurant related behaviors during COVID-19, with little known about how parent and child factors predict variability in families’ restaurant use ( 9 ). The current analysis found that both child negative affectivity and child care/school status predicted the child’s frequency of restaurant use, with higher negativity linked to more frequent dining in-person at restaurants and via delivery and at-home childcare/schooling linked to less delivery from restaurants. Continued research in this area can help us understand differential experiences during COVID-19, the extent to which these persist, and potential corresponding health implications and intervention opportunities. In addition, increased use of restaurants via take-out and delivery may be here to stay, suggesting the need for additional research on these modes of restaurant use. Additional research in these areas can inform intervention and policy approaches that are in alignment with current contexts and different families’ needs.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Ethics statement

The study was approved by the University at Buffalo Institutional Review Board (STUDY00004723, 8 September 2020). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

JG: Writing – original draft, Writing – review & editing. MF: Writing – review & editing. ST: Writing – review & editing. LE: Writing – review & editing. LL: Writing – review & editing. SA-F: Writing – original draft, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was funded by NIH R01HD096748 (PI: SA-F).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1. Decker, S., Peele, H., and Riser-Kositsk, M. (2020). The coronavirus spring: The historic closing of U.S. schools. Education week . Available at: https://www.edweek.org/leadership/the-coronavirus-spring-the-historic-closing-of-u-s-schools-a-timeline/2020/07

Google Scholar

2. Dutta, M . COVID-19 and impact of school closures on the children of the United States; a point of view with an empirical analysis. SSRN . (2020). doi: 10.2139/ssrn.3596096

Crossref Full Text | Google Scholar

3. Brazendale, K, Beets, MW, Weaver, RG, Armstrong, B, and Hunt, ET. Covid-19 mitigation strategies: a natural experiment highlighting the importance of structure in the prevention and treatment of childhood obesity. Prev Med Rep . (2022) 30:102023. doi: 10.1016/j.pmedr.2022.102023

PubMed Abstract | Crossref Full Text | Google Scholar

4. Irwin, M, Lazarevic, B, Soled, D, and Adesman, A. The COVID-19 pandemic and its potential enduring impact on children. Curr Opin Pediatr . (2022) 34:107–15. doi: 10.1097/MOP.0000000000001097

5. Ford, CN, Slining, MM, and Popkin, BM. Trends in dietary intake among US 2-to 6-year-old children, 1989-2008. J Acad Nutr Diet . (2013) 113:35–42.e6. doi: 10.1016/j.jand.2012.08.022

6. Moran, AJ, Block, JP, Goshev, SG, Bleich, SN, and Roberto, CA. Trends in nutrient content of children’s menu items in U.S. chain restaurants. Am J Prev Med . (2017) 52:284–91. doi: 10.1016/j.amepre.2016.11.007

7. Fryar, C. D., Carroll, M. D., Ahluwalia, N., and Ogden, C. L. Fast food intake among children and adolescents in the United States. NCHS data brief . (2020) 375:1–8.

8. Powell, LM, Nguyen, BT, and Han, E. Energy intake from restaurants: demographics and socioeconomics, 2003–2008. Am J Prev Med . (2012) 43:498–504. doi: 10.1016/j.amepre.2012.07.041

9. Ferrante, M. J., Goldsmith, J, Tauriello, S, Epstein, L. H., Leone, L. A., Anzman-Frasca, S, et al. Food Acquisition and Daily Life for U.S. Families with 4-to 8-Year-Old Children during COVID-19: Findings from a Nationally Representative Survey. Int J Environ Res Public Health . (2021) 18:1734. doi: 10.3390/ijerph18041734

10. Open Government Data United States Department of Agriculture, Economic Research Service. Eating-out expenditures in March 2020 were 28 percent below March 2019 expenditures. (2020). Available at: https://wwwersusdagov/data-products/chart-gallery/gallery/chart-detail/?chartId=98556

11. Wilkins, JL . Challenges and opportunities created by the COVID-19 pandemic. J Nutr Educ Behav . (2020) 52:669–70. doi: 10.1016/j.jneb.2020.05.005

12. Bennett, G, Young, E, Butler, I, and Coe, S. The impact of lockdown during the COVID-19 outbreak on dietary habits in various population groups: a scoping review. Front Nutr . (2021) 8:626432. doi: 10.3389/fnut.2021.626432

13. Hill, DC, Moss, RH, Sykes-Muskett, B, Conner, M, and O'Connor, DB. Stress and eating behaviors in children and adolescents: systematic review and meta-analysis. ScienceDirect . (2018) 123:14–22. doi: 10.1016/j.appet.2017.11.109

14. Bergmeier, H., Skouteris, H., Horwood, S., Hooley, M., and Richardson, B. Associations between child temperament, maternal feeding practices and child body mass index during the preschool years: A systematic review of the literature. Obesity Reviews (2013) 15:9–18. doi: 10.1111/obr.1206

15. Adams, EL, Caccavale, LJ, Smith, D, and Bean, MK. Food insecurity, the home food environment, and parent feeding practices in the era of COVID-19. Obesity . (2020) 28:2056–63. doi: 10.1002/oby.22996

16. Riser-Kositsky, M. Education statistics: Facts about American schools. (2020). Available at: https://www.edweek.org/ew/issues/education-statistics/index.html .

17. Carroll, N, Sadowski, A, Laila, A, Hruska, V, Nixon, M, Ma, DWL, et al. The impact of COVID-19 on health behavior, stress, financial and food security among middle to high income Canadian families with Young children. Nutrients . (2020) 12:2352. doi: 10.3390/nu12082352

18. Rundle, AG, Park, Y, Herbstman, JB, Kinsey, EW, and Wang, YC. COVID-19-related school closings and risk of weight gain among children. Obesity . (2020) 28:1008–9. doi: 10.1002/oby.22813

19. Rothbart, M. K., and Bates, J. E. Temperament. In Handbook of child psychology. Social, emotional, and personality development Wiley Online Library . (2007). doi: 10.1002/9780470147658.chpsy0303

20. Haycraft, E., Farrow, C., Meyer, C., Powell, F, and Blissett, J. Relationships between temperament and eating behaviours in young children. Appetite . (2011) 53:689–692. doi: 10.1016/j.appet.2011.02.005

21. Baker, LN, Witherspoon, DO, Nicholson, JS, and Fuglestad, AJ. The roles of child temperament, parent stress, and parenting style in family mealtimes. Appetite . (2023) 188:106758. doi: 10.1016/j.appet.2023.106758

22. Kidwell, KM, Kozikowski, C, Roth, T, Lundahl, A, and Nelson, TD. Concurrent and longitudinal associations among temperament, parental feeding styles, and selective eating in a preschool sample. J Pediatr Psychol . (2018) 43:572–83. doi: 10.1093/jpepsy/jsx148

23. Hager, ER, Quigg, AM, Black, MM, Coleman, SM, Heeren, T, Rose-Jacobs, R, et al. Development and validity of a 2-item screen to identify families at risk for food insecurity. Pediatrics . (2010) 126:e26–32. doi: 10.1542/peds.2009-3146

24. Putnam, SP, and Rothbart, MK. Development of short and very short forms of the Children's behavior questionnaire. J Pers Assess . (2006) 87:102–12. doi: 10.1207/s15327752jpa8701_09

25. Anzman-Frasca, S, Dawes, F, Sliwa, S, Dolan, PR, Nelson, ME, Washburn, K, et al. Healthier side dishes at restaurants: an analysis of children’s perspectives, menu content, and energy impacts. Int J Behav Nutr Phys Act . (2014) 11:81. doi: 10.1186/1479-5868-11-81

26. Lange, SJ, Kompaniyets, L, Freedman, DS, Kraus, EM, and Porter, RDNP3, et al. Longitudinal trends in body mass index before and during the COVID-19 pandemic among persons aged 2–19 years — United States, 2018–2020. MMWR Morb Mortal Wkly Rep . (2021) 70:1278–83. doi: 10.15585/mmwr.mm7037a3

27. Wang, SD, Devjani, S, Chillakanti, M, Dunton, GF, and Mason, TB. The comet study: examining the effects of COVID-19-RELATED perceived stress on Los Angeles MOTHERS’ dysregulated eating behaviors, child feeding practices, and body mass index. Appetite . (2021) 163:105209. doi: 10.1016/j.appet.2021.105209

28. Grindy, Bruce . Consumers are expected to continue using takeout and delivery. (2020). Available at: https://restaurant.org/education-and-resources/resource-library/consumers-are-expected-to-continue-using-takeout-and-delivery/ .

29. Fantozzi, Joanna . “Will delivery still be king in a post-COVID world?” restaurant hospitality (2021). Available at: https://www.restaurant-hospitality.com/limited-service/will-delivery-still-be-king-post-covid-world .

30. Chick, J., Morris, J. P., and Kress, A. (2023). Future of restaurants study. Deloitte United States. Available at: https://www2.deloitte.com/us/en/pages/consumer-business/articles/future-of-restaurants-study.html

31. Chick, J., Duffy, K., and Newell, E. (2021). The future of restaurants survey 2021 Deloitte United States. Available at: https://www2.deloitte.com/us/en/pages/consumer-business/articles/restaurant-future-survey-technology-customer-experience.html

32. Connell, CL, Lofton, KL, Yadrick, K, and Rehner, TA. Children's experiences of food insecurity can assist in understanding its effect on their well-being. J Nutr . (2005) 135:1683–90. doi: 10.1093/jn/135.7.1683

33. Vollrath, ME, Stene-Larsen, K, Tonstad, S, Rothbart, MK, and Hampson, SE. Associations between temperament at age 1.5 years and obesogenic diet at ages 3 and 7 years. J Dev Behav Pediatr . (2012) 33:721–7. doi: 10.1097/DBP.0b013e31826bac0d

34. Office for National Statistics Which occupations have the highest potential exposure to the coronavirus (Covid-19)? , (2020) Available at: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/hichoccupationshavethehighestpotentialexposuretothecoronaviruscovid19/2020-05-11

Keywords: temperament, restaurants, children, COVID-19, childcare

Citation: Goldsmith J, Ferrante MJ, Tauriello S, Epstein LH, Leone LA and Anzman-Frasca S (2024) Examining child schooling/care location and child temperament as predictors of restaurant-related behaviors during the COVID-19 pandemic: findings from a nationally representative survey. Front. Nutr . 11:1281686. doi: 10.3389/fnut.2024.1281686

Received: 22 August 2023; Accepted: 04 July 2024; Published: 07 August 2024.

Reviewed by:

Copyright © 2024 Goldsmith, Ferrante, Tauriello, Epstein, Leone and Anzman-Frasca. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Stephanie Anzman-Frasca, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.