# Survey data analysis: From methods to implementation

Getting valuable survey responses is only half the battle. The other half is to make sense of that data using survey data analysis. How, you ask? Read on!

Getting valuable feedback from customers is hard work, but it's only half the battle. You need to understand what that data means and translate it into actionable information through survey data analysis, which is no easy feat.

It’s no wonder there are entire books and courses dedicated to mastering this skill. It requires the right approach, a dedicated team, and a few handy survey analysis tools, without which making sense of huge piles of data is almost impossible.

In this article, I'll show you how to identify what drives your customer surveys and understand the insights within your data with survey data analysis – all without needing a Ph.D. in statistics.

Let’s get started!

## Importance of survey data analysis

Survey data analysis means examining the responses collected from surveys to extract insights and draw conclusions. It's crucial to understand what people think, feel, or prefer based on their answers to survey questions.

Research by McKinsey & Company shows that companies utilizing data analytics are 23 times more successful in acquiring new customers compared to those that don't use data-driven strategies.

Here are a few reasons why you should bother analyzing survey data:

- Helps in making decisions based on factual information rather than assumptions.
- Reveals trends over time or across different demographics.
- Pinpoints areas for improvement based on feedback.
- Identifies distinct groups within a population for targeted strategies.
- Compares performance against industry standards or previous surveys.
- Predicts future behaviors or outcomes based on current data.
- Tests hypotheses or assumptions against real-world data.

## Survey data analysis methods

Before we explore how to analyze survey data, you need to know the different methods of analysis.

Now, not all survey data analysis methods described below will be applicable to your survey type. But, your research questions will determine which one you should use to reach the right conclusion.

### 1. Descriptive analysis

Descriptive analysis is the simplest method to understand survey data, where you focus on the basic aspects of your data, like averages, percentages, and frequency counts.

This method gives you a clear picture of what the data looks like and highlights the most common responses. You can see patterns and trends at a glance.

Descriptive analysis works by organizing your data into simple summaries. For example, if you asked survey respondents about their satisfaction with a product, you can calculate the average satisfaction score. You can also see what percentage of people rated the product highly or poorly.

One of the key aspects of descriptive analysis is that it does not go into the deeper relationships between different variables. It simply tells you what is happening in your data. This makes it a great starting point for any data analysis

### 2. Inferential analysis

Inferential analysis takes your survey data a step further. Instead of just describing what you see, this method helps you make predictions and draw conclusions about a larger group based on your sample data.

A key part of this method is hypothesis testing. You begin with a hypothesis, which is a statement you want to test.

For instance, you might hypothesize that your new product is more popular among younger customers. Inferential analysis helps you determine whether your survey data supports this hypothesis.

You also use confidence intervals in inferential analysis. A confidence interval gives you a range within which you can expect the true value to fall.

For example, if you find that 60% of your sample likes your product, a confidence interval might tell you that between 55% and 65% of the entire population likely feels the same way.

### 3. Crosstab analysis

Crosstab or cross-tabulation analysis is used to examine the relationship between two or more variables in your survey data.

In this data analysis method, you create a table, or "crosstab," where you list one variable's categories across the top and the other variable's categories down the side.

Each cell in the table shows the count or percentage of respondents that fall into the corresponding categories.

Crosstab analysis often includes calculating percentages to make comparisons easier. You can look at row percentages, column percentages, or the overall percentage to understand the relative importance of different categories.

Another key aspect of crosstab analysis is the chi-square test, which tells you if the relationships you see in your crosstab are statistically significant.

This means you can determine whether the patterns you observe are likely due to chance or if they represent a real relationship in your data.

### 4. Factor analysis

Factor analysis is a way to find patterns in survey data. It helps you see which questions or items are related to each other.

The goal of factor analysis is to reduce data complexity by finding common patterns or factors that explain the correlations among variables.

It does this by examining how variables co-vary together. Variables that co-vary strongly with each other are likely measuring the same underlying factor.

During factor analysis, you extract factors and then interpret them based on the variables that load onto each factor.

Factor loadings indicate how much each variable contributes to a factor. High loadings suggest that a variable is strongly related to that factor.

Factor analysis results in a factor matrix, a table that shows the factors and their loadings. You can use this matrix while interpreting the factors and understand what they represent.

### 5. Regression analysis

Regression is a survey data analysis method used to understand the relationships between variables in your survey data.

It helps you see how changes in one variable might affect another. Regression analysis is the “go-to method in analytics,” according to Harvard Business Review.

This method is especially useful when you want to predict outcomes or identify key factors that influence your results.

For regression analysis, you identify the dependent variable (what you want to predict) and independent variables (factors that might influence it).

Next, you fit a regression model to your data. In simple linear regression, this involves finding the best-fitting line that describes the relationship between the dependent and independent variables. The formula for this line is:

Y=a+bX

Here, Y is the dependent variable, X is the independent variable, a is the intercept, and b is the slope.

Multiple regression allows the analysis of several independent variables at once. The R-squared value indicates how well your model explains the variation in the dependent variable, with values close to 1 indicating a strong fit.

### 6. Cluster analysis

Cluster analysis is a method used to group similar data points together. It's useful when you want to find patterns or segments within your survey data.

To start with cluster analysis, you need to decide on the variables you want to use for grouping. For example, if you’re analyzing survey data on customer satisfaction, you might use factors like age, purchase frequency, and product ratings.

Next, you apply a clustering algorithm to your data. The most common algorithm is K-means clustering. You choose the number of clusters (groups) you want, and the algorithm assigns each data point to a cluster.

During the analysis, the algorithm calculates the distance between data points, usually using Euclidean distance. The goal is to minimize the distance within clusters and maximize the distance between clusters.

Cluster analysis results in clear groupings of your data. For instance, you might find a cluster of young, frequent buyers who are highly satisfied and another cluster of older, occasional buyers who are less satisfied.

Now, let's roll up our sleeves and get started on the step-by-step blueprint that turns your raw survey data into a collection of actionable insights.

## How to analyze survey data?

Here are 6 crucial steps to make sense of your data.

### 1. Determine your survey goal

Think back to when you were building your survey. What was the big-picture goal? You must have penned down your top research questions when setting your survey objective.

Pinpoint the overarching survey question(s) you're aiming to solve. For example, "What is the overall level of satisfaction among our customers?"

Then, look at specific survey questions that help answer this research question. For instance, "How would you rate your satisfaction with our product quality?" or "How satisfied are you with our customer support?"

Segmenting your survey questions will help you isolate data that are relevant to your goals.

### 2. Understand your sample size

In surveys, sample size refers to the number of people who actually completed your survey. This number is crucial because it tells you how well your results represent the entire group you were interested in (known as the population).

The bigger your sample size, the more confident you can be that your findings apply to the whole population.

To ensure your sample size is sufficient, use a sample size calculator. It helps you figure out how many responses you need based on the size of your population, the margin of error you can accept, and your confidence level.

Common confidence levels are 95% or 99%, which means you can be 95% or 99% sure that your results reflect the views of the whole population.

Once you have determined your ideal sample size, compare it to the number of responses you received. If you received fewer responses than needed, your findings might not be as accurate or generalizable.

### 3. Cross-tabulate results

To cross-tabulate results, you create a table where you place one survey question's responses across the top (columns) and another question's responses down the side (rows).

Each cell in the table shows the count or percentage of respondents who chose a particular combination of answers.

Let’s say you conducted a survey asking customers about their satisfaction with a product and their age group. Your survey questions might be:

- "How satisfied are you with our product?" (Very satisfied, Satisfied, Neutral, Dissatisfied, Very dissatisfied)

- "What is your age group?" (18-25, 26-35, 36-45, 46-55, 56+)

And now, you want to see if there is a relationship between age and product satisfaction. Your crosstab table comparing the two questions with the number of respondents in the cells might look like this:

The table helps you quickly see patterns. For instance, you can notice that customers aged 36-45 are the most satisfied with the product.

### 4. Look for significance

When you analyze survey data, you want to know if the patterns you see are just random chance or if they truly reflect something real about the population.

To look for significance, you first define your hypothesis. This is a statement you want to test, like "Customers who receive more personalized emails are more satisfied with our service."

When you run a survey data analysis, it shows you if a particular data point has statistical significance using a p-value. The p-value indicates the probability that the observed results occurred by chance.

If the p-value is equal to or lower than 0.05, the result is considered moderately significant. This means there is less than a 5% chance that the result is due to random variation.

If the p-value is lower than 0.01, the result is highly significant. This indicates there is less than a 1% chance that the result is due to chance.

### 5. Use statistical analysis to confirm findings

Finally, use statistical tests through software like SPSS, STATA, and R to confirm significance. Common tests include the chi-square test, t-test, and ANOVA. These tests help you compare groups and see if differences are statistically significant.

**Chi-Square Test**: Use this to see if there is a significant relationship between two categorical variables, like customer satisfaction and age group.

**T-Test**: Use this to compare the means of two groups, such as satisfaction scores from two different stores.

**ANOVA**: Use this to compare the means of three or more groups, like satisfaction levels across different regions.

Besides, you can use any of the survey data analysis methods we discussed earlier depending upon your data and the research question.

### 6. Understand the difference between correlation & causation

Once researchers identify relationships between variables through statistical tests, it’s crucial to distinguish between correlation and causation.

Correlation means that two variables change together in a predictable way; as one increases or decreases, the other does too. However, correlation alone doesn’t prove that one variable causes the other.

Causation, on the other hand, implies that one variable directly influences the other. Establishing causation requires more rigorous testing to rule out other factors that could be influencing the relationship.

For instance, consider the relationship between exercise and weight loss. There’s a strong correlation between regular exercise and lower body weight. However, exercise alone may not directly cause weight loss. Factors like diet, metabolism, and genetics also play significant roles.

We have now understood how to analyze survey data. Next, let’s learn how to report survey results.

## How to report survey results?

Survey data analysis means digging into the data to find meaningful insights. Once you’ve done that, the next step is to share your findings. Reporting survey results means presenting the data in a clear and understandable way.

So, let's move on to how to report survey results in a clear and concise way.

### 1. Introduction & background

The introduction and background section of your survey results report sets the stage by providing context and outlining what the survey aimed to achieve.

During survey reporting, the first thing you should do is state the purpose of your survey. Explain why you conducted it and what you hoped to learn or achieve.

For example, if you surveyed customers, mention that you wanted to understand their satisfaction levels to improve services.

Also, explain what problem or question your survey aimed to answer. Give some background information that helps readers understand why this survey is important.

You can mention trends in your industry, changes in customer preferences, or specific challenges your organization is facing that prompted the survey.

### 2. Methodology

In your survey report, the methodology section explains how you conducted the survey.

Discuss how you selected participants for the survey. If you used random sampling, explain how it was done to ensure fairness. Mention the sample size and any demographics you targeted.

Describe how data was collected. For instance, was it done online, over the phone, or in person? Explain any tools or software used to gather responses and how respondents were contacted.

Briefly mention any ethical guidelines you followed, such as obtaining informed consent from participants and ensuring confidentiality of their responses to build trust.

### 3. Key findings

In this section, you showcase the most interesting and impactful results from your survey. Summarize the main discoveries and trends observed across different survey questions.

If you segmented your data (by demographics, customer types, etc.), compare findings between different groups.

Connect each finding back to the objectives of your survey. Explain how each discovery contributes to understanding your research questions or achieving your survey goals.

Additionally, emphasize any unexpected findings that could challenge assumptions or require further investigation.

It's crucial to maintain clarity and avoid overwhelming your audience with excessive details in the results section.

### 4. Data visualization

Data visualization makes complex information easier to understand at a glance.

Select appropriate charts and graphs that best represent your data. For example, bar charts are great for comparing different groups or categories. Pie charts show proportions or percentages. Line graphs are useful for showing trends over time.

Ensure your visuals are clear. Use labels and legends to explain what each part of the chart represents. Provide clear and concise labels for axes, data points, and any annotations.

Colors should be used purposefully to distinguish between different data sets or categories. Stick to a simple color scheme and avoid using too many colors in one chart.

### 5. Analysis & interpretation

In the analysis and interpretation section of your survey report, you describe the meaning behind the data you've collected. You don't just present findings in a vacuum. Explain what your results mean for real-world situations.

Explain the data by interpreting survey results and their implications. For example, if a large percentage of respondents expressed dissatisfaction with a product feature, discuss why this might be the case based on their feedback.

Look for connections between different findings. Also, address any variances or outliers in your data.

Again, always link your survey data analysis back to the objectives of your survey. Explain how each finding contributes to answering your research questions.

Surveys aren't perfect. Briefly acknowledge any limitations of your survey, such as sample size or specific demographics, that might impact the generalizability of the results.

### 6. Conclusion & recommendations

This is the final step of your survey analytics reporting that wraps up your findings and suggests actionable steps based on your survey data analysis.

Briefly remind readers of the most important findings from your survey to solidify the key takeaways. Reiterate why these findings matter. Explain how they contribute to understanding your topic or issue and why they are relevant to your audience.

Based on your analysis, offer specific recommendations for action. These should be practical and directly related to addressing the issues or opportunities identified in your survey. The recommendations could be for businesses, policymakers, or future research projects.

Conclude with a strong statement that reinforces the importance of acting on the survey findings.

## The best of survey analysis tools!

Survey data analysis is essential for transforming raw feedback into actionable insights. And effective survey reporting requires not only presenting survey results clearly but also interpreting them to provide meaningful context and guidance.

Many of these tasks are made easier with survey analysis tools like Formaloo.

As a leading survey maker and best-in-class analysis features, Formaloo offers robust capabilities for generating crystal-clear reports in no time. After collecting survey responses, you can analyze them with Formaloo AI.

With real-time tables and charts, you can instantly see trends, patterns, and key metrics. The platform also allows your visuals to match your brand, sort, and filter responses for deeper analysis, and tag key findings for easy reference.

Formaloo's customizable dashboards help you to create dynamic Kanban boards and write in-depth analyses with engaging visuals.

Get started for free with Formaloo today.