While there are many advanced statistical packages out there you don’t need them to perform a detailed and comprehensive analysis of your survey data.

You can use the same techniques and approaches in Excel and in this post, I’ll take you through how to analyze survey data in Excel.

Excel is a very good tool to use for your analysis and has the benefit of being on almost everyone’s desktop. With a little bit of insight, you can do almost everything the statistical packages can do in Excel.

Don’t fall into the trap of thinking it’s an inferior tool.

## What Your Analysis Needs to Achieve

When analyzing survey data there are surprisingly few different types of questions you need to answer.

You will generally want to know:

- If the responses for one question / customer segment are significantly different from the responses for another question /customer segment.
- If the responses for one question are changing over time.
- If the responses for one question are correlated (implies causes / is caused by) another question.

This is good news because there are only a few types of statistical approaches you will need.

**Excel Spreadsheet**you can download and use: Download Now

## Your Data

There are three different types of data a typical customer survey generates:

### Numbers: also called ordinal data

Numbers come from questions like this:

How responsive is Company X in closing the loop after problem resolution? Where 1 is very unresponsive and 5 is very responsive

### Categorical: Category data

Categorical data comes from questions like this:

Of the following, which is your favourite colour: red, blue, green

### Text: Words

Text data comes from questions like this:

Please tell us what you like most about our widget.

We are going to focus on Number data in this post.

## You Survey Analysis Plan

Below are the steps in using Excel to analyze your survey data:

- Calculate simple statistics (mean, max, etc.) for all questions
- Graph the questions, with error bars
- Create histograms for the questions and interpret them
- If you have past data, plot averages over time, with errors
- Decide which groups you want to compare and test to see if they have changed
- Perform correlation analysis to see if two variables might be linked: mostly on outcome variables and attributes.

In the rest of this post I’ll show you how to undertake each of those tasks.

## Simple Statistics Using Excel.

Generating simple statistics for your data, mean, maximum and minimum, is quite easy.

In the image below we have included the standard maximum, minimum and average along with a couple of additional statistics: Standard Error and 1.96 x Standard Error. We’ll use these statistics in the next section when we graph the scores add confidence intervals for the average.

Excel has formulas for each of these and is smart enough that you can simply highlight an entire data column and it will calculate the statistics for you.

## Graph Each Question and add error bars

When you present your data you will almost certainly use charts. So, the next step is to graph the average of each question.

However, one of the problems with most feedback charts is they show the average as one number. This can be misleading because the response average is only an estimate of the population average.

With a couple of simple steps, you can add Error Bars to your charts to show the 95% confidence interval – which is much more informative.

To interpret the error bars you need to know a little about Standard Error:

The standard error of the sample mean is an estimate of how far the sample mean is likely to be from the population mean.

When we multiply it by 1.96 we are creating a 95% confidence interval. In other words we are 95% confident that the actual average for all customers is between the upper error bar and the lower error bar.

Here is a quick video on exactly how to do that.

## Add Histograms of each question

Before you go on to more advanced statistics you should also create histograms of them to better understand the data.

### What are Histograms?

Histograms show you the proportion of different responses to a question and can be used on Number and Categorical data.

They are very useful because, while averages can be interesting, they can also hide a lot information about the responses.

Are the responses nicely spread around the average or skewed to one side or maybe there are there two peaks?

While each sample might have the same average, they can have very different looking histograms – and seeing those histograms can help you more effectively interpret the responses.

So, you should create a histogram for each of your questions and then review them for interesting profiles. You can use this resource to help you interpret your histograms

### How to Create a Histogram

**Note**: You will need the Excel Data Analysis tool kit before you begin. If you don’t have it, or you’re not sure, check out the Microsoft site

First, you’ll need to create a set of “bin” values. Typically, your survey response scale will use 0-5 or 0-7 or 1-7 or something similar so your bins need to cover each of the score options.

Here is a bin range for a 0-5 response scale. Note that it starts at -0.5 to catch the 0’s and goes to 5.499 to catch the 5’s. Also, note the use of “0.499” elements – this is to catch the half scores that some people might use (if your survey allows it.)

Now you have the bins you can create a histogram for each of your survey response. This short video shows you exactly how to do that.

## Testing for Significant Changes: Student’s t-Test

Now, if your histograms show a relatively nice “normal” distribution curve, you can use some more advanced statistics to add value to the data.

See below for the histogram from the example. You can see that it has a nice even shape with just one peak. It looks normally distributed (the “bell curve”) so we can carry on with the more advanced statistics.

Student’s t-Test is a good way to answer two of our questions from the start of the post:

- Are the responses for one question / customer segment significantly different from the responses for another question / customer segment
- Are the responses for one question changing over time

The t-Test allows you to test if the average for one set of scores is probably different from the average for another set of scores.

Hopefully you can see that both these questions, and all their variations, are asking this question:

Does the avearage for this set of question scores differ significantly from the average for that set of question scores

At all times, you are just comparing two sets of scores, for example:

- Question 1 last month with Question 1 this month
- Question 1 this month with question 2 this month
- Question 1 for respondents from Segment A with Question 1 for respondents from Segment B

So, you can use the T-test in all these areas. I’ve already written a detailed post exactly how to use the t-Test on survey responses so I’ll refer you there for the details.

**Note**: there are downsides with just comparing everything with everything. Think about it for a few seconds. If you set your test so you have a 95% confidence level and you test 20 different pairs of responses, on average, one of them will show a positive test just on random chance (5% chance). There are ways to overcome this problem but they are outside the scope of this post.

So, in general take care just comparing everything with everything – do it deliberately.

## Correlation

Cause and effect is the focus of this section. For instance: does our Net Promoter Score go up, and down as our Responsiveness score goes up and down?

You have probably heard the saying that correlation does not imply causation and it is true that you need to be careful not to over emphasize the impact of a high correlation. However, it is also true that correlation does not deny causation. It’s a good place to start your investigation.

When investigating correlation, start by looking at how your survey outcome question correlates with your attribute questions. Creating the chart below for each of your attributes is a good place to start.

Attribute questions that correlate highly with the outcome question are good candidates for key business drivers.

### Calculating in Correlation in Excel

The easiest way of calculating correlation is to simply graph your two data sets and ask Excel to add in the “Linear Trend Line”. This has the advantage of also letting you see the data so you can determine if the data looks right, i.e. one or two points are not skewing the results.

Again I have a short video to help you on this.

Interpreting the correlation coefficient (R) is the subject of a whole blog post, but in summary:

The correlation coefficient (R) indicates the correlation between the two values.

- R = 1 is a perfect correlation
- R = 0 indicates no correlation at all
- R = -1 is a perfect negative correlation, a move up in one variables correlates to a move down in the other.

So, the closer 1 the better the correlation and you can generally ignore anything less that about 0.3 for this value.

Excel shows you the R-squared value. This number indicate the percentage of variation in that one variable explains variation in the other variable.

## Summary

In this post I’ve outlined simple process for using Excel to analyze survey data. If you follow it you’ll be ahead of many people’s survey analysis and on your way to correctly interpreting what your customers are telling you.

**Excel Spreadsheet**you can download and use: Download Now

Keith Yap says

Thanks for the post Adam, most informative. To those who are interested in the text analysis of verbatim collected from surveys, I recently summarised my explorations using excel to do so. I feel it compliments this post nicely.

https://www.keithyap.com.au/text-analysis-using-excel/

Adam Ramshaw says

Keith,

Thanks for stopping by to comment. I checked out your page — you have some nice approaches for text analysis using Excel.

Adam

Keith Yap says

If the old URL above does not work anymore – try this new one instead: https://medium.com/keith-yap/text-analysis-using-excel-3d38b8f0c906

alina says

i need your help. i have questionnair based on water used in agriculture… its production and how much land does farmers have….and how much they are cultivating……no any option in my survey like ( agree, disagree,positive )… fill in the blanks type qusetionnair… now i should solve it in excel and take out results… if you have any his type of video please help me…

Adam Ramshaw says

Alina,

I’m sorry I don’t understand the question you are asking me. Are you able to provide some more clarity on what you are trying to achieve.

Regards,

Adam Ramshaw

Jon says

Hi Adam,

I ran a survey which got 661 respondents and I have the clean data in tabs in excel by segment that we are targeting.

I have both the actual # of respondents per question/answer as well as broken into percentages.

There are a few questions that I am looking to answer regarding who the buyer of said product is (age, gender, sports & activities participated). Where they are shopping for lifestyle clothing as well as were they expect to find it.

What would be the best approach to using the survey data to answer these questions? How would you approach this business issue through the data from the survey?

Adam Ramshaw says

Jon,

Sounds like you have the data ready. Unfortunately it’s very difficult to outline exactly how to use the data without seeing and knowing the questions you’re trying to answer. A simple bar chart of the different values could work and well as an over/under representation by group. I hope that helps a little.

Adam

Mark Koss says

Hi Adam, I think the link is broken as it takes me to a how to do NPS excel sheet versus a sheet showing how to do excel survey analysis. Could you update the link or send me the excel referenced above? Thanks in advance.

Adam Ramshaw says

Mark,

Thanks for letting us know. You were right — the link went to the wrong download but it’s been fixed now.

Thanks again.

Adam

Tunde Balogun says

Thanks for the insights provided on the subject matter, Adam. It appears to me that I would also find it useful for academic research too. I hope you would be there if I should get stuck anywhere along the line.

Regards,