When it comes to quantitative research, it’s fair to say many people in the industry consider it to be a minor (or less credible) form of research. The truth is that surveys and other unmoderated tasks are amazing sources of information, but only when used at the right time in the research process… and when done properly.

There are ways of ensuring quantitative research – and the insights you’ll get from it – is relevant to your business or brand. Start with building each campaign on the right foundations, with clear objectives, audience familiarity and the right survey design (we’ll touch on all of these later) will mean you get the right results out.

So, that’s what we’re talking about today: how to get the most out of your next survey. Starting with participants and qualification criteria, onto exporting data and finally utilising survey design.

📊 Get the data you need

Obvious, eh? This starts with your research audience. With survey participants, some of the common pitfalls are with data validity, how representative the audience is of the overall demographics you are targeting, and how credible the audiences’ opinions are.

Putting the right data into the survey means you get better data out of it. Consider demographics that mean more to you: age groups, job titles, previous experiences, etc. Think about the data that might be a factor in the way they interact with your brand/product/service and capture that information to analyse trends further down the line.

🤔 Drawing information from the data

Let’s look at the two skills that are central to getting the right information from your data.

1. Identifying inconsistencies

It’s one thing to get data, but another to see what it is telling you (and what it isn’t). The first thing to note with data consistencies is the type of questions in use. If you have a lot of manual input questions (such as – tell us about…), then analysing the results and sentiment is going to take you a lot longer.

On the other hand, scoring and weighted questions aren’t always what they seem. For example, asking someone how proficient they are using a computer may mean different things to different people. So, a web developer may give themselves a 4 out of 5 because they are proficient in HTML, CSS, JavaScript, Python and PHP, but not Angular; however, my mum will also call herself a 4 out of 5 because she knows how to load Zoom and launch a video call, use websites and sets up her friends’ online poker tournaments.

The point is if the goalposts are different for different demographics or audiences, you won’t get consistent results. This is only an example, but sliders and scales are often inconsistent and subjective.

It’s great if you can spot this early on in the process, but what if you’re inheriting the data? Can you go through it and tell if someone is bending the truth to appear to be in the demographic you’re looking for? The short answer is ‘yes’, but it’s very time consuming depending on the amount of data you’re working with.

2. Building visualisations

This brings us on nicely to lovely clean data. Let’s get this clear now: not everyone has the same level of understanding when it comes to numbers, so reporting on a 42% uplift in X from Y won’t mean a lot to everyone.

Depending on the purpose of the research, you’re going to need to take the numbers and turn them into a relatable visual or facts for most people to understand. In its simplest form, these can be translated into bullet point takeaways, but you can get more sophisticated with graphs, heatmaps and even 3D visualisations.

Excel is a great place to start working on data visualisations, but to design your graphs and charts easily use a tool like Canva or Venngage.

🦉 Quantitative survey knowledge, insights and wisdom

Knowledge of what’s happening in your surveys feeds insights and key findings from your research, which in turn builds internal wisdom for an organisation.

5 stages of survey analysis. Data, Informtation, Knowledge, Insight and Wisdom

We’ll start with knowledge. This comes from getting the right information to tell you X and Y correlate, meaning if people are X they are more likely to Y – this is data analysis in its most simple form. Going beyond two units, you may start to build on that knowledge and see multiple things working together.

When doing research, a lot of the time you’re capturing demographic information such as age, gender, location, previous experience of the topic and much more. All this knowledge will lead to its own stories and this is where insights come in, allowing you to build your personas or define your audience’s characteristics.

Time for an example: if most men prefer green in a survey, this doesn’t mean all future insights point towards men liking green.

If you break down the results to read:

  • + 135 men over 50 like green
  • + 201 men between 35-50 like purple
  • + 197 men 25-35 like brown
  • + 90 men 18-25 like green

The result here is not a unanimous ‘men like green’. That is the most common response, but we can continue to break the numbers down and identify trends that are not apparent depending on the target market.

This example may be oversimplified, but the point we are trying to get across is: getting insights from a quantitative survey means not taking the information at face value. Get your hands dirty and chop up the data to see trends that may not have been immediately apparent at a first glance.

This leads on to wisdom, and wisdom comes in two parts.

1. Making actionable change off the back of the insights
Improving your product/service by reporting research findings back to the board.

2. Using the insights to shape future research
Depending on when you’re doing this research, you may have further qualitative research in mind to follow up on the initial insights you’ve gathered. This insight will help shape what you want to drill down on with these projects and narrow down into exactly what you want to get out of user research.

🖌️ Design the right survey

We’re going right back to the start with this one. Design is critical to the success of any quantitative survey or task, and there are key elements to consider to ensure you can practice these insights.

+ Make your questions clear: don’t leave room for ambiguity.

+ Minimise manual input questions: for big surveys, it’s hard to get data out.

+ Show progress: people like seeing how far through they are.

+ Minimise repetition: people HATE being asked the same things more than once. It creates survey fatigue.

+ Remove bias in design: survey bias skews results. Don’t lead participants down a path that you want to prove with your results.

+ Design for all: people have different levels of accessibility needs. Don’t leave people out.

There are more than six tips for design in surveys, but we’ll save that for a different article.

It is important to know that it makes a huge difference to getting the right insights from a quantitative survey. If you want to talk to an expert about your next quantitative research project, get in touch.



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At People for Research, we recruit participants for UX and usability testing and market research. We work with award winning UX agencies across the UK and partner up with a number of end clients who are leading the way with in-house user experience and insight.