Data. Whether the word makes you cringe or grin, there’s no denying that data is an essential part of today’s business world.
We have more of it at our fingertips than ever before. And, provided you know what you’re doing, it can be your secret weapon for success.
Rather than relying on your own assumptions or preconceived notions, data gives you the facts to make well-informed strategic decisions. And this reduces risk – music to any manager or business leader’s ears. You can use data to confirm if there’s a market for your product or service, and how big it is. To understand your target audience and their behaviours. To identify issues and opportunities, and act on them. And to establish what you’re doing well – and what you’re not.
So how do you get this data, and make use of it? Through research and analysis. Quantitative research, in particular, can provide you with a whole host of useful data. In this article, you’ll learn what quantitative research is, explore its advantages and disadvantages, and see a variety of quantitative research methods and examples. Finally, you’ll discover how to analyse quantitative data and how SurveyMonkey’s tools and templates can help.
Quantitative research involves gathering numerical data – that is, data that can be turned into numbers. It’s objective and involves hard facts. This data is then analysed using statistics to draw conclusions. Quantitative research is typified by closed-ended questions and a fixed set of answer options, while open-ended questions are examples of qualitative research. Find out more about when to use qualitative vs quantitative research.
There are different ways of carrying out quantitative research. For a start, there’s primary research, where you, or someone on your behalf, conducts original research for a specific purpose. You could do this by using surveys, observations or experiments. For instance, you might send out a new product launch survey to a group of people who are representative of your target market.
Then there’s secondary research. Secondary research involves examining public data in order to draw conclusions relevant to your business or organisation. For example, you might look at Office of National Statistics data on the earnings of employees in specific geographic areas to determine the likely earnings of your target audience and inform your pricing strategy.
There are four main types of quantitative research: descriptive, correlational, causal-comparative (also known as quasi-experimental) and experimental.
Descriptive research uses observation or measurement to describe a situation or phenomenon. A staff satisfaction survey is a good example, since it aims to assess how satisfied employees are with different aspects of their work at a particular point in time. You could send this survey out on a regular basis, such as once a year, to measure how satisfaction has changed over time.
Correlational research involves measuring how one variable impacts another by looking at patterns or trends. It’s not focused on why one thing impacts another, but rather what that relationship looks like.
For example, you could use correlational research to determine if there’s a correlation between education level and spending habits. Because correlational research doesn’t look at causal relationships, we wouldn’t examine whether people’s level of education influences their spending habits, or vice versa – we’d instead be looking at whether there’s an association between them.
This type of quantitative research aims to evaluate the cause-and-effect relationship between variables. Or to put it more simply, does x cause y? In this sense it’s very similar to experimental research. But researchers conducting causal-comparative research don’t manipulate the independent variable – or the cause. There’s also no control group.
Let’s look at an example.
Imagine your organisation plans to introduce a new suite of professional development tools and training sessions, and you want to measure whether these initiatives have improved employees’ perceptions of career development opportunities. You could send out a career development survey as you launch the initiatives, and then again six months later. By including questions like “Do you feel career development opportunities have improved in the past six months?”, as well as questions about whether staff are satisfied with the investment you make in training and education, you can assess whether your work has had an impact.
Like causal-comparative research, experimental research studies cause-and-effect relationships. But it involves the researcher manipulating or controlling independent variables (or causes) to measure their impact on dependent variables (or effects). This type of research must include a control group.
Experimental research is what we typically think of when we think about scientific experiments. This research method can, however, also be applied to other contexts.
Say you want to make some changes to your app and assess whether the changes improve the user experience. You could send out the same product feedback survey to a group using the old version of the app, and to a group of users using the new version. Comparing the results from each group will allow you to see what difference your changes have made.
Once you have your precious quantitative data, you have to make sense of it. We recommend starting out by developing a data analysis plan. As part of this, you’ll link your findings to your initial research questions and decide which of the two main statistical analysis techniques to use: descriptive or inferential statistics.
The good news? You don’t have to go it alone. When you create a survey on SurveyMonkey, you also benefit from analysis tools such as automatic calculations, filtering and comparison options, and automatically generated charts and graphs.
When you choose descriptive statistics, essentially you’re just summarising what the data shows. You might translate the hard numbers back into words, talk about averages and percentages, or display your findings visually on graphs or charts.
Inferential statistics goes a step further than descriptive statistics. It involves making predictions or generalisations based on your findings.
Quantitative data really comes into its own when you compare results across different segments or groups of people. For instance, perhaps younger employees are more positive about your organisation’s new career development opportunities.
There’s a reason quantitative research is the first port of call for people carrying out all sorts of research, from customer satisfaction to market research and staff surveys. Quantitative research has the following benefits:
Quantitative research is a great way to confirm or disprove a hypothesis and measure trends over time. Plus, you might already be gathering some of the data you need to carry out your research, such as website analytics or sales figures, meaning you can reduce your research spend even further.
That said, quantitative research does have some drawbacks:
Since quantitative research is structured, you can’t explore topics in more depth, meaning you can miss out on key information if you’ve forgotten to ask about it or weren’t aware of particular issues.
So what are some examples of quantitative research in the wild? In what areas can you use it effectively?
Quantitative research can be particularly useful for market research, as it provides the statistics on which to base your marketing and strategic decisions.
For example, you might want to understand the buying behaviour of a typical consumer in your target market, and could ask questions along these lines:
o Once a week
o A few times a month
o Once a month
o Less than once a month
o Shopping in person
o TV adverts
o Online adverts
o Social media
o Searching on the internet
o Word of mouth
o Other (please specify below)
o Brand A
o Brand B
o Brand C
o Brand D
Quantitative questions can also help you build up a picture of your target audience and different segments within it. For instance, you could ask about their demographics, such as age, gender and race, or about a certain behaviour – say, their eating, exercise or shopping habits.
Some sample quantitative questions include:
o 17 or younger
o 60 or older
o Never married
o Less than once a month
o 1–2 times a month
o 3–5 times a month
o More than 5 times a month
o Employed, working 1–39 hours per week
o Employed, working 40 or more hours per week
o Not employed, looking for work
o Not employed, not looking for work
o Disabled, not able to work
Let’s say you host a conference and want to gather feedback from speakers and attendees to find out what went well, what didn’t and how you can improve for next time. A professional event feedback survey can include quantitative questions like:
o More than 6
o Very satisfied
o Neither satisfied nor dissatisfied
o Very dissatisfied
And when it comes to making sense of the results, SurveyMonkey pulls it out of the bag again. Just head to “analyse results” and enjoy the filtering, comparison, calculations and data visualisation options, all ready for you to use.
Quantitative research is a valuable tool, providing useful insights into all manner of topics. But, like any tool, you need to know how to use it. You need to start by deciding whether quantitative research is the right method for answering your research question, consider whether you should also incorporate qualitative research, which research method will allow you to answer your research question, and finally, how you’re going to analyse the resulting data. Find out how SurveyMonkey can help you tap into market research insights.
There are four main types of quantitative research: descriptive, correlational, causal-comparative (also known as quasi-experimental) and experimental research. Descriptive research uses observation or measurement to describe a situation or phenomenon. Correlational research looks at the association between different factors. Causal-comparative and experimental research both look at the cause-and-effect relationship between variables, but experimental researchers manipulate the cause to examine the effect, while causal-comparative research doesn’t involve any manipulation.
Quantitative research is the process of gathering objective facts and numerical data. In surveys, quantitative questions are closed-ended questions with predetermined answer options.
Quantitative research can be used in a variety of settings to gather objective data that’s easy to compare and analyse. For instance, it can be used in market research to find out about consumers’ buying habits or brand awareness, where you would ask a series of closed-ended questions.