Using Quantitative Research Effectively

Identifying quantitative methods and collecting data.

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Quantitative research allows you to generalise the results from a sample group to an entire group of people.

Being both structured and statistical, quantitative research provides you with the ability to draw conclusions and make an educated decision about a course of action.

Most quantitative research is used to prove or disprove a predetermined hypothesis that you may have come up with while conducting qualitative research.

Quantitative questions take up the major bulk of most surveys, but they are often used inefficiently. When creating a quantitative question, make sure it will allow you to achieve one of these three goals:

  • 1. Defining a characteristic of your respondents: All closed-ended questions aim to better define a characteristic of your respondents. This could include gleaning information about:
  • – A trait: Identifying age, gender, race, income, etc.
  • – A behaviour: Identifying respondents’ habits, such as hours spent on the internet each week, commuting habits, exercise routines, etc.
  • – An opinion or attitude: Identifying respondents’ thoughts, such as how satisfied a person is with a product or whether they like their elected politician.
  • Knowing these characteristics helps you understand who your respondents are, how they act and what they like or expect.

  • 2. Measuring trends in your data: By running the same survey over time, you can start to recognise trends in your data. Maybe opinions are slowly shifting in a particular direction, or maybe you’ll recognise seasonal patterns? The bottom line is that looking at trends over time gives your survey results context.
  • For example, let’s suppose you ask your customers to rate their satisfaction with your customer service on a scale from “very satisfied” to “very dissatisfied”, and 20 percent say they are “very satisfied”. Although it’s good to know where you currently stand, this number can also be used as a yardstick to measure your progress in the future.

    Let’s suppose that after the original survey you make changes to better meet customer needs. You can now conduct the same survey again and see whether the percentage of customers who are “very satisfied” has risen or fallen. This allows you to effectively measure the progress you are making with customer satisfaction over time as well as to directly measure the effects of new initiatives and processes implemented between surveys.

  • 3. Comparing Groups: Survey questions can also be used to draw comparisons between groups of respondents.
  • Let’s go back to the example above. By adding demographic questions about your respondents’ age, gender and income, you will be able to compare questions such as: Are young men more satisfied with your service than older women?

    By comparing different groups, you’ll be able to work out who to target, how to message them and when your product needs to change to fit a particular market.

    Alternate uses of quantitative research

    Beyond the realm of surveys, you can also use quantitative research in various ways. Check out a couple of examples to show how:

  • 1. Observing real data: The chances are that you collect data every day that can help you make quantitatively driven decisions. It could be anything from how long a customer visits your website to when your sales peak seasons are. This real world information, which is now referred to as Big Data, can be just as useful in directing your decisions as conducting your own research!
  • Big Data can tell you a lot about what people do, but remember that it seldom tells you why they act in a certain way. You’ll need more direct qualitative and quantitative research for that!

  • 2. Causal experimentation: To try to understand that “why” a little better, causal experimentation seeks to determine a cause-effect relationship by watching what happens when something new is added to an environment. This new element could be anything from measuring the effect of a commercial on sales or office parties on employee engagement.
  • Let suppose you plan to change the packaging for something you sell and you want to understand its potential impact on sales. You could introduce the new packaging in just a few shops and compare its sales to the old packaging. Causal experimentation is the concept behind A/B testing.

    Now you have the tools to hit the ground running, but don’t forget to incorporate some qualitative research before you do. For more information about how to use both in your research design, check out this page.

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