Being both structured and statistical, quantitative research provides you with the ability to draw conclusions and make an educated decision on 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 are often used inefficiently. When creating a quantitative question, make sure it will allow you to reach 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 gaining information on:
– A trait: Identifying age, gender, race, income, etc.
– A behavior: Identifying respondent habits, such as hours spent on the internet each week, commuting habits, exercise routines, etc.
– An opinion or attitude: Identifying respondent’s 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 begin to recognize trends in your data. Maybe opinions are slowly shifting in a particular direction, or maybe you’ll recognize seasonal patterns? Bottom line, looking at trends over time gives your survey results context.
For example, say you ask your customers to rate their satisfaction on your customer service a scale from “very satisfied” to “very dissatisfied,” and 20 percent say they are “very satisfied.” Though it’s good to know where you stand currently, this number can also be used as a yardstick to measure your progress in the future.
Let’s say after the original survey you make changes to better meet customer needs. You can now conduct the same survey again and see whether the percent 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 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 are more satisfied with your service than older women?
By comparing different groups, you’ll be able to figure out who to target, how to message them, and when your product needs to change to fit a particular market. You can also compare the percent of your customers who are satisfied to a benchmark in order to see how you stand compared to your competitors.
Beyond the realm of surveys, you can also use quantitative research in various ways. Check out a couple of examples how:
1. Observing real data: Chances are 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 what seasons your sales peak. This real world information, lately earning the name 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 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.
Say you plan on changing the packaging on something you sell, and you want to understand it’s potential impact on sales. You could introduce the new packaging in just a few stores and compare its sales to the old packaging. Causal experimentation is the concept behind A/B testing.
Now you’ve got the tools to hit the ground running, but don’t forget to incorporate some qualitative research before you do. For more information on how to use both in your research design, check out this article.
This article is part of SurveyMonkey’s Surveys 101 project. We hope to help more people create smart surveys. Learn more about the project and our involvement in the research community.
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