5 MIN READ
The Best Data Collection Methods for Quantitative Research
Quantitative research is an effective way to learn about your users and build products that are well-suited to their needs. Statistical insights from quantitative research can help you predict user behavior accurately. However, to derive these meaningful insights, you need to collect the right data.
So what's the best way to find the data you need for quantitative research?
In this article, we will discuss seven of the best quantitative data collection methods you can try out for yourself. Here's what we'll cover:
- Secondary quantitative research
- Research questionnaires and surveys
- Structured User Interviews
- Quantitative Observation
- Data Analytics
- A/B Testing
Secondary quantitative research
Someone in your industry has done groundwork related to your research subject matter and has published meaningful results. These results are categorized as secondary data and are an excellent resource for supplementary data. You can find them in case studies, round-up industry reports, customer reviews, public records, peer-reviewed journals, podcasts, literature reviews, and similar documentation.
Secondary data is cost-effective, easily accessible, and has a fast speed-to-insight, making it one of the most preferred quantitative research methods. Unlike primary data produced from scratch, secondary data is already synthesized into done-for-you insights. So, you only have to retrieve the existing information, find what you need, and use it as a base for your current research efforts.
Ultimately, secondary data gives you a headstart in understanding users' preferences and behaviors without doing the heavy lifting.
Limitations of secondary quantitative research
- You have zero control over secondary data quality since you use the information as a third party.
- Secondary data can be inaccurate or outdated, which distorts your research outcomes.
- It only provides supplementary research insights.
Research questionnaires and surveys
While questionnaires are typically administered as part of interviews, they can also serve as a stand-alone quantitative data collection method. A research questionnaire is a data-gathering tool consisting of questions and predefined options that participants choose from. Questionnaires were first developed in 1838 by the Statistical Society of London.
Research questionnaires can be self-administered or researcher-administered depending on the research context and complexities. Self-administered questionnaires contain standardized questions that users can respond to with little or no guidance. For example, you can send out NPS surveys to users to know how likely they are to recommend your product to others.
On the other hand, researcher-administered questionnaires are administered by a researcher, usually, in the form of an interview—we would delve further into interviews in subsequent sections. In addition, researcher-administered questionnaires enable you to gain deeper insights into user behavior than self-administered questionnaires.
To gather valuable data with a research survey, you need to ask concise and logical questions relevant to the research context. More importantly, these questions must be set on a user-friendly platform that's easily accessible. That said, here are a few tips for creating an effective questionnaire:
- Keep it short and simple—avoid vagueness
- Split your questions into multiple pages. You don't have to cram all the questions into a single page
- Avoid ambiguity
- Ensure that your questionnaire is easily-accessible so users can complete it conveniently.
Benefits of using a questionnaire for quantitative research
- With questionnaires, you can quickly collect large volumes of data from many participants in a relatively short period.
- You incur little or no overhead costs with questionnaires. For instance, you can use a form builder like Google Forms to create and share an online questionnaire for free.
- Questionnaires enable rapid speed-to-insight, which results in faster decision-making. You can gain insights about users in as little as 24 hours, depending on your research scale. In addition, most online questionnaire builders automatically categorize and analyze results, making it easier for you to derive meaningful insights quickly.
Limitations of a questionnaire
- Survey response bias can affect the quality of data and insights derived from questionnaires.
- It can pool responses from people who have little or no knowledge about the research context.
Structured User interviews
Originally, interviews were used as a qualitative data collection method due to their in-depth nature. However, these days, they also serve as a reliable means of gathering statistical information related to your users.
A structured interview has a strict sequence and requires the user to answer a set of predetermined closed-ended questions following strict guidelines. Examples of structured interview questions include:
- How long have you used our product?
- What social media platform do you use the most?
Benefits of using structured user interviews for quantitative research
- User interviews help you contextualize data accurately, leading to better research insights.
- User interviews adhere to an interview schedule that helps you gather the exact data needed for your research. Gathering concise and precise data leads to rapid speed-to-insight because you don't waste time sifting through irrelevant information.
Limitations of structured user interviews
- They are time-consuming and expensive.
- Conducting successful user interviews requires an advanced level of expertise.
Quantitative observation method or structured observation is an ethnographic data collection technique where the researcher studies users as they interact in a natural or controlled environment to understand one or more specific behaviors better. For example, let's say you own a social media scheduling tool and want to learn more about your target audience. In this case, you could join a Slack community of social media managers to get first-hand information about when and how they prefer to share content on social media.
The difference between quantitative observation and other direct observation methods like participant observation and natural observation is that it focuses on a limited set of behaviors. And focusing on a limited number of behaviors allows your product team to quantify their observations.
Benefits of quantitative observation
- Data from quantitative observation offers a high level of accuracy and validity.
- It leads to reliable data insights.
Limitations of quantitative observation
- Quantitative observation is time-consuming.
- It has a slow speed-to-insight, especially when it involves studying a large population of users.
A/B testing or split testing is a randomized controlled experiment. Here, the researcher tests two versions of a variable with an audience and runs a statistical analysis to determine which version performs better and drives more business metrics. Basically, you’re comparing two variations of the same thing to see which version is a better fit for your audience. A/B testing is the best way to remove uncertainty from decision-making and leverage quantitative data to make product changes that resonate with your target audience.
You can run A/B tests on nearly all the aspects of your business’s digital presence and assets—from websites and landing pages to email drips, newsletters, and CTAs. For instance, you can use A/B tests to determine whether adding new Header to your landing page will improve conversion rate and reduce bounce rate.
How to run A/B tests
- Pick one variable to test—like a CTA or landing page header—so your efforts are unified.
- Choose a primary metric to focus on, like conversion rate or bounce rate, depending on your goal.
- Create the variation of the variable for the tests.
- Split the user sample group into two equal and random sets, and expose these to separate versions of the variable simultaneously.
- Use A/B testing tools like Optimizely and Google Optimize to run your experiments and collect quantitative data.
- Determine the statistical significance of the results. Statistical significance is the likelihood that the difference in conversion rates between a given variation and the baseline is not due to random chance.
Data analytics is the process of extracting valuable insights from quantitative data. While your product team has done lots of heavy-lifting to gather quantitative data, their work doesn’t end there—quantitative data isn’t helpful on its own; you need to make sense of the numbers and extract relevant information that can lead to better business decision-making. Data analysts examine quantitative data sets to find trends and draw conclusions about the information they contain.
Steps for Quantitative Data Analysis
- Clean the datasets to remove duplicate information and anomalies that affect your final results.
- Analyze the data using the best-fit techniques such as cluster analysis, regression analysis, and time-series analysis. The method you choose will depend on the type of data you’re working with and what you want to achieve at the end of the day. For example, you should use regression analysis if you're going to make predictions from data sets.
- Interpret and share the results of the analytics. Be sure to share the results in an easily-digestible format like a chart or a graph, and tie your findings to specific business goals. You can use Tableau for data visualization.
Heatmaps are graphical representations of data where colors denote values. They help you understand the distribution of large volumes of data. Instead of creating complex data tables, you can use heatmaps to depict extensive data patterns. Common types of heatmaps include scroll maps, move maps and click maps.
With website heatmaps, you can analyze your website’s performance and see how users interact with different web pages. For example, you can see what buttons they click on more often, the sections of the site they scroll through, and what they ignore entirely. You can create website heatmaps using tools like Hotjar and Clicktale.
The best quantitative data collection techniques are the ones that provide accurate and reliable data that leads to valuable user insights. While we've highlighted some of the best quantitative data collection methods, it's up to you to decide which ones are best suited for your research needs.
Quantitative research is a great way to learn about your users, but it isn't exhaustive. To get holistic insights on user behavior, you need to embrace mixed methods research—that's the best way to win.