Fields as varied as marketing, product management, political research, and psychology all rely on quantitative research. It helps provide solid, irrefutable data that can be extrapolated to better understand the beliefs, habits, and tendencies of larger groups of people. But in order for quantitative research to be useful it must be carried out effectively, following best practices in terms of sample selection, question construction, and methodology. All of this can be informed by the type of quantitative research you use.
Consider this analogy: picture yourself camping and wanting to explore the area. Think of a topographical map as a form of quantitative research, providing precise details like coordinates, distances, and elevations, enabling accurate location identification. Another map may focus solely on landmarks, representing a different type of information. In contrast, qualitative research, akin to storytelling, offers narratives from fellow campers sharing their experiences on paths leading to scenic waterfalls and interesting sites.
All these sources can complement each other to guide your journey. However, if you misinterpret the topographical map or choose an unsuitable one, it won't help you find the right path through the woods. This underscores the importance of familiarizing yourself with various types of quantitative research, understanding their benefits, and recognizing when to apply them, as well as knowing how qualitative research can enhance their effectiveness.
Understanding quantitative research
Quantitative research is the process by which data is collected and analyzed with the goal of uncovering patterns, relationships, and insights that can be expressed in numerical terms. By quantifying and measuring phenomena, you’re able to draw objective conclusions about a larger group, including better understanding user behaviors, preferences, and market trends.
When a product manager needs to gather information about how users are adopting a new feature, they may look at user engagement, click-through rates, and conversion metrics. By quantifying usage via these metrics, they can identify areas for improvement, prioritize changes, and optimize the user experience.
For a UX researcher, quantitative research helps identify broader patterns in user behavior, such as which design elements lead to higher engagement or where users drop off in their journey. Broad data about patterns like these can help create more user-centric designs and improve overall usability.
The quantitative research process typically follows a few clearly defined steps.
Define research objectives: Start by figuring out exactly why you’re undertaking the research, whether it’s to assess user needs for a new feature or evaluating the usability of a proposed interface update.
Formulate questions or hypotheses: Come up with an educated guess based on the objectives, as well as some overarching questions the research may help answer.
Choose your research design: Select an appropriate research design based on the objectives. This will in turn inform the sampling strategy and data collection instruments.
Collect data: Recruit participants and share your study.
Analyze and interpret data: Use statistical analysis software (e.g. R, Python, SPSS, Excel) to process and analyze the collected data, and then determine what it reveals about user behavior, preferences, or campaign effectiveness.
Communicate findings: Present the results to relevant stakeholders via reports, dashboards, or presentations.
Apply insights and iterate: Apply any insights gleaned from the findings and iterate based on the results.
What we’re focused on today is step three – figuring out the right shape and design of the research initiative. There are no wrong choices, just different methodologies for different situations.
Characteristics of quantitative research
Thanks to its systematic and data-driven nature, quantitative research generates data with consistent qualities. Here are some of the main ones to keep in mind.
Numerical data collection: Quantitative research is interested in numerical data only, including counts, measurements, ratings, and percentages.
Structured instruments: Tools like surveys, questionnaires, experiments, and observations are conducted methodically, utilizing standardized procedures. The idea is to minimize variation so the data is more reliable.
Large sample sizes: A large group of subjects helps ensure the statistical validity and generalizability of the findings. Samples should contain a representative portion of the target population.
Replicability: Other researchers should be able to replicate the study using the same methods and achieve similar results.
Hypothesis testing: Quantitative research often closely follows the scientific method, meaning a hypothesis will be proposed and then tested via the research.
Objectivity: The results should lead to conclusions based on statistical evidence. This makes it appealing to decision-makers.
Types of quantitative research
Quantitative research comes in various forms, each tailored to specific objectives and scenarios.
In this section, we explore the various methodologies and approaches you can use to gather valuable insights, so you can feel equipped with the knowledge you need to make informed research decisions.
Descriptive research aims to provide an in-depth and accurate description of a phenomenon as it exists naturally, without any researcher input or manipulation. It focuses on describing a clear and accurate picture of the subject – its “what” and “how.” Surveys, observational studies, and content analysis are common methods used in this type of research.
For example, if you wanted to better understand user navigation patterns within a website, you might utilize descriptive research to understand how users navigate through different product pages, whether they browse or search, and which pathways lead to the most conversions. You might use website data, user interaction logs, and possibly demographic information to gain this descriptive picture of current user behavior.
Survey research involves collecting data from a sample of participants using standardized questionnaires or interviews. These questions are closed-ended, meaning they involve multiple choices, yes-or-no answers, or the implementation of a Likert scale, which asks users to rate responses on a numbered scale. Survey research aims to gather insights about how participants perceive or engage with a particular topic. When done at scale and through careful representational sampling, these results can be extrapolated across the larger population.
For example, if you wanted to conduct a survey to assess customer satisfaction, you might design a questionnaire soliciting feedback about product features, ease of use, and overall user experience. You could target the survey to a representative subset of the audience that had used the feature at some point and deliver it via push notifications or email. This could be used to create percentages, averages, and correlations that reflect how users feel about the new feature.
Perhaps certain demographics used the feature more frequently than others, and may respond to marketing built around it. This sort of actionable insight would only be possible via the survey research.
Correlational research examines the relationship between variables without manipulating them, providing insight into how changes in one aspect might impact another. The goal is to identify and quantify the strength and direction of the relationship between the two variables. A positive correlation means an increase in one leads to an increase in the other; a negative correlation means they move in the opposition. It’s also possible for there to be no correlation between the two variables.
For example, let’s say you’re trying to figure out if there’s a connection between an app’s loading times and user satisfaction. You could collect data about app loading times (in seconds) and user satisfaction scores (from 1 to 10) in a sample of app users. These could be culled from app performance monitoring tools and post-interaction user surveys. After that, you could use a correlation coefficient to determine if there’s any connection between the two metrics and, if so, how strong it is. A strong positive correlation may suggest a new area to prioritize in development.
Experimental research, true to its name, involves researchers manipulating one or more variables to observe their effect on a dependent variable. It aims to establish cause-and-effect relationships between the variables.
A classic example of experimental research in UX and marketing is the A/B test. If a product marketer wanted to test the impact of two different CTA buttons on clickthrough rates, they could randomly assign two variations of a new UI to users and measure the number of clicks received on each. Perhaps they had hypothesized that red buttons would generate more clickthrough. Following the A/B test, they’d have clear data across a representative sample, which could then be used to inform implementation.
Often undertaken due to practical or ethical reasons, quasi-experimental research shares many similarities with experimental research but it lacks full experimental control. It involves manipulating an independent variable to observe its effects on a dependent variable, just as in experimental research, but with the admission that you haven't been able to manipulate them as precisely as you may have in a “true” experiment. It’s also sometimes called “causal comparative” research.
There are many reasons why researchers opt for quasi-experimental methods instead of full experimental research. These include ethical constraints (such as manipulating variables that could potentially harm participant data), historical comparisons (such as studying the effects of policy changes over time), and real-world complexity (it may be impractical to isolate every variable when studying something complex, like social media use across hemispheres).
But when it’s appropriate, quasi-experimental research can generate valid and usable data. If a marketing team wanted to evaluate the effectiveness of a new campaign using different ad formats, they could compare the engagement metrics of users exposed to each type of ad. This wouldn’t be a true experiment, as individuals weren’t randomly assigned specific ads, but there would be good ballpark metrics to evaluate, including click-through rates, conversion rates, and engagement.
Longitudinal vs cross-sectional research
Two types of research methodologies worth considering are cross-sectional studies – which occur at a single point in time – and longitudinal studies – which collect data from the same group of people over an extended period.
Cross-sectional studies are obviously faster, and provide an immediate and comprehensive snapshot of a given moment, while longitudinal studies are more costly but provide unparalleled insights into user behaviors and patterns over time.
For example, if you were tasked with understanding user engagement with a mobile app, you could check usage time, most popular features, and demographics with a cross-sectional study. That allows you to report quickly on how users are interacting with the product at the present moment. However, a longitudinal study may allow you to explore retention rates, peak usage periods, and potential drop-off points. Surveys to these users could show how their attitude toward the product changes over time. Some longitudinal studies last for years. Either can be useful given the right situation.
Navigating quantitative data for informed insights
Quantitative research serves as a powerful tool across various fields, offering the means to gather, analyze, and interpret data with numerical precision. It enables us to understand complex phenomena, user behaviors, and market trends, aiding decision-making processes.
Just as in our camping analogy, selecting the right "map" or research methodology is essential for a successful journey in uncovering insights. Whether you're exploring user navigation on a website, evaluating customer satisfaction, identifying correlations, conducting experiments, or comparing ad campaign effectiveness, understanding the nuances of each type of quantitative research is key to success.
Ready to embark on your research journey? Consider leveraging tools like Lyssna to streamline your quantitative research processes.
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