Numbers, percentages, and charts make quantitative research look like the ultimate decision-making tool.
But can it really give you the full picture? Do the numbers always add up?
In this article, you’ll discover the pros and cons of quantitative research and how to make the most of it.
By the end, you’ll know:
- The differences between qualitative and quantitative methods
- When to use quantitative research alone
- When to cross-check with qualitative research
TL;DR – Pros and Cons of Quantitative Research
Quantitative research helps shape the big picture fast. It’s great for spotting trends, yet it struggles to explain them. Even so, it’s well worth the effort. Especially if you know how to leverage its strengths and compensate for its shortcomings:
| 🟢 Advantages of quantitative research | 🔴 Disadvantages of quantitative research |
| Measurable results | Lacks depth |
| Scales fast | Limited by predefined questions |
| Easy to analyze | Can mislead if not executed properly |
| Reduces subjective bias | Doesn’t capture decision-making |
| Supports predictive decisions | Needs a large sample |
Before discussing these advantages and disadvantages of quantitative research, let’s introduce you to Marvin. Built as an end-to-end research repository, our AI-powered research assistant analyzes survey responses and tracks NPS trends. It even connects insights across multiple studies.
Create a free account today! See how Marvin can help you make sense of thousands of survey responses in hours instead of days.
What is Quantitative Research?
Quantitative research is a method for gathering structured data and analyzing it with numbers.
You collect responses through surveys, experiments, or tracking user behavior, then convert that data into measurable results. The focus is on quantity:
- How many users did something
- How often
- How much they prefer one option over another
In product development, quantitative research can track drop-off rates, feature adoption, response times, user sentiment, etc.
Why Do Researchers Use Quantitative Methods?
Researchers use quantitative methods to get clear, measurable answers to specific questions.
Quantifying user data helps remove guesswork and personal bias. It’s also excellent for spotting trends in large user bases. You can track changes over time, compare user groups, or test design variations.
For example, when launching an onboarding flow, you might survey users to measure ease of use. You could ask:
- On a scale from 1 to 5, how easy was it to complete the setup?
- How long did it take you to finish onboarding?
- Did you encounter any confusing steps? (Yes/No)
The results show that many users rate the flow as “difficult.” Quantitative data helps pinpoint who struggles the most and by how much.
If mobile users take 30% longer than desktop users, that signals a usability issue. Do they drop off at a specific step?
If so, a targeted fix, such as clearer instructions or larger tap targets, may be enough.
You might need a complete redesign if frustration has spread throughout the process.
These insights help you decide whether to tweak specific features or overhaul the experience entirely.
Advantages of Quantitative Research
In any UX research project, quantitative research gives you the numbers you need to:
- Validate insights
- Measure impact
- Track changes over time
Without quantification, research risks being mostly guesswork. That’s why researchers quantify qualitative data.
Below, we’ll explore the specific advantages of quantitative research in product design and development.
1. Measurable and Reliable Results
Quantitative research uses standardized methods, which reduces guesswork. It gives you structured, repeatable data to back your decisions.
First, you measure something — task completion rates, satisfaction scores, etc. Then, you work on your product, retest under similar conditions, and compare the results.
For example, you discover that users take an average of 30 seconds to complete a checkout flow.
You tweak the flow and retest to see if you’ve made the user experience faster or slower. This allows you to measure improvements and make informed design choices.
2. Scales Easily for Broader Insights
The more data you have, the more confident you can be in your conclusions.
Quantitative research lets you gather structured responses from a wider audience. While qualitative research focuses on small groups, quantitative surveys and analytics can reach thousands of users.
This is especially useful when designing products for a diverse user base or launching an app internationally. Five thousand survey responses better reflect user needs than 10 qualitative interviews.
And making sense of all that data doesn’t have to be complicated. Marvin, our UX research platform, can help you automate large-scale quantitative studies. With AI-powered analysis, you’ll quickly process thousands of survey responses and identify trends. Book a free demo today to see how Marvin can reduce your time to insights from days to hours.
3. Easy to Analyze and Compare
“Easy to analyze” might sound counterintuitive when you’re looking at thousands of data points. However, the tools in your UX tech stack can turn what appears to be a barrier into a significant benefit.
A/B tests, heatmaps, and survey results provide structured data that’s easy to break down and visualize.
Entertainment Partners uses Marvin to analyze thousands of NPS survey responses at once. Instead of manually sorting through feedback, they get structured insights instantly.
But our AI research assistant doesn’t stop there. Marvin allows you to organize insights and:
- Use built-in canvas and Kanban boards to visualize workflows
- Automatically generate charts and graphs to communicate your findings
- Integrate with affinity mapping tools such as Miro and FigJam
4. Eliminates Subjective Bias
Numbers remove personal opinions from the equation.
Qualitative research may be rich in detail, yet it’s easy to let personal interpretations color the results. Quantitative research is structured, reducing the risk of bias.
For example, in interviews, some users may tell you they “love” a new feature. But analytics may actually show low engagement, with the numbers telling a different story.
By combining both research methods, you make decisions based on real user behavior, not just assumptions or statements.
5. Supports Predictive Decision-Making
Past data helps you forecast trends and improve future products.
Collecting quantitative data over time, allows you to see patterns before they happen. For example, if support ticket volume spikes every time you release a new update, that’s a sign your onboarding process needs work.
Predictive insights are especially valuable in product development. Companies use AI-powered tools like Marvin to analyze large datasets and detect patterns early. If a survey shows declining satisfaction with a key feature, you can address the issue before users churn.
The more data you collect, the better your ability to make informed decisions. Rather than relying on gut feelings, you can let the numbers guide you.
Disadvantages of Quantitative Research
Numbers alone aren’t enough to drive UX research. Keep reading to discover the weaknesses of quantitative research, which you can counterbalance with qualitative research.
1. Lacks Depth and Context
Quantitative research can’t tell the whole story. It shows what’s happening but lacks the context behind it.
For example, a survey might tell you that 80% of users are satisfied. But was it the design? The speed? The support team? Without qualitative insights, you won’t know what truly shaped their experience.
This limitation affects product decisions. If a feature gets high ratings but low engagement, you’re left guessing why. Qualitative research, through interviews and open-ended feedback, fills in those gaps.
Previously, we mentioned how Entertainment Partners used Marvin to analyze thousands of NPS survey responses. While scores showed overall satisfaction, they needed to dig deeper.
Our AI research assistant also helped analyze open-ended responses. As a result, the company identified specific pain points that numbers alone wouldn’t have surfaced.
2. Limited by Predefined Questions
Surveys force users to pick from set responses, limiting their ability to share honest thoughts.
Let’s say you’re testing a new app feature. You ask, “How easy was it to use?” with options from 1 to 5.
A user who found it generally easy but frustrating in some instances has no way to explain that. Their experience gets reduced to a single number.
This can lead to incomplete or misleading insights. If you only rely on closed-ended questions, you risk missing critical details.
Adding open-ended responses or follow-up qualitative research helps fill the gaps.
3. Can Be Misleading Without Proper Analysis
Biased questions, small samples, or incomplete data can skew your quantitative findings.
If you ask, “How much do you love our new feature?” you’re already leading users toward a positive response.
If you only survey loyal users, your data won’t represent the entire audience.
And if you misinterpret an average score, you might overlook strong opinions hidden in the data.
Bad data leads to bad decisions. That’s why analysis tools help detect biases and highlight outliers so you get a more accurate picture.
4. Can’t Capture Decision-Making and User Behavior
People don’t always act how they say they will, and quantitative research won’t catch those disconnects.
A survey might show that users want a feature, but analytics could reveal that they rarely use it. A high NPS score might suggest loyalty, but churn data might tell another story.
Quantitative research is great for tracking behavior but not for explaining motivations. It shows patterns, but without qualitative methods, you’re left interpreting numbers instead of understanding users.
5. Needs a Sufficient Sample for Reliable Insights
Too few responses can make your data misleading.
If only 20 people take your survey, their opinions might not represent the full audience. One or two extreme responses could skew the results.
Larger samples give more accurate insights, but collecting enough data takes time and effort.
Tools like Marvin help by automating data collection and analysis. They make gathering and processing feedback at scale easier so you can trust your findings.
When to Choose Quantitative Research Over Qualitative
Quantitative research works well when you already have a hypothesis and need to test it with structured data. It’s best for questions that require numbers, comparisons, or statistical proof.
Use quantitative research over qualitative to:
- Track trends over time: Measuring how satisfaction scores change after a redesign
- Compare different user groups: Checking if mobile users rate a feature higher than desktop users
- Ensure accuracy with a large sample: Validating a new feature with survey data
- Gather objective data to support a business decision: Proving a design change increases conversions
Frequently Asked Questions (FAQs)
Now that you know the quantitative research pros and cons, here are some valuable FAQs:
What is the Difference Between Quantitative and Qualitative Research Methodologies?
Quantitative research focuses on numbers, patterns, and measurable data. It answers “how many” or “how often” questions.
Qualitative research explores behaviors, motivations, and emotions. It answers “why” and “how” questions.
For example, survey ratings are quantitative, while user interviews explaining frustrations are qualitative. These methods work best together.
What Are the Major Limitations Of Quantitative Research?
Quantitative research lacks context and depth.
Predefined questions limit responses, and large samples don’t always capture unique user experiences. It also assumes that users understand survey questions correctly, which isn’t always true.
In short, numbers help, but they don’t tell the full story.
How Do the Disadvantages of Quantitative Research Affect Data Interpretation?
Without context, numbers can be misleading.
High survey ratings might not mean true satisfaction. Averages can hide extreme opinions, and biased questions lead to skewed results.
If you rely only on numbers, you might miss important details. Combining quantitative with qualitative research gives a clearer, more accurate picture.
Conclusion
To use or not to use quantitative research isn’t the question. Instead, you want to know when to use it, because it’s essential for making data-driven decisions.
You’ve seen the pros and cons of quantitative research. And you can tell that, whatever shortcomings it has, qualitative research makes up for them.
Numbers show the trends, and stories explain them. You need quantitative research to see patterns at scale, even in your qualitative user data. And Marvin helps with both.
From structured data to open-ended insights, our AI-powered research assistant centralizes and analyzes everything. Create your free Marvin account today to get the best of both research worlds without the manual grind.
