Evidence rating

Effect score (E): 4/5 Evidence for the underlying principle or measurement method.

Transfer score (T): 4/5 Evidence for reliable results in digital product practice.

Evidence level: High

[Plausible based on UX research] Choose the method from the question: qualitative data often explain why, while quantitative data estimate frequency or differences.

Scale and rating rules

Effect score: 5 established · 4 robust · 3 context-dependent · 2 weak · 1 preliminary.

Transfer score: 5 practice-proven · 4 field-supported · 3 partially supported · 2 plausible · 1 hypothesis.

Without a robust scientific or normative source, E is capped at 3. Blog and agency opinions support at most T=2. If evidence conflicts, the more conservative score applies until justified otherwise.

Evidence level: High = E ≥ 4 and T ≥ 3; Moderate = E ≥ 3; Preliminary = E ≤ 2 or T ≤ 1.

Quantitative vs. Qualitative in UX

UX research uses two basic methodological approaches: quantitative and qualitative methods. While quantitative methods focus on measurable, statistically analyzable data, qualitative methods enable a deeper understanding of individual experiences and perceptions. In practice, both approaches complement each other - and should not be seen as opposites, but rather as complementary perspectives.

Quantitative Methods - When Numbers Speak

Quantitative methods provide answers to questions such as: “How many users click on the call-to-action?”, “How long does it take to fill out a form?” or “Which prototype achieves the higher conversion rate?” These methods are based on structured data formats - usually numerical - that are based on scales, counts or time measurements.

A team compares two landing-page variants in an A/B test. Variant A has a large image; variant B has a short product description. After a week, the observed conversion rate in B is 12% higher relative to A. This is initially a descriptive difference; causal interpretation requires an appropriately randomized experiment, adequate data, and a prespecified analysis.

Typical quantitative UX methods:

Standardized online surveys (e.g. SUS, UEQ)
Web analytics (e.g. bounce rate, funnel drop-offs)
Task Success Rate & Time-on-Task
Session Recordings & Heatmaps
A/B tests and multivariate tests

Quantitative and qualitative analysis — Quantitative meets qualitative: figures, metrics and charts on the left - observation, interviews and context on the right. Two perspectives, one goal: understanding users better.

Strengths: Quantitative methods can summarize behavior across many observations and test hypotheses under appropriate conditions. Results still depend on measurement definitions, data quality, and study design.

Weaknesses: Numbers do not explain the “why”. The mere realization that 40% of users drop out at one step says nothing about the reasons. Without context, there is a risk of misinterpretation - especially in the case of complex user behavior.

Qualitative Methods - Depth Instead of Breadth

Qualitative methods focus on subjective experiences, expectations and attributions of meaning. They provide contextual, narrative and explanatory insights that are difficult to quantify.

Imagine this: Users often abandon a form after the second step. The heatmap shows that the mouse pointer remains on a mandatory field. An interview reveals: The field name is misleading - “customer number” is confused with “order number”. Such insights are accessible through qualitative methods.

Typical qualitative UX methods:

Guided interviews or narrative interviews
Field observations and shadowing
Thinking Aloud methods
Diary Studies and Cultural Probes
Video analysis and qualitative coding

Strengths: Qualitative methods provide rich descriptions and reveal mental models, emotional reactions and unarticulated needs. They are particularly valuable in early project phases or for explorative questions.

Weaknesses: Qualitative findings generally do not support statistical generalization to a population. They can provide analytically transferable insights when sampling, context, and analysis are transparent. Planning and analysis may require more resources than standardized data collection.

When Which Procedure?

The choice between a qualitative and quantitative approach depends on the respective objective. The following table provides an initial orientation:

Objective	Recommended access
Measure behavior (e.g. click paths)	Quantitative
Understanding use	Qualitative
Compare perceived usability	Quantitative (e.g. SUS)
Measure willingness to recommend	Quantitative (NPS; not a direct usability measure)
Exploring needs	Qualitative (interviews)
Evaluate redesign	Combination recommended

Practical Example

A UX team is planning a redesign for a customer portal. First, they conduct qualitative interviews with existing customers to understand needs and problems. They create interactive, functional variants that—if traffic and study design allow—are assessed in a randomized A/B test against a predefined metric. This combines exploratory findings and a quantitative experiment where appropriate to the question.

Mixed Methods - the Best of Both Worlds

Combining both perspectives can be useful when a question requires both breadth and context. Triangulation can reveal agreement or disagreement, but does not automatically compensate for methodological weaknesses.

Example

An app has a high abandonment rate during onboarding (quantitative). Interviews with test users (qualitative) show: The process is too text-heavy and creates uncertainty. Solution: Reduction of text, additional visual aids - and a new test with a measured success rate.

Conclusion

Quantitative and qualitative methods answer different questions. Not every project needs both. A sound UX process chooses methods to fit the research question, documents limitations, and combines perspectives when they usefully complement one another.

Frequently asked questions (FAQ)

What is the difference between quantitative and qualitative methods?

Quantitative methods provide measurable, statistically analyzable data - e.g. click numbers or time measurements. Qualitative methods capture subjective experiences, mental models and contextual information through interviews, observations or thinking aloud.

When are quantitative methods useful?

When it comes to scalability, comparability and hypothesis testing - e.g. for A/B tests, web analytics or standardized questionnaires such as SUS or UEQ.

When are qualitative methods useful?

For explorative questions, to survey needs and to analyze complex usage contexts. They provide deeper insights into motivation, emotions and mental models.

What are the advantages of quantitative methods?

They can describe frequencies and differences numerically and support statistical analysis. Strength and comparability depend on operationalization, data quality, and sampling; numbers alone do not explain why something happens.

What are the advantages of qualitative methods?

They can surface context and possible explanations. They are generally not designed for statistical representativeness; transferability depends on sampling, context, and analysis. Planning and analysis can be resource-intensive.

Should you combine both approaches?

When a research question requires both perspectives, mixed methods can combine breadth and context. Triangulation does not automatically make findings valid; conflicting results should also be reported and investigated.

Quantitative and mixed-methods approaches in UX research

This work examines the combination and targeted use of qualitative and quantitative methods for evaluating user experience - from frameworks and mapping approaches to practical guidelines for evaluation.

Bridging Quantitative and Qualitative Digital Experience Testing

Develops a platform idea that integrates quantitative conversion analysis (e.g. A/B tests) with qualitative UX feedback (e.g. think-aloud, surveys). Example of multimodal method triangulation.

Kumar, R. (2023). Bridging quantitative and qualitative digital experience testing. In ACM SIGIR. https://doi.org/10.1145/3539618.3591873

DOI

The Past, Present, and Future of UX Empirical Research

Metastudy analyzing 400 UX studies shows a strong increase in mixed-methods approaches since 2010

Robinson, J., Lanius, C., & Weber, R. (2018). The past, present, and future of UX empirical research. Communication Design Quarterly. https://doi.org/10.1145/3188173.3188175

DOI

Quantifying User Research

Practical guide to quantitative UX research including task times, error rates and statistical analysis.

Sauro, J., & Lewis, J. R. (2012). Quantifying user research. In Quantifying the User Experience. https://doi.org/10.1016/B978-0-12-384968-7.00002-3

DOI

Science corner: evidence data

Setting

Research design depends on the inquiry and decision question

Replication status

Mixed findings

Evidence level

High (E=4, T=4)

Key sources (APA)

Greene, J. C., Caracelli, V. J., & Graham, W. F. (1989). Toward a conceptual framework for mixed-method evaluation designs. Educational Evaluation and Policy Analysis, 11(3), 255–274. https://doi.org/10.3102/01623737011003255
Johnson, R. B., & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33(7), 14–26. https://doi.org/10.3102/0013189X033007014
Jick, T. D. (1979). Mixing qualitative and quantitative methods: Triangulation in action. Administrative Science Quarterly, 24(4), 602–611. https://doi.org/10.2307/2392366
Hornbæk, K. (2006). Current practice in measuring usability: Challenges to usability studies and research. International Journal of Human-Computer Studies, 64(2), 79–102. https://doi.org/10.1016/j.ijhcs.2005.06.002
Faulkner, L. (2003). Beyond the five-user assumption: Benefits of increased sample sizes in usability testing. Behavior Research Methods, Instruments, & Computers, 35(3), 379–383. https://doi.org/10.3758/BF03195514

Review date / version

2026-11-01 · 1.1

Next review

2027-11-01 (Review cycle: 12 months)

Last modified: 1 November 2026