Triangulation, Mixed Methods, and the Illusion of Completeness: Why More Data Does Not Necessarily Mean More Understanding

When Combining Quantitative and Qualitative Research Still Leaves Important Things Out

For many researchers, mixed methods research appears to offer an attractive compromise. Quantitative approaches provide measurement, comparison, and statistical rigor. Qualitative approaches add context, interpretation, and lived experience. Together, they seem to promise a more complete picture of reality.

The appeal is understandable. If numbers alone are too narrow, add interviews. If interviews are too subjective, add surveys. If one method has blind spots, perhaps another can compensate.

But there is a deeper question that is rarely discussed:

What if combining methods does not overcome the fundamental limits of what can be known through those methods?

This article explores a possibility that receives surprisingly little attention: mixed methods may reduce some methodological limitations while leaving deeper epistemological and ontological limitations untouched. In some cases, the combination of methods may even create an illusion of completeness—a sense that reality has been adequately captured simply because multiple techniques were used.

The problem is not that mixed methods are flawed. The problem is that they may be asked to solve questions that no combination of empirical methods can fully resolve.

The Promise of Mixed Methods

Mixed methods research emerged partly as a response to long-standing debates between quantitative and qualitative traditions.

Quantitative researchers often emphasize:

  • Measurement
  • Replicability
  • Statistical inference
  • Generalization

Qualitative researchers often emphasize:

  • Meaning
  • Context
  • Interpretation
  • Human experience

The mixed methods movement argues that combining these strengths can provide richer and more reliable knowledge.

At first glance, this seems entirely reasonable.

Suppose an educational researcher wants to investigate student engagement.

A quantitative component might measure:

  • Attendance rates
  • Test scores
  • Survey responses

A qualitative component might explore:

  • Student narratives
  • Classroom experiences
  • Teacher observations

Compared with using only one approach, the combined design undoubtedly captures more dimensions of the phenomenon.

Yet an important question remains.

Does capturing more dimensions necessarily mean capturing the phenomenon itself?

The Difference Between More Data and More Understanding

Researchers often assume that increasing the amount or diversity of data automatically increases understanding.

Sometimes it does.

But not always.

Imagine observing a city from different angles.

One camera records traffic patterns.

Another records economic activity.

A third records population density.

A fourth collects interviews from residents.

The resulting picture is richer than any single source alone.

Yet the city itself remains more than the collection of observations.

The same principle applies to research.

Multiple methods often increase informational coverage. What they do not automatically increase is access to the full nature of the phenomenon being studied.

This distinction is crucial.

A broader dataset is not identical to a broader reality.

Ontology and Epistemology in Plain Language

The discussion becomes easier once two philosophical terms are clarified.

Ontology

Ontology concerns what exists.

What is the nature of reality?

What kinds of things are real?

What aspects of a phenomenon exist independently of our attempts to observe them?

Epistemology

Epistemology concerns how we know.

How is knowledge produced?

What can be observed, measured, interpreted, or inferred?

What are the limits of our methods of knowing?

The distinction matters because a study can expand its epistemology without expanding its ontology.

In other words:

Researchers can collect more information while remaining confined to the same assumptions about what counts as observable, measurable, or legitimate evidence.

Mixed methods often broaden epistemological access.

But they do not necessarily broaden ontological horizons.

The Problem of Epistemological Convergence

A common assumption is that combining methods increases diversity.

In one sense, it does.

But many mixed methods designs remain surprisingly similar at a deeper level.

Both quantitative and qualitative approaches often operate within the same basic empirical framework.

Both typically depend on:

  • Observation
  • Recording
  • Categorization
  • Interpretation of evidence

The techniques differ, but the underlying epistemological assumptions frequently remain aligned.

This creates what might be called epistemological convergence.

The methods appear diverse on the surface while remaining rooted in the same fundamental way of knowing.

Picture: Raimond Klavins on Unsplash

As a result, multiple methods may converge on the same blind spots.

The appearance of methodological plurality can conceal a deeper uniformity.

The Educational Sciences Example

A useful example comes from educational research.

Education concerns extraordinarily complex phenomena:

  • Learning
  • Understanding
  • Meaning-making
  • Motivation
  • Development
  • Identity formation

Yet many contemporary educational studies increasingly rely on measurable indicators.

Researchers examine:

  • Test performance
  • Behavioral outcomes
  • Survey responses
  • Attendance statistics

Even qualitative studies often remain tied to observable reports and articulated experiences.

These approaches generate valuable knowledge.

The problem is not that they are wrong.

The problem is that the phenomenon itself may be larger than what these approaches can access.

Human learning involves internal processes that are often:

  • Implicit
  • Unconscious
  • Symbolic
  • Developmental
  • Context-dependent

Many of these dimensions resist straightforward measurement.

Others resist verbalization altogether.

A student may be profoundly transformed by an educational experience without being able to articulate exactly what changed.

A statistical model cannot fully capture that transformation.

Neither can an interview.

Using both methods together still does not guarantee access to the phenomenon in its entirety.

The Historical "Bonnet" Example

An even clearer example comes from cultural and historical research.

Imagine a researcher asking:

How many women wore bonnets in a particular region during the nineteenth century?

With sufficient archival material, census data, visual records, and historical documents, it may be possible to produce an estimate.

The result could even be statistically sophisticated.

A qualitative component might add personal accounts describing why women wore bonnets.

At this point, mixed methods appears to have succeeded.

But consider a deeper question:

What did the bonnet mean?

Not merely what percentage of women wore one.

Not merely what they reported about it.

But what symbolic role did it play within the culture?

What unconscious meanings surrounded it?

What social, religious, aesthetic, or identity-related functions did it embody?

These questions point toward dimensions that are not easily reducible to either quantitative or qualitative data collection.

The issue is not that symbolism cannot be studied.

Rather, symbolism often requires forms of interpretation that exceed standard empirical frameworks.

Consequently, even an exemplary mixed methods design may leave crucial aspects of the phenomenon unexplored.

The Illusion of Completeness

The greatest danger may not be methodological failure.

The greatest danger may be methodological success.

When researchers use multiple methods, gather large datasets, conduct interviews, and triangulate findings, the resulting study can feel comprehensive.

This feeling is understandable.

Consider the implicit reasoning:

  • We used quantitative methods.
  • We used qualitative methods.
  • We triangulated results.
  • Therefore, we probably captured reality adequately.

Yet the conclusion does not necessarily follow.

What has been demonstrated is methodological breadth.

What has not been demonstrated is ontological completeness.

This distinction is easily overlooked.

The more sophisticated the research design becomes, the easier it may be to assume that remaining blind spots have disappeared.

In reality, some blind spots may simply become harder to see.

Why This Matters Beyond Research

The implications extend beyond academic methodology.

Students are often taught research techniques without extensive reflection on their philosophical limits.

Researchers are trained to evaluate methods, but not always to question the broader assumptions underlying those methods.

Public audiences encounter increasingly sophisticated visualizations, dashboards, and statistical graphics.

The result is a culture in which data often acquires an aura of authority.

Numbers appear objective.

Large datasets appear comprehensive.

Multiple methods appear exhaustive.

Yet none of these assumptions is automatically justified.

A society that becomes highly skilled at producing data can still remain surprisingly unreflective about what its data cannot capture.

Toward Epistemological Humility

The solution is not to abandon mixed methods.

Nor is it to reject empirical research.

Mixed methods often provides better evidence than single-method approaches.

The problem arises only when methodological expansion is mistaken for epistemological or ontological completion.

What may be needed is a stronger culture of epistemological humility.

Such humility recognizes that:

  • More data is not identical to more reality.
  • Multiple methods do not eliminate all blind spots.
  • Observation and measurement remain bounded activities.
  • Some dimensions of human experience resist straightforward capture.
  • Research can become more comprehensive without becoming complete.

This perspective does not weaken science.

It strengthens it.

Science advances not only through improved methods but also through reflection on the limits of those methods.

Conclusion

Mixed methods research is often presented as a bridge between competing traditions. In many cases, it successfully integrates strengths that would otherwise remain isolated.

Yet integration should not be confused with completeness.

Combining quantitative and qualitative approaches may broaden our perspective while still leaving deeper dimensions of reality beyond reach. Methodological diversity does not automatically produce ontological breadth, and richer datasets do not necessarily yield richer understandings.

The central challenge, therefore, is not whether mixed methods works.

It clearly does.

The deeper challenge is whether researchers remain aware of what even the most sophisticated methodological combinations cannot fully capture.

The future of research may depend less on finding ever more methods and more on cultivating the intellectual humility to recognize where every method—whether quantitative, qualitative, or mixed—ultimately reaches its limits.

References 

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Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). Sage.

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Hampson, T., & McKinley, J. (2023). Problems posing as solutions: Criticising pragmatism as a paradigm for mixed research. Research in Education, 116(1), 3–18. Sage Journals

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Ruggles, S., & Magnuson, D. L. (2020). The history of quantification in history: The JIH as a case study. Journal of Interdisciplinary History, 50(3), 363–382. PMC

Sale, J. E. M., Lohfeld, L. H., & Brazil, K. (2002). Revisiting the quantitative-qualitative debate: Implications for mixed-methods research. Quality and Quantity, 36(1), 43–53.

Inspired by HBS Puar 
Authored by Rebekka Brandt 
LinkedIn 

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