Ontology explained: the nature of reality and what is real in social work research

How is ontology BEST defined?

• A. The study of knowledge production
• B. The examination of social issues
• C. The nature of reality and what is real
• D. The analysis of numerical data

Answer: (C)

Ontology is best defined as the nature of reality and what is real. In the context of philosophy and social science, ontology involves exploring the fundamental categories of being and existence. It addresses questions about what entities exist in the world and how they can be categorized or understood. This examination helps inform research methodologies and epistemologies within social work practice, as understanding the nature of reality influences how researchers approach their subject matter, define constructs, and interpret data. While other options address important aspects of research and inquiry, they represent different areas of focus. The study of knowledge production relates more closely to epistemology than ontology, the examination of social issues pertains to social theory or applied research, and the analysis of numerical data refers specifically to quantitative research methods. These areas are interconnected but do not define ontology directly, which is fundamentally concerned with the essence and existence of reality itself.

Ontology might sound like a heavyweight word from a philosophy seminar, but it’s actually a surprisingly down-to-earth idea. Think of it as a lens that helps us ask: what counts as real in the world we study? What exists, and how do we know it exists? That’s the essence of ontology. In social work research, your stance on reality isn’t just a background detail; it shapes the questions you ask, the data you collect, and how you interpret what you find. Here’s a friendly, practical look at what ontology means and why it matters.

What is ontology, really?

Let me explain with a simple compass analogy. Ontology is about the nature of reality and what is real. It asks questions like: Do social categories exist independently of people’s beliefs (like poverty as a fixed, objective state), or are they constructs that only take on meaning when we label and talk about them (like poverty as a set of lived experiences shaped by culture and policy)? The answers aren’t just philosophical fluff; they guide how you approach research—from what you measure to how you understand people’s stories.

In other words, ontology is about existence and meaning. It’s not just about “facts.” It’s about which facts count, what kinds of things you can study, and what counts as a genuine explanation. For researchers in social spheres, that distinction is surprisingly practical: it nudges you to decide what counts as a real thing to study and how real that thing is in different settings.

Two common stances—and why they feel different

Most people don’t wear their ontology on their sleeve, but you can feel the difference in your choices of methods and questions. Here are two well-known ways many researchers think about reality.

• Realist or objectivist stance: This view treats some things as real and discoverable, almost lying “out there” in the world. If you adopt this angle, you tend to treat constructs like poverty, health status, or risk as things with measurable, stable properties. Your data might lean toward numbers, because you’re looking for patterns you can count and compare. It’s the classic path for quantitative approaches.

• Constructivist or interpretive stance: This outlook sees reality as something constructed through people’s experiences, cultures, and language. Here, categories aren’t fixed; they’re shaped by social context, power, and history. You’ll hear more emphasis on people’s voices, meanings, and circumstances. The data might be richer in nuance, leaning toward interviews, observations, and narrative analysis.

Here’s the thing: neither stance is “wrong.” They simply start from different premises about what counts as real. And that starting point matters because it nudges you toward different questions, different data sources, and different kinds of explanations.

Ontology and epistemology: a practical duo

If ontology is about what exists, epistemology is about how we know it. Epistemology asks: what counts as knowledge, and how can we justify it? In many studies, ontology and epistemology travel together like gloves and a hand. Your beliefs about reality influence what you consider credible evidence and how you validate findings.

For example, if you start from a realist ontology, you might favor objective measurements and statistical analysis. If you start from a constructivist ontology, you might emphasize participants’ stories and the contexts that shape those stories, using methods that honor those voices. Understanding both helps you design studies that feel coherent—your questions, your data, and your interpretations all line up.

Real-world flavors of ontology

Let’s ground this with a couple of social-world examples. They’ll feel familiar, even if you haven’t labeled them as “ontological” positions before.

• Studying homelessness: A realist stance might treat homelessness as a measurable condition with variables like shelter duration, income, and health indicators. You’d try to quantify risk factors and outcomes across a population. A constructivist stance would focus on how people experience homelessness, the meanings they attach to shelter, family separations, or stigma. Data could come from life histories, in-depth interviews, and community narratives. Both approaches can yield valuable insights; the key is to be clear about what you’re treating as real and why.

• Understanding family support networks: A realist approach might map formal supports—caseworker visits, service availability, funding levels. A constructivist approach might explore how families interpret support, what counts as “help,” and how cultural norms shape relationships to assistance. Again, the methods would reflect those starting assumptions.

Why ontology matters in your work

Ontology isn’t just a philosophical curiosity tucked away in a dusty library. It has practical consequences:

• What you choose to measure: If you start from a certain belief about what exists, you’ll select indicators that align with that belief. A view of social problems as stable conditions pushes you toward standardized metrics. A view of problems as lived, evolving experiences nudges you toward qualitative, flexible data.

• How you interpret data: Two researchers might look at the same interview and come away with different interpretations because they’re anchoring their readings to different ontological assumptions. Being explicit about your stance helps others understand where you’re coming from—and why your conclusions look the way they do.

• How you describe constructs: Are terms like “risk,” “well-being,” or “social inclusion” real things you can measure, or are they social meanings that change with culture and policy? Your ontology helps decide how precise or flexible your definitions should be.

• How you build knowledge with communities: If you view reality as something co-created with participants, you’ll likely collaborate more closely with communities, inviting their perspectives into the design, data collection, and interpretation. That stance can strengthen trust and relevance.

Connecting the dots in everyday research design

So, how do you bring ontology into actual study design without turning it into a vocabulary exercise? Here are practical steps.

1. Name your stance early

Before you draft your research plan, say aloud (or write) what you think exists in your topic space and why. Is poverty a fixed condition you measure, or a social experience you understand? This clarity guides choices without being heavy-handed.

2. Align methods with your stance

• If you lean realist: you’ll likely mix surveys, standardized instruments, and perhaps experimental or quasi-experimental elements to test hypotheses about relationships and outcomes.

• If you lean constructivist: you’ll emphasize interviews, focus groups, ethnography, and participatory methods that reveal meanings and contexts. You’ll expect variability across settings and avoid forcing neat numbers where they don’t fit.

3. Be explicit about constructs

Define your terms with attention to ontology. For example, when you say “risk,” specify whether you mean a measurable probability, a lived sense of vulnerability, or both. This helps readers (and peers) understand what’s real in your study and why.

4. Reflect on data interpretation

When you report findings, connect them back to your ontological stance. If you found a common pattern in interviews, explain how that pattern reflects shared meanings you observed rather than claiming a universal law.

5. Value triangulation, thoughtfully

Combining methods can be powerful, but keep your triangles consistent with your stance. Mixed-methods work best when the integration makes sense given your underlying beliefs about reality.

A few gentle caveats

Ontology is a tricky compass because there isn’t a one-size-fits-all map. People drift between positions, especially when asked to explain complex social realities. Also, it’s easy to conflate ontology with epistemology or to treat “truth” as a single thing. Reality in social life is messy, layered, and often contested. That’s not a flaw; it’s the natural texture of human existence.

If you want a quick mental model, think of ontology as the ground you stand on when you study. It defines the horizon you’re trying to reach and the steps you take to get there. Your epistemology is the route you choose to travel and how you verify you’ve crossed the right terrain. Together, they shape your entire research journey.

Common misunderstandings to avoid

• Believing ontology is only about big metaphysical claims. It’s also about everyday decisions: what you decide exists, how you talk about it, and what you measure.

• Thinking there’s only one “correct” stance. Different topics and communities may call for different angles, and that’s perfectly legitimate.

• Treating constructs as if they are inherently stable. In many social contexts, meanings shift with time, policy, and culture.

A short glossary to keep handy

• Ontology: The nature of reality and what is real.

• Realism/objectivism: The belief that some things exist independently of observers.

• Constructivism/interpretivism: The belief that reality is shaped by social meaning and context.

• Epistemology: How we know what we know; what counts as evidence.

Bringing it back to the bigger picture

Ontology isn’t a dry checkbox in a syllabus. It’s a compass that helps researchers listen more carefully to the people and communities they study. It guides not just what we count, but what we value as meaningful explanations. When you approach a social question—whether it’s about housing, health, or support networks—your stance on reality subtly tunes the whole inquiry.

And yes, that means your choice about reality will influence the language you use. You might describe findings in terms of measured prevalence, or you might tell a story about lived experience. The key is honesty about the stance you took and clarity about how that stance shaped your questions, your methods, and your conclusions.

A final nudge for clarity and curiosity

Ontology invites curiosity without demanding certainty. It’s the sense that, in social life, there are many ways to see what’s real, and each way can illuminate a different corner of the truth. If you stay curious about those corners and transparent about your starting point, your research—whatever the topic—will feel more human, more connected, and more useful to the people and communities you aim to serve.

If you’re ever unsure, ask yourself a few quick checks: What counts as real in this study? How do I know it’s real? Do my methods reflect that belief? If the answers line up, you’re likely on a thoughtful path. And that, more than anything, is what makes research in social realms both rigorous and resonant.