Name the boundaries your committee will ask about.
A working guide for doctoral researchers on assumptions, limitations and delimitations. Six modules break each concept into the sub-principles you actually have to defend in a methods chapter or dissertation proposal — with side-by-side study examples and short coached practice.
Three concepts, six modules
Assumptions are the beliefs you hold before you begin — about what exists, how knowledge is produced, and how phenomena can be studied. Limitations are constraints you did not fully control that affect interpretation — design weaknesses, sample issues, or measurement bounds. Delimitations are choices you made to narrow the study — the boundaries you set intentionally. Each module shows how to identify, justify and write up one piece of that triad.
| Module | Answers the question | Quantitative looks like… | Qualitative looks like… |
|---|---|---|---|
| Theoretical Assumptions | What do I believe about reality and knowledge? | Positivist/post-positivist stance; variables are measurable; causality is discoverable | Interpretivist/constructivist stance; meaning is co-constructed; researcher is instrument |
| Methodological Assumptions | What do my methods assume about data and inference? | Scales produce interval data; samples support generalization; statistics estimate population parameters | Interviews access lived experience; themes emerge inductively; trustworthiness criteria fit naturalistic inquiry |
| Internal Validity Limitations | What threatens my causal claim? | Selection, history, maturation, testing, instrumentation, attrition, confounding | Alternative explanations in case studies; researcher bias; incomplete access to context |
| External Validity Limitations | How far can findings travel? | Convenience sample, single site, WEIRD population, one operationalization | Thick description bounded by context; analytic rather than statistical generalization |
| Scope Delimitations | What did I choose to include or exclude? | Variables selected; time period; geographic region; theoretical lens | Phenomenon bounded; participant roles; setting; conceptual focus |
| Boundary Delimitations | Who is in and who is out, by design? | Inclusion/exclusion criteria; sampling frame; eligibility rules | Purposive sampling boundaries; case selection criteria; saturation logic |
Interactive practice
Diagnose the boundary issue
Seven short study vignettes across quant, qual, and mixed methods. Identify whether the issue is an assumption, limitation or delimitation, then pick the best way to write it up — with feedback grounded in research methods literature.
Assumptions · Sub-principle 1
Theoretical Assumptions
What do you believe about reality, knowledge and causality before you collect a single data point? Ontological, epistemological and axiological assumptions shape every design choice and must be named explicitly.
Open moduleAssumptions · Sub-principle 2
Methodological Assumptions
Your assumptions about measurement, generalization, and the researcher-participant relationship determine whether your methods fit your question. Make them visible so readers can judge alignment.
Open moduleLimitations · Sub-principle 1
Internal Validity / Design Limitations
When you claim X caused Y, what alternative explanations remain live? Selection, history, maturation, testing, instrumentation and attrition are design-level constraints you name honestly.
Open moduleLimitations · Sub-principle 2
External Validity / Generalizability Limitations
From which sample to which population, in which settings, with which operationalizations? Sample, setting and measurement boundaries constrain how far findings can travel.
Open moduleDelimitations
Scope Delimitations
What is intentionally included or excluded from the study by researcher choice? Scope delimitations bound the topic, concepts, variables and time frame and explain why those boundaries were necessary.
Open moduleDelimitations
Boundary / Population Delimitations
Who and what is inside the study, and who is left out by design? Clear inclusion and exclusion criteria, geographic boundaries and population bounds turn a vague scope into an auditable decision.
Open moduleApp version v2.0.0