Your research design is the architecture of your study — the plan that determines what kind of conclusions you'll be entitled to draw. Choose it after your research questions, never before: the design serves the question, not the other way round.
Designs by purpose
Exploratory
Used when little is known: open-ended, flexible, usually qualitative (interviews, focus groups, open literature work). Output is understanding and hypotheses — not measurements. Often the first phase of a larger design.
Descriptive
Answers what is happening: profiles a population, practice or phenomenon through surveys or observation. Strong for prevalence and patterns; makes no causal claims.
Correlational
Measures relationships between variables as they naturally occur — the backbone of most survey-based management and social-science theses (including SEM studies in SmartPLS or AMOS). Can establish association and prediction, not causation.
Experimental & quasi-experimental
Manipulates an independent variable with control (and randomisation, in true experiments) to test causation. Quasi-experiments drop randomisation when reality doesn't permit it, trading some internal validity for feasibility.
Designs by time horizon
- Cross-sectional — one snapshot; fast and common, but silent about change.
- Longitudinal — repeated measures of the same subjects; supports claims about change and temporal ordering, at the cost of time and attrition.
Case study and mixed designs
A case study examines one (or a few) bounded cases in depth using multiple evidence sources — powerful for how and why questions in context. Mixed-methods designs combine strands deliberately; see the decision logic in qualitative vs quantitative vs mixed methods.
A decision path
- 1Write your research questions first — sharp ones (use the research question generator).
- 2Ask what each question demands: exploration → exploratory/qualitative; description → descriptive; relationships → correlational; causation → (quasi-)experimental; process in context → case study.
- 3Check feasibility: access, sample, time, ethics. Feasibility legitimately eliminates designs.
- 4Justify the survivor against the alternatives in your methodology chapter — that comparison is what examiners reward.
State your question, design, data and analysis in one paragraph. If any element doesn't obviously require the next, the design isn't aligned yet — fix it before data collection, when it's still cheap.
Frequently asked
Can a correlational design ever support causal language?+
Not on its own. You can strengthen causal plausibility with theory, temporal ordering (longitudinal data) and controls, but honest correlational studies claim association and prediction, and state the causal limitation openly.
Which research design is best for a PhD?+
None universally — examiners assess fit, not fashion. The best design is the one your questions require and your resources can execute rigorously, justified explicitly against alternatives.
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