Your design decides what your study can claim. We help you choose between quantitative, qualitative, mixed methods and practice-based designs, and size your sample so the study can answer its question.
Start from the kind of question you are asking. Questions about how much, how many, or whether one thing affects another usually call for a quantitative design, from surveys to experiments. Questions about experience, meaning or process call for a qualitative approach such as phenomenology, grounded theory, ethnography, case study or narrative inquiry. When you need both breadth and explanation, a mixed methods design integrates the two. When the aim is to improve a practice or a designed intervention while studying it, action research or design-based research fits.
Whichever design you choose, sampling and sample size need a justification: a power analysis for hypothesis testing, a precision calculation for surveys, or a reasoned argument based on information power for qualitative studies. Committees and funders increasingly reject rules of thumb, so the calculation is worth getting right early.
Design support for quantitative studies: matching your question to an experimental, quasi-experimental, correlational or survey design, defining variables, controlling validity threats and aligning sample and analysis.
Design support for qualitative studies: choosing an approach that fits your question and philosophical stance, then aligning sampling, data collection, analysis and quality criteria so the study holds together.
Design support for mixed methods studies: choosing a core design, planning how and where quantitative and qualitative strands integrate, and presenting the integration with joint displays and meta-inferences.
Planning support for practice-based research: action research and participatory action research cycles, and design-based research or design science iterations, with data collection, reflection and rigor built into each cycle.
A defensible sample-size justification and sampling plan: power analysis in G*Power or R, simulation for complex models, precision-based calculations for surveys and reasoned sample sizes for qualitative work.
Tell us what you’re working on and where you’re stuck. We’ll recommend the right kind of help and send a quote.