Research
Insights from data
What is research design?
Research design is concerned with obtaining information to generate key insights and answer questions. The requirements below are used by research + DESIGN to develop a framework that can obtain appropriate and credible data.
- Real-life problem:
The real-life problem is not necessarily addressable and is rather a meta-level background to the research problem. - Research problem:
The research problem is the primary reason behind the study and, more specifically, what is required to be better understood. - Methodology:
The research can be carried out to prove an idea, or if unknown, to explore a topic in order to develop a range of ideas. - Research method:
Attitudinal (Qualitative) and/or statistical (Quantitative) data is used to address questions ranging from interpretative to functional. - Unit of analysis:
The phenomenon being studied is reduced to a unit-of-analysis that can be formally measured and analysed. - Research sample:
The research sample is representative of the individuals, communities, or users being addressed in the later design phases. - Timescales:
A long-term (Longitudinal) or short-term (Cross-sectional) timeframe is used to correspond with sample behaviour. - Instruments:
Data is gathered using existing and/or new instruments such as surveys, questionnaires, check-lists, Likert-scales, and photographs. - Gate-keepers:
Access to communities and groups is often mediated through a gate-keeper who can make appropriate introductions. - Key-informants:
A sample may comprise entirely of, or contain a selection of, key-informants who are individuals with specialist information. - Fieldworker:
Consideration of sample demographics and fieldworker capabilities can help obtain more in-depth or cleaner research data. - Piloting:
A pilot phase tests the research design and may motivate for refinements before formally implementing the fieldwork. - Data collation:
Depending on the research method, data can be collated in real-time to ascertain and rectify sample selection bias. - Data preparation:
Prior to analysis, fieldwork data is reviewed for errors, cleaned without affecting integrity, and collated into datasets. - Data analysis:
Data is analysed with Qualitative Data Analysis (QDA) or descriptive statistics software to identify trends, anomalies, and insights. - Reporting method:
Reports outline key findings, recommendations for subsequent design phases, and include datasets as appendices.