Stage 8

DATA ANALYSIS AND INTERPRETATION

During this stage the data is ‘cleaned’ (so that only material deemed trustworthy is included) and analysis will typically be used to distil trends. Emerging themes and implications will emerge from the data. No data analysis occurs with pure objectivity. Ethical challenges arise in relation to every ‘choice point’ including choosing analytic methods, level of granularity of analysis and analytic assumptions as well as how findings are interpreted. Data analysis and interpretation is a time when conscious and unconscious biases may influence what we ‘see’ in the data, what we say about the data and whose voice is heard. In turn, these demand characteristics can influence the evidence-base and the translation of this evidence into policy and practice.

Being alert to ethical dilemmas

Who is leading data analysis? What measures are we adopting to control biases?

Supporting considerations:

• Could / how will these data affect the daily life and future of many people in the community?
• When and how should we use data collected in this context? For example, what if there is a political conflict or political “interference” in interpreting/communicating results?
• How should we handle confidentiality and anonymity in small communities (where it is easy to recognize participants) and large and varied research groups (which usually change over time)?
• How do we make a culturally sensitive interpretation of data considering language barriers and different worldviews?
• How am I making sure that we’re not treating knowledge as something to be extracted from one place, exported and stock-piled for the benefit of another?

Working towards solutions

Place

People

Principles

Precedent

Inform yourself - data that may be harmless in your home country can be highly sensitive in the place where you are doing the research. Find out about the context in which this data will have an impact – what will the legacy of your analysis and interpretation be?

Think about immediate, short term and long-term impact when considering how to make meaning of your data

Think beyond reporting the data to the scientific community - reflect on how this data can affect the lives of participants, their communities and your local partners.

Think about who benefits or who may be harmed by this data. Obviously, you must analyse, interpret and report the data faithfully, but at the same time you should prioritise doing it carefully so as not to cause harm along the way.

Reflect on who can help you interpret data in a culturally respectful way. Discuss with your team and ask for support from local partners.

Be both honest and respectful. You should strive to achieve at the same time a reliable analysis of the data (controlling biases) that is presented in a respectful way (that allows the message to be understood and that benefits as many people as possible)

Find out about the main analytic strategies for this data (and if there are people in your team who can do it) and what are the main obstacles to analysing data in this context. A good practice in Global challenges research is to read papers from this context and study the challenges and solutions proposed.

Remember that your choices and your analysis and interpretation will become part of the precedent available to future researchers.