In order to conduct good analysis and go through all the steps in the analysis spectrum, analysts are encouraged to use an analytical framework. Analytical frameworks are designed to structure an analyst’s thinking, and to help logical thinking in a systematic manner. In short, analytical frameworks are models that aim to guide and facilitate sense making and understanding. An analytical framework is often presented visually.
Frameworks within the humanitarian sector often need to be both needs and risk based, meaning they allow for a model to be build that looks at the current and future humanitarian developments. A good framework ensures the data is structured in a way for analysis to have tangible outcomes. Analytical outcomes for example can be used to answer questions such as “what are key priority needs?” and “what are current gaps in humanitarian response?”.
Defining a theoretical framework forces analysts to be selective. This means they will have to decide what variables are most important and informative, therefore reducing the amount of information that will be collected and analysed. Analysis conducted using frameworks is focused on the research questions, systematic, comprehensive and transparent and reduces the impact of selection and process biases. If multiple stakeholders are analysing data, a framework helps them study the same phenomenon using the same categorisation, reducing duplication of information (Chataigner 07/2017).
Analytical frameworks are of critical importance to:
- The data analysis plan which will have to reflect what categories are presented in an analytical framework, detailing indicators, sources, units of analysis, and data collection techniques.
- The structure of a database/means of data storing
- The type of analytical conclusions that need to be reached and agreed upon by multiple stakeholders in the event of joint analysis
- The structure of a final report
In summary, an analytical framework is used as it:
- Underpins, supports and guides the collection, collation, storage and analysis of data by identifying key analytical outputs and products at each step of the analysis
- Provides a way to organise what data to collect and how to analyse it
- Supports a common analysis of where deficiencies and gaps have the most severe humanitarian outcomes or present the greatest risks, and identify which geographical areas and population groups are a priority for intervention (current and forecasted priority needs)
- Serves as a communication tool and a driver for collaboration between humanitarian actors and is used as a reference throughout the process.
- Is used to identify what information will be useful for analysis and what can be discarded (Chataigner 07/2017).