C4D: Describe

Describe is one of the seven clusters of R,M&E tasks in the Rainbow Framework.

The describe cluster of evaluation tasks involves collecting or retrieving data and analyzing it to answer R,M&E questions about situations and what has happened (the activities, outcomes and impacts) and other important contextual information.

There are seven tasks associated with describe. Each task includes C4D specific methods, advice and resources for generating data that describes situations and changes

In this section

  • In some cases, it might be possible to gather data on an entire population (for example, some data might be available from every participant, or about every project), but in most cases, it will be necessary to take a sample of projects, sites, events, or people. Deciding on sampling strategies is an important part of an R,M&E design.
  • Measures, indicators or metrics are used to succinctly describe the context, implementation and/or results of an intervention (project, program, policy) such as inputs, processes or activities, outputs, outcomes and impacts. The terms are often used in different ways in different organisations, so it is important to check their meaning in a specific setting or context.
  • Data collection methods should be selected on the basis of how well they will answer the key questions, with due consideration of available resources. Decisions about methods need to be made in conjunction with other decisions about the key questions (what to collect data on), whether indicators might be used, how sampling will be used, and how data will managed and analysed.
  • Good data management means that systems are in place for consistent and ethical (see Define ethical and quality evaluation standards) collection, recording, storage, security, backing up, cleaning, and modifying, and ownership of data. This is part of data quality assurance (DQA).
  • M&E Frameworks and evaluation/study designs that include the collection both qualitative and quantitative data, are an important strategy for strong and balanced findings. It is important to plan in advance how the different types of data will be combined.
  • Analysing descriptive data (data about what has happened or is happening) means looking for patterns, themes and making sense of and summarising the data. It is an important part of every RM&E system or study. Techniques for analysis should be selected alongside the selection of methods in the design of a research study or evaluation.
  • Data visualisation is the process of representing data graphically. It can make it easier to see trends and patterns. Data visualisation can be used during data analysis as part of making sense of data. It can also be used to communicate results as part of producing the reports.

'C4D: Describe' is referenced in:

  • Framework/Guide