Use standard methodologies to lead assessment or train partners
The WASH coordination platform can be responsible to lead a WASH assessment at national or subnational level. WASH coordination platform staff, and in particular assessment specialist, and must be aware of standard methodologies to design assessment, collect data and analysis it. They are also frequently delivering training on assessment for partners.
A WASH assessment will follow the below steps:
Set the objectives and scope
Define in a TOR (see xxx in the Key Guidance and Tools) the research questions, the geographical areas and population groups to be covered. Detail your information needs, focusing generally on affected people needs, vulnerabilities, and their preferred modality to receive aid (for instance, cash vs in-kind), but also aspects related to access, operational feasibility, etc. Set the timeframe of the assessment and the information products that will be produced.
Develop indicators and questions
The starting point of the assessment tool is the WASH core indicators. In addition to that, any additional indicators that might be needed to inform the research questions can be included. The GWC has developed an indicator and question bank (see in the key guidance and tools section) that you can use to develop your own list.
Develop the analysis plan
For each indicator, explain how it will be aggregated/disaggregated, and what type of analysis you want to run. This process is usually done by building a matrix called Analysis plan. This matrix will help making sure that all data required for the analysis are collected, that no time is wasted in collecting unnecessary data, and will guide the analysis process. See example/template of analysis plan in xxx
Design the methodology
To design the methodology, consideration should be given to both objectives of the assessment, and constraints such as time, resources, access, etc.
The main dimensions that needs to be addressed are:
- Unit of measurement, which is the level the data is collected at (e.g. individual, household, institution/infrastructure, community, area). This will have an influence on the type of data collected: the higher the level, the less reliable is the answer of the interviewee.
- Data-collection methods (e.g. direct observation, key informant interviews, focus group discussions, community discussions, key-informant interviews, household interviews, etc.): in the key guidance and tools section can be found a table detailing pros and cons of the different data collection methods.
- Sampling methods, or in other words the criteria you will use to select the respondents. There are two main types of sampling: probability sampling – in which respondents are selected randomly and every person in the sampling frame has the same chance of being selected, and non-probability sampling – in which respondents are not selected randomly. Probability samplings are much more resource-intensive but can generate statistically significant findings, while non-probability samplings are often lighter in terms of resources but generate findings that are indicative only. For this reason, there is always a tradeoff between representativeness of findings and cost/time constraints.
Designing the data collection tools
Once you have set the methodology, formulate the questions that will be asked to the respondents to gather the data needed for each of the indicators of the analysis plan. Remember that questions should be as brief as possible, simple-worded, and not leading towards pre-defined answers. In the GWC indicator and question bankyou will find some examples you can use to develop your questions.
Data is more and more collected through mobile data collection systems, such as Kobo and ODK: this allows easier data management. In the key guidance and tools section you will find guidance on how to set up and use mobile data collection systems.
Follow up data collection
Ensuring close follow up during the collection phase will improve the quality and timeliness of data. It is key that progress and challenges of data collection is regularly monitored. To achieve this, a matrix can be set up to track the number of forms that have been submitted, the areas that have been completed and the issues hampering progress. You need to check and clean data as soon as they come through to spot inconsistencies and follow up with the enumerators.
In the key guidance and tools section you will find templates of progress tracking matrices and cleaning logs.
Data analysis
Once data has been collated, analysis can start. This process should be guided by the analysis plan, as the indicators chosen must help answering the research questions of the assessment.
Analysis should aim not only at describing the situation (for instance, where and who lacks safe water), but also at explaining the causes (for instance, lack of improved water points), interpreting the effects (for instance, linking presence of AWD with lack of safe water) and anticipating possible evolutions (for instance, the potential increase of child mortality rate in certain areas). Another key aspect is the implementation of cross-sectorial analysis based of WASH data or data from other relevant sectors, such as nutrition, health, education, etc.
In the key guidance and resource section can be find documents that describe possible approaches towards these different levels of analysis.
Share information
Findings should be disseminated in a timely and effective way. Different types of information products can be considered, including factsheets, maps, web-platforms, reports, etc. depending on the audience and the resources available. In the key guidance and tools section can be found templates as well as example of information products from past assessments.
Information products should be shared both with the primary audience through the coordination platforms channels (coordination meetings, MailChimp, SendinBlue, social media, etc.), and the broader humanitarian community, thought platforms such as HumanitarianResponse.info, ReliefWeb, the GWC site, etc. It is important to share the anonymized, cleaned dataset on the Humanitarian Data Exchange (HDX, see useful links) – the main humanitarian online data sharing platform, so that other people can have access to data and run their own analysis.