ANTECEDENTES 1 Los bosques templados

Step 4: Agree on field monitoring data collection and management process

Objective of step: Determine the most relevant data to be collected, its source, data collection

method, timing and frequency of collection, the people responsible, and the intended audience and use of the data, and how data will be systematically and reliably stored, managed and accessed

Timing: During project implementation, before each round of monitoring Activities:

4.1 Agree on relevant data collection methods/tools 4.2 Determine beneficiary counting

4.3 Agree on sampling requirements

4.4 Interview guide and questionnaire creation

4.5 Recruitment and training of field monitors/data collectors 4.6 Undertake on-site monitoring

4.7 Triangulate data collection sources and methods 4.8 Define data entry and management process

2.4.1 Agree on relevant data collection methods/tools

A list of Data Collection Methods, Types and Sources and when they can be used is summarised in Annex 4. It is important to agree on what data collection methods and tools will be used well in advance of the data collection itself, to ensure the appropriate resources, including people with the appropriate skills are available for data collection. Data collection can be expensive, therefore

planning for it is critical. When data collection methods have been agreed on, these should be

summarized in the M&E Plan.

In general the following checklist for data collection preparation should be considered:

Box 2.13: Checklist for data collection preparation Check

box Step Details

        1. Confirm intended use of data to be collected 2. Agree data collection tools 3. Check sample size 4. Prepare data collection guide 5. Recruit data collectors 6. Train data collectors 7. Communicate with population 8. Consider other options to support monitoring, e.g. photography, GIS, self- evaluation tools, etc.

• Check that the information being collected is necessary/ sufficient. Collect only what is necessary for project management – e.g. reviewing project objectives, indicators, and assumptions.

• Agree on methods, collection time, human resources required and skill sets of data collectors. This can help raise and address any issues with data collection well in advance of collection commencing.

• Check that the sample size that is necessary to assess change, is adequate and manageable.

• This ensures a consistent and standardised approach to data collection amongst those involved.

• Consider if local people (university students, community workers etc.) can be used for data collection.

• Training should focus on data collection purpose, techniques, tools, ethics, culturally appropriate communications and practical tips.

• Notify beneficiary population, communicating policies on confidentiality and participation and addressing any concerns. Ensure any required permission is obtained from local/national authorities and data collection is in line with any stipulated regulations and local customs.

• Consider local skills and capacities, consider permission for photography, and how it can be used for documentation and monitoring.

Appropriate data collection, for monitoring and evaluation, will include quantitative and qualitative data. Likely different methods will be used:

• Household interviews • Semi- structured interviews • Focus groups discussions • Observations

Different tools can be employed to support and facilitated the above methods: • Proportional Piling

• Ranking • Zoning • Mapping • Etc.

Guidance to all of these can be found in the respective annexes and in the ACF FSL Assessment Guidelines (2009).

2.4.2 Determine beneficiary counting

A key aspect of data management will be beneficiary counting. Each project should keep a clear and confidential database with a list of direct beneficiaries. To ease data collection on this, it is important to define who is and who is not a direct beneficiary by project/activity type, such that a standard way of counting beneficiaries is agreed on. Examples and further explanation are given in Annex 33.

2.4.3 Agree on sampling requirements

Sampling is a critical aspect of planning the collection of primary quantitative data. Most projects do not have sufficient resources to measure what is occurring across a whole population (e.g. a census), nor is it usually necessary. Sampling is used to save time and money by collecting data from a subgroup of the population to make generalizations about the larger population, within a specified margin of error with a known probability.

Box 2.14: Defining sampling

What is sampling? Sampling is the selection of a representative portion or part of a whole

population or group of things that could be analyzed, to make conclusions about the whole. For example, when doing a survey, it would be expensive and difficult to survey the entire population being targeted. Sampling allows the selection of a proportion of that total population to give representative answers to questions. In designing your sampling methods it is essential to minimize potential bias (see Chapter 3) and try to accurately represent the whole population. A key question to always keep in mind is: “Who is being included and who is potentially being excluded in light of our sampling methodology?” Choices therefore have to be made about: • What the appropriate method is for selecting samples;

• What the appropriate sample size is (e.g. how many households to sample from the population); and

• Who should be included in it so it is representative of the whole population (e.g. which households should be selected).

What is the purpose of sampling? To reduce the time and cost of data collection about a

population by gathering information from a subset of that population.

What is the sample or target population? It is the whole population from which a representative

sample is drawn. Common examples of sample or target populations in FSL surveys include the entire population of specific geographic areas such as a nation, province, region or town. Refugee or IDP camps may also be defined as sample populations. The population should be well defined before determining a sample and undertaking a survey.

What is the sampling frame? It is a list of the total population or units or a geographical

boundary from which a sample is drawn. In strictly controlled refugee camps or villages with defined boundaries and little in–out migration, camp lists may be exhaustive and provide a useful sampling frame. In more fluid situations where populations change or are not known, geographic areas may serve as the sampling frame.

What is a sampling bias? It is the tendency of a sample to exclude some members of the

population and over-represent others. A common source of bias in FSL surveys, especially in emergency and displacement contexts, is when the sampling frame does not include the whole sample population. For example, a survey to assess the household food security of IDP households in a conflict-affected area may be strongly biased if insecure areas where IDPs are found are not sampled or if only camp-based IDPs are sampled, with those living in host families left out. In such cases the sample population may need to be reconsidered, or limitations must be clearly spelled out and interpreted in the report. Again the question to consider is “Who is being included and who is potentially being excluded in light of the sampling methodology?”

What is the sampling unit? It is the element or unit selected in sampling which the data refers to.

Most food security and livelihoods indicators use ‘households’ as the sampling unit, while nutrition surveys may use children under 5 years of age especially in anthropometric surveys. Thus, in collecting data on income, assets and coping strategies to determine household food security, individual household units are sampled from all the households in the sample population.

What is a control group? It is a group of households with similar needs and vulnerability as the

programme beneficiaries, who are monitored but who are not benefiting or participating in the programme or project. ACF cannot use control groups to establish a comparative analysis of its project impacts on the population in need, due to ethics and need to respond to identified needs. It is hence recommended to use a stepped-wedge sampling method (see Toolkit 11), which is using several generations and groups of beneficiaries as comparativ e groups to define and measure impact.

The process of sampling includes the following six steps. For more information and detail consult the ACF FSL Assessment Guidelines (2009).

1. Formulate objectives and define what needs to be measured – Here agreement should be