|Counting and Identification of Beneficiary Populations in Emergency Operations (ODI, 1997, 110 p.)|
|6. Identifying a beneficiary population: a social, cultural, economic and political profile|
Household samples can be used both for obtaining and verifying information. As already stated, a wide range of types of information may be directly relevant to the planning of an assistance programme: cultural, religious, ethnic, economic, social and political contexts in which a population either has been, or now finds itself, are obviously of great importance in understanding the needs and resources of that population. Naturally, the need to enquire into any specific aspect (e.g. religion, possessions or ethnic background), and how access is to be gained to such information if it is sufficiently important, must be decided with respect for privacy and confidentiality. Since the household is a basic planning unit for humanitarian programmes, a professionally conducted household survey (preferably responded to by the women of the household) can be a productive exercise. A household survey can provide the hard data to confirm or reject initial impressions (for example on the number of inhabitants of the average household) arrived at using other methods, such as visual inspection.
Qualitative research and household surveys in northern Iraq
During the Iran-Iraq war in the 1980s the government of Iraq started food distributions in the Kurdish region in the north. Households registered with the Food Department would receive food rations at highly subsidised prices through designated agents. A study immediately after the Gulf War concluded that the system was exemplary in terms of coverage, equity, efficiency and contribution to the nutritional needs of the population. Some time after the Kurdish uprising however the government reduced the amount of food for distribution and imposed an economic blockade against the North. UN agencies and NGOs began to distribute food through the system of food agents, but that programme got criticised for being untargeted. Rather than effect a re-registration, rapid research was carried out in two phases. First informal, qualitative research on the overall food security situation identified processes of impoverishment and strategies for coping, and suggested indicators of poverty and vulnerability. To enable precise targeting and to assess the number and distribution of poor and vulnerable households however, this had to be followed by a household survey in which a questionnaire was used (Ward and Rimmer, 1995).
Given obvious time and resource constraints during an initial assessment, it will rarely, if ever, be possible to consult every household. Therefore, a group, or sample, may need to be selected to represent the composition of the whole group. Several sampling methods can be used for choosing the group, or sample to be surveyed (see box below and for a fuller explanation, Annex 1).
Every person or household in the population has an equal probability of being included in a random sample (e.g. picking names from a hat, or the blindfold and pin exercise used to pick from a list). But again, to draw any valid conclusion, the sample must be representative of the whole population. For example, nutritional data obtained from health services are not representative of the total population. Nor are those collected in the most accessible villages or centres, or in camps that are reported to be in a particularly bad state.
Here, cases are selected at given intervals. For example, if 200 cases are to be selected at given intervals from a listed population of 10,000, every fiftieth case may be chosen.
Cluster or stratified sampling
Instead of selecting individual units as above, in this case the researcher divides the population into groups or categories, called strata or clusters (e.g. by location, ethnic origin, religion, gender, age). By doing this, certain priority groups are guaranteed to be represented. The total population in that group may be surveyed, or random samples drawn from each group or stratum. While not as statistically sound as pure random sampling, this method will guarantee that known priority groups or sites are not overlooked. However, the danger is that because the spread of samples is artificially decided, significant groups or sites not singled out may be ignored, and the sample skewed (distorted) as a result, thus devaluing the information collected.