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Module 4 - Session 4.5a Sampling. Learning Objectives. By the end of this session, you should be able to: Describe the different types of sampling methods and their strengths and weaknesses Select an appropriate sampling method
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Learning Objectives By the end of this session, you should be able to: • Describe the different types of sampling methods and their strengths and weaknesses • Select an appropriate sampling method • Be aware of how sampling methods can be linked to the collection, analysis, and interpretation of data • Know the sample designs that can be used for an EFSA and Rapid Rural Assessment
Sampling Overview • Sampling is the process of selecting a small number of people from a larger group of people • The goal of sampling is to estimate something in a larger group • To estimate something in a larger group, the smaller group must be representative of the larger group
Bias Non-proportional sampling Accuracy Precision Random selection Representative Sampling Overview • Sampling can be a complicated process and can be simplified by understanding a few key elements
Sampling Overview • Sampling can be a complicated process and can be simplified by understanding a few key elements • Sampling is done because it is cheaper and quicker than questioning everyone in the area and will give you accurate information if done right there are 600,000 women in the province ten teams can interview 60 women a day interviewing 200 women will take 3.3 days interviewing 600,000 women will take 2.7 years
Sampling Overview • Sampling can be a complicated process and can be simplified by understanding a few key elements • Sampling is done because it is cheaper and quicker than questioning everyone in the area • The challenge in sampling is to • select the right group of people to interview • accurately interpret the information collected from the smaller sampled group to the larger target population
Precision and Accuracy Not precise Not accurate Not precise Accurate Precise Not accurate Accurate Precise
Pop Quiz Representative or bias sample?
biased… • Mothers who use the clinic may not be representative of all mothers in the population • Only people with a radio can hear about the survey • Mothers must go to the clinic on Tuesday
Representative or biased sample? All the houses in a village is numbered. You randomly select 1% of the houses and write down the house number that has been selected. You go to the house number and interview the occupants.
representative… • You had a list of all the houses in the village • Every house had an equal chance of being selected • You were able to go the houses that were selected • This is an example of simple random selection
Representative or biased sample? An NGO website asks online visitors to answer questions on whether NGOs appropriately use food assistance in emergencies
biased… • People who visit the NGO home page may not be representative of the humanitarian community • People with strong opinions about the subject are more likely to participate • This is an example of self-selection bias where participants choose to be sampled
Errors in Sampling • Measurement • Confusing questions • Pilot questions • Interviewer suggests responses • Train interviewers • Sampling Not representative of target population Random selection of respondents Too few participants Recruit more participants • Response • People refuse to be interviewed • Train interviewers • People give response they think you want • Train interviewers
Examples of probability sampling You have a list of 35,000 people registered in a refugee camp. You sample every 350th person on the list. The chance of being selected is 350/35,000 or 1 in 1000.
Examples of probability sampling You have the names of the 45 villages in a province and write the names of each village on a piece of paper. You put all the pieces of paper in a glass jar, mix the pieces of paper and pick out 5 pieces. The chance of a village being selected is 5 out of 45 or 1 in 9.
Examples of non-probability sampling You go to the village square and ask people in the market about the kind of food they have at their house.
Examples of non-probability sampling You go to a village and speak to the key informants about food security and availability
When to Use Probability andNon-probability Sampling • Probability sampling • Number or percent of people with a characteristic • Assess small changes when monitoring or evaluating a program • Nutritional or health surveys use probability sampling • Non-probability sampling • Some information is more important than an accurate and precise number • Pilot testing • Rapid appraisal methods often use non-probability sampling
The big difference… Probability sampling – a lot of effort is taken to select the person to interview because that person represents many other people. That process must be replicated for each person that is interviewed. Non-probability sampling – there is less restrictions on selecting the person to interview.
so…why bother with probability sampling? • Sample is representative of a larger population • Results can be generalized • Minimizes bias for selecting people to be interviewed
so…why bother withnon-probability sampling? • Easy and fast recruitment • Explore a problem and some basic idea of a solution • Provide insight and comprehension of a situation • Good at probing below the surface for affective drives and subconscious motivations • May be only realistic option in an emergency
Summary • Sampling gathers data on a small group of people to help understand what is going on in a larger group • Probability and non-probability sampling are the two major sampling designs • The challenge in sampling is to 1) select the right group of people to interview and 2) accurately interpret the information collected from the smaller sampled group to the larger target population
