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Slide 1

LIS 570

Selecting a Sample

Slide 2

Summary

• Sampling - the process of selecting observations

• random; non-random

• probability; non-probability

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Aim

• A representative sample

• A sample which accurately reflects its population

• Avoiding bias

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Basic terminology

• Population - the entire group of objects about which information is wanted

• Unit - any individual member of the population

• Sample - a part or subset of the population used to gain information about the whole

• Sampling frame - the list of units from which the sample is chosen

• Variable - a characteristic of a unit, to be measured for those units in the sample

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Step 1: Identify the Population

• The units of analysis about whom or which you want to know

• Define the population concretely

• Example

• Adult Residents of Seattle

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2.  Decide on a Census or a Sample

• Census

• Observe each unit

• an “attempt” to sample the entire population

• not foolproof           

• Sample

• observe  a sub-group of the population

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3.  Decide on Sampling Approach

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Random sampling

• Random (Probability) Sampling

• Each unit (element) has the same chance (probability) of being in the sample

• Chance or luck of the draw determines who is in the sample (Random)

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Random samples

• Each unit has a known probability or chance of being included in the sample

• An objective way of selecting units

• Random Sampling is not haphazard or unplanned sampling

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Types of random sampling

• Simple random sample

• Systematic sampling

• Stratified sampling

• Cluster sampling

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How to choose

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Simple random samples

• Obtain a complete sampling frame

• Give each case a unique number starting with one

• Decide on the required sample size

• Select that many numbers from a table of random numbers

• Select the cases which correspond to the randomly chosen numbers

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Systematic sampling

• Sample fraction

• divide the population size by the desired sample size

• Select from the sampling frame according to the sample fraction

• e.g sample faction = 1/5 means that we select one person for every five in the population

• Must decide where to start

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Stratified sampling

• Premise -  if a sample is to be representative then proportions for various groups in the sample should be the same as in the population

• Stratifying variable

• characteristic on which we want to ensure correct representation in the sample

• Order sampling frame into groups

• Use systematic sampling to select appropriate proportion of people from each strata

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Cluster sampling

• Involves drawing several different samples

• draw a sample of areas

• start with large areas then progressively sample smaller areas within the larger

• Divide city into districts - select SRS sample of districts

• Divide sample of districts into blocks - select SRS sample of blocks

• Draw list of households in each block - select SRS sample of households

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Random Samples

• Advantages

• Ability to generalise from sample to population using statistical techniques

• Inferential statistics

• High probability that sample generally representative of the population on variables of interest

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Non-random Samples

• Purposive

• Quota

• Accidental

• Generalizability based on “argument”

• Replication

• Sample “like” the population

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Selecting a sampling method

• Depends on the population

• Problem and aims of the research

• Existence of sampling frame

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Conclusion

• The purpose of sampling is to select a set of elements from the population in such a way that what we learn about the sample can be generalised to the population from which it was selected

• The sampling method used determines the generalizability of findings