LIS 570 Selecting a Sample

Summary Sampling - the process of selecting observations random; non-random probability; non-probability You don’t have to eat the whole ox to know that the meat is tough

Aim A representative sample Avoiding bias A sample which accurately reflects its population Avoiding bias

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

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

2. Decide on a Census or a Sample Observe each unit an “attempt” to sample the entire population not foolproof Sample observe a sub-group of the population

3. Decide on Sampling Approach Random Non-random Probability Non-probability

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)

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

Types of random sampling Simple random sample Systematic sampling Stratified sampling Cluster sampling

How to choose The nature of the research problem Availability of a sampling frame Money Desired level of accuracy Data collection method

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

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

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

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

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

Non-random Samples Purposive Quota Accidental Generalizability based on “argument” Replication Sample “like” the population

Selecting a sampling method Depends on the population Problem and aims of the research Existence of sampling frame

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 Random samples X Non-random sample