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 toknow that the meat is tough

Aim

A representative sample 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

Census 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 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)

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

Random samples

Types of random sampling

Simple random sample Systematic sampling Stratified sampling Cluster sampling

How to choose

The nature of the research problem

Availability of asampling 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

Non-random sample