Download PowerPoint

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

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)
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
Money
Availability of a
sampling frame
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