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1.
Slide 1
Sampling Issues in
Quantitative Research
Anji Waring
Faculty of Health and Social Work
Slide 2
What is a sample?
• A small group drawn from a larger
population.
• The population is the entire set of
subjects in a given group that form
the focus of the study.
• It may be necessary to distinguish
between the theoretical and
accessible population
Slide 3
How can you access your
sample?
• Sample Frame: A list, register, map
or other set of data that contains all
the accessible population. (e.g phone
book; electoral roll; NMC register)
• The sample is the group selected
from your sample frame – not the
group who are actually in the study.
2.
Slide 4
Selecting a sample
• In order to draw conclusions about a
larger population, the sample must be
representative of that population.
• There are two main approaches to
sampling: Probability and Non-
probability
Slide 5
Probability Sampling
• Any method of sampling that utilizes
some form of random selection. This
means that different units in your
population have equal chance of being
chosen.
• This is often done by using random
numbers either generated by
computers or by using tables.
Slide 6
Types of probability
sample
• Simple Random sampling: Generated from
random tables etc of the whole population.
• Stratified Random sampling: (proportional or
quota random sampling) – dividing the sample
frame into homogenous subgroups and taking a
simple random sample from each group.
• Cluster Sampling – divide population into
clusters (e.g wards) and then take a random
sample of the clusters
• Multi Stage Sampling
3.
Slide 7
Non-Probability Sampling
• Includes all sampling procedures in
which chance plays no rule in the
determination of the actual make up
of the sample.
Slide 8
Types of Non-Probability
Sampling
• Convenience Sampling: Based on
accessibility to the researcher
rather than on the basis of random
sample procedures. Often used when
time and resources are limited.
• Volunteer Sampling: Sample consists
of subjects who have responded to an
advertisement & have volunteered to
take part in the study.
Slide 9
Types of Non-Probability
Sampling
• Quota Sampling: Deliberate choice of
approaching a quota of respondents to
represent the population (e.g. men and
women)
• Snowball: In hard to reach groups, original
respondents are asked to name others who
share their characteristics
• Purposive Sampling: Term used in
qualitative research.
4.
Slide 10
Sample Size
• Has to be large enough to allow for
generalisation. This is influenced by:
– Subgroups for data analysis
– Effect size – difference between groups
– Statistical calculations (may require a
power calculation to determine size)
– Likely response rate
Slide 11
Bias in relation to
Sampling
• Sampling Bias: means that the
sampling procedure results in a
sample that does not represent the
population of interest.
• Selection Bias: occurs if the
characteristics of the sample differ
from those of the wider population.
Slide 12
Randomisation of a Sample
(Random Assignment)
• This is NOT the same as random
sampling.
• A procedure which is used to assign
subjects randomly to treatment or
control groups, in which the subjects
have an equal opportunity to be
assigned to either group.
•Sampling Issues in
Quantitative Research
5.
Anji Waring
Faculty of Health and Social Work
What is a sample?
•A small group drawn from a
larger population.
•You could to it on an entire
population but it is unusual
•Looking for a group who
represents the group
•The population is the entire
set of subjects in a given
group that form the focus of
the study.
6.
•The group you are going to
apply the results
•Want to apply the results
to the bigger group
•May want to limit it (uk
population of women over 50
with breast cancer
Who
What
When
Where
•It may be necessary to
distinguish between the
7.
theoretical and accessible
population
IN THEORY WHO CAN YOU ACCESS (THEORETICAL)
THOSE CAN GET ACCESS TO YOU (ACCESSIBLE)
NOT BE ABLE TO GET TO EVERYONE!!!
How can you access your
sample?
•Sample Frame: A list,
register, map or other set of
data that contains all the
accessible population. (e.g
phone book; electoral roll;
NMC register)
8.
•HOW YOU ARE GOING
TO CHOOSE YOUR
SAMPLE IN THE FIRST
PLACE!!!
Not always obvious from
the survey
Need to read and find out
how they got hold of the
people.
•The sample is the group
selected from your sample
frame – not the group who are
actually in the study.
.
Some may say no – they are still part of the sample because they become the non-respondants
So sample is the numbers who did and didn’t respond
Beware of self selecting they can produce bias.
9.
Poor response rate could mean you are missing out on poeple
Selecting a sample
•In order to draw conclusions
about a larger population, the
sample must be
representative of that
population.
Got to make sure that it respesents the numbers and the types of people studied.
•There are two main
approaches to sampling:
Probability (proably
represattive) and Non-
probability (probably not
resprestative)
Probability Sampling
10.
•Any method of sampling that
utilizes some form of random
selection. This means that
different units in your
population have equal chance
of being chosen.
Like raffle
The lotery
•This is often done by using
random numbers either
generated by computers or by
using tables.
•Usually more structured in
research
See hand out.
11.
One hundred people who
attended an outpatient clinic.
So you have 00 to 99 just the
way of making sure that there
is an equal cance of being
selected. (Cormack (2001))
You can use random numbers
and do things lk usisnt them
backwards or upside down or
inside down.
Would this give you a random
and representative
12.
A way of not being biased
Some use computers – work it
out.
Sample to respesent the
population as a whole.
Look for demographic table
Breaking it all down
Then you decide if that is
represtative of what you are
looking for.
Types of probability sample
13.
•Simple Random sampling:
Generated from random tables
etc of the whole population.
•Take the whole population. Take
the sample frame reprenstitive of
population then sample randomly
from it.
•Accepted as the best way!!!!
•Stratified Random sampling:
(proportional or quota random
sampling) – dividing the sample
frame into homogenous subgroups
and taking a simple random sample
from each group.
•Cluster Sampling – divide
population into clusters (e.g
14.
wards) and then take a random
sample of the clusters
Multi Stage Sampling: Cluster
then stratify then randomise
Although you do a random
sample, you need to know the
demography because chance can
result in a bias in the first
place.
Looking to build a body of
evidence. Evidence that would
help to inform rather than just
as a one of piece of research
(eg, MMR)
15.
Non-Probability Sampling
•Includes all sampling
procedures in which chance
plays no rule in the
determination of the actual
make up of the sample.
Less rigourous
Very often done
The easiest way to getting a
sample
Some include all the sample
frame even though it is not
16.
a randomly generated
sample.
More descriptive but still
quantitive.
Types of Non-Probability
Sampling
•Convenience Sampling: Based
on accessibility to the
researcher rather than on
the basis of random sample
procedures. Often used when
time and resources are
limited.
17.
The weakest form of sampling
But it is the most commonly
used
Not generalisable to a very
great extent.
Sometimes, there is some
randomisation used
Make for more rigour but
still not good enough
•Volunteer Sampling: Sample
consists of subjects who have
responded to an
advertisement & have
18.
volunteered to take part in
the study.
You are not trying to
generalise
Ie, survey want people with
a bereavement difficult
to get a list of people ask
for volunteers got to a
place where you know you
will find people.
You only get people who have
axes to grind.
19.
Journal samples violence in
Nursing standard in 1986
just printed in the journals
400 odd people responded
0.05%
78% of the 0.05%
Then they said that 78% of
nurses have suffered
violence!!!!
Mostly men (men are more
likely to be hit as they work
in places where it tends to
get hit anyway)
But it was a volunteer sample
20.
They may have an axe to
grind
It may be the only way to
access that group
What type of sample
If it was a volunteer
sample, was effort made
to make it representative.
Types of Non-Probability
Sampling
•Quota Sampling: Deliberate
choice of approaching a quota of
21.
respondents to represent the
population (e.g. men and women)
Done in market research
They have to have x who are y,
x who are z… etc.
There is some chance
But not an EVEN chance!!!!
Who ever happens to be there
Not a true sample
A deliberate choice
•Snowball: In hard to reach
groups, original respondents are
asked to name others who share
their characteristics
22.
Ie IDVUS: who are part of a
needle exchange. “could you tell
your mates…..”
Yes…. But….
Not random
May not be true
Confidentiality
Could be people who are similar
with reduced diversity
(did it with football hooligans)
Everyone in the same gang had
similar views
Samples tend to be small
There are skews
There are biases
23.
•Purposive Sampling: Term used in
qualitative research.
Gone out looking for people who
have the attributes that you are
looking for. Not random
Just for QUALATIVE!!!!!
Sample Size
More important in
quantatitve rather than in
qualitive….
Going on and on until you
reach saturation
Where you aren’t
getting anything new
24.
•Has to be large enough to
allow for generalisation.
This is influenced by:
–Subgroups for data analysis
need to be
representative of the
smallest group
Is this big enough for
this to be valid….
A statistision can
usually tell you.
–Effect size – difference
Should be worked out
for you by the
research
25.
Significant numbers in
the group for it is
doing what it is
purporting it is.
–
–between groups
–Statistical calculations (may
require a power calculation to
determine size)
Gives you a number
of how many you
need.
Need to look at if
they say that a
power calculation of
how many people you
need so that they
26.
need to get to make
it do what they want
it to …
The bigger the
better!!!!!!
–Likely response rate
Postal research
rates tend to be
lower.
If you want 100,
you need to send
out 4-500
Face to face, it is
less but you still
need more as
some will not be
27.
interested and
also you will have
biases due to the
interviewer
used…..
You may have
information about
theose who didn’t
respond.
Keep a
demography of
the non-response
rate.
Careful how you
use the
information. is
this reported
28.
without there
consent.
No mimium size
but the smaller
the size, the less
represntable thus
the less
generisable.
Bias in relation to Sampling
•Sampling Bias: means that
the sampling procedure
results in a sample that does
not represent the population
of interest.
29.
•Selection Bias: occurs if the
characteristics of the sample
differ from those of the
wider population.
Randomisation of a
Sample (Random
Assignment)
•This is NOT the same as
random sampling.
Random sampling is when
you randomise
Randomisation is when you
have your sample, you
30.
then randomly assign them
to a group…
So you can have a
convenience sample and
then randomly assign
them.
•A procedure which is used to
assign subjects randomly to
treatment or control groups,
in which the subjects have an
equal opportunity to be
assigned to either group.
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