Teaching how to sample in
research:
some things that might help
Mark NK Saunders
The Surrey Business School
www.surrey.ac.uk/sbs
A sampling issue?
n February 2011 the UK’s Outdoor Media Centre launched its
‘Hall of Fame’ competition to identify the 100 best advertising
posters of all time. Working with the History of Advertising
Trust, they generated a list of 500 posters. This was reduced
to a shortlist of 228 posters by a committee of media and
creative experts together with the editor of weekly magazine Campaign. These were displayed on a dedicated website
www.outdoorhalloffame.co.uk.
Creative agencies, media planners, advertisers, media owners
and the general public were invited in an article in Campaign to
go to the web site, view the advertising campaigns and cast
their votes for what they considered to be the best outdoor
posters. Each person was able to cast a total of ten votes, the
best advertisements being “chosen after more than 10,000
reader votes”.
The Surrey Business School
I
www.surrey.ac.uk/sbs
How might sample selection
have affected the results?
5
1
2
8
3
6
7
4
The Surrey Business School
www.surrey.ac.uk/sbs
Session Plan
Sampling is not boring –attracting
attention
Sampling does not always involve balls
and urns –introducing non probability
sampling
Sampling does not always require a
sampling frame
Sampling and statistics –misconceptions
about probability sampling
The sample selected matters –the link
with research question
The how many question -the issue of
sample size
The Surrey Business School
www.surrey.ac.uk/sbs
Attracting attention
“A small test of
50 women found
almost nine out of 10 of them said
their skin looked more luminous
after using it, while 72 per cent
said fine lines appeared less
visible.”
The Surrey Business School
www.surrey.ac.uk/sbs
Introducing non probability
sampling (1)
The Surrey Business School
www.surrey.ac.uk/sbs
Introducing non probability
sampling (2)
Guildford
 Some implied research questions (students’ suggestions)
How do people manage their careers?
What is the best way to manage a career?
The Surrey Business School
www.surrey.ac.uk/sbs
Sampling does not always
require a sampling frame…
Data collected by…
 Catching commuter train from
Guildford to London
 2nd class ticket
 5 weeks – Monday to Friday
 Interviewing 5 people on average
each journey
Unclear regarding…
 Time of year
 How selected participants
The Surrey Business School
www.surrey.ac.uk/sbs
The sample selected matters
Characteristics of population from which
sample selected
 Catching commuter train
(commuters?)
 Living near Portsmouth –
Guildford – London line
 Working in London
 Working “9 to 5”
What else?
The Surrey Business School
www.surrey.ac.uk/sbs
Sample selected matters (2)…
Generalisabiity: the way in which the sample is drawn
affects the extent to which the findings can be
applied (generalised) or other settings
Sample from:
 Commuters
 Living south of London
 Unlikely to be senior
managers
 Working “9 to 5”
 Unlikely to be employed
in manufacturing
Research Question
 People
 Living anywhere
 With a career
 Doing any type of job
The Surrey Business School
www.surrey.ac.uk/sbs
So what have we done so far?
 Hopefully sampling is not boring –real issue in research
 Sampling does not always involve balls and urns –introduced
non probability sampling and shown in relation to this…
 …sampling does not always rely on a sampling frame –can’t
get a list of passengers easily
 The sample selected matters…
The Surrey Business School
www.surrey.ac.uk/sbs
Testing understanding…
What is the relationship between
a population and a sample?
1. The sample is a sub set
of the population
2. The sample is the
population
3. The sample is always
representative of the
population
4. The sample is the
method by which you
select the population
www.rwpoll.com
0%
1
0%
0%
2
3
0%
4
15
The Surrey Business School
www.surrey.ac.uk/sbs
Samples … realities in
management research (& the
Guildford study)
REALITIES
 Don’t always have sampling frame
 Sampling frame often restricted
 Difficult to show sample truly random
CONSEQUENCES
 Population assumed itself to be a
random sample from larger population or
 Inferences (including statistical) confined to actual population from
which sample drawn –can’t generalise statistically
 May not be able to answer certain questions … rephrase the question!
The Surrey Business School
www.surrey.ac.uk/sbs
Unclear how non-probability
sample was drawn
Sampling
Probability
Quota
Non-probability
Purposive
Snowball
Quota
Volunteer
Haphazard
Self
selection
Convenience
But different
nonHomogeneous
Typical
Extreme
probability
case
purposive
case
techniques
purposive
purposive
would have
Critical case
Theoretical
Heterogeneous
differing
purposive
purposive
implications
The Surrey Business School
www.surrey.ac.uk/sbs
The how many question –what
non-probability sample size?
 REMEMBER –making generalisations to theory not about
a population (except with quota samples)
 Sample size depends upon research question and
objectives…
 what need to find out
 what will be useful
 what is credible
 what can be done within available resources
Continue until
reach data
saturation
The Surrey Business School
www.surrey.ac.uk/sbs
Non-probability sample size
...more detail
Author
Nature of study
Size
Bertaux (1981)
Qualitative
15
Kvale and Brinkmann (2009)
Interviews
5-25
Bernard (2000); Morse (1994)
Ethnographic
35 - 36
Creswell (1998); Morse (1994)
Grounded theory
20 - 35
Creswell (1998); Morse (1994)
Phenomenological
5 - 25
Guest et al. (2006); Kuzel (1992); Considering a
Romney, Batchelder and Weller
homogenous
(1986)
population
4 – 12
Kuzel (1992); Creswell (2007)
12 - 30
Considering a
heterogeneous
population
Source: Saunders (2012)
The Surrey Business School
www.surrey.ac.uk/sbs
Non probability sample selection
 Justifying choices
 Following a recipe
for Quota
sampling
The Surrey Business School
www.surrey.ac.uk/sbs
Misconceptions about
probability sampling –simple
random sampling
 Selecting a sample using simple random sampling will select
the sample evenly from the population
 If a truly random process is used to select a sample from a
population, then the resulting sample will be just like the
population, but smaller –a miniature replica of the population.
 The size of the sample should be proportional to the size of
the population. -the ratio of sample size to population size
needs to be considered when deciding how large the sample
should be.
 The larger the sample the greater the accuracy
The Surrey Business School
www.surrey.ac.uk/sbs
Misconceptions…
-simple random sampling
selects evenly
Last five UK lottery draws
5
17
32
39
42
40
9
22
31
34
35
43
7
10
12
15
25
42
2
9
27
33
39
40
8
14
15
17
20
47
Look at the
pattern of
numbers
The Surrey Business School
www.surrey.ac.uk/sbs
Misconceptions about probability
sampling –miniature replica
 Write down your student number.
 Then answer the questions on the slide for the last digit of
your student number
115678 = 8
113459 = 9 etc etc
 Then, I will show the questions again but this time we will
ALL answer them
 In comparing the answers, we will be able to see realities of
random sampling…. (and of questionnaires!)
(Idea from Prof Sam Warren)
The Surrey Business School
www.surrey.ac.uk/sbs
I often receive bad service when
I am shopping in England
(student number ending 1, 4, 5)
27%
1. Strongly agree
2. Agree
3. Neither agree or
disagree
4. Disagree
5. Strongly disagree
20%
20%
17%
1
2
17%
3
4
5
The Surrey Business School
www.surrey.ac.uk/sbs
Results –a miniature replica?
Students
Number
1,4,5
2,3,8
6,7,0
All (incl.
9)
Impact of
larger
sample size
Strongly
agree
Agree
Neither
Disagree
Strongly
disagree
57
63
47
20%
12%
19%
17%
19%
18%
27%
29%
26%
17%
23%
19%
20%
17%
18%
162
17%
18%
28%
20%
17%
Variation in
number
responding
Variations in
response
The Surrey Business School
www.surrey.ac.uk/sbs
Misconception: Sample size is
proportional to population(1)
The problem of making statistical inferences from a small
probability sample
Sample size for 95% certainty with 5% margin of
error 350
300
Sample size
250
200
150
100
50
0
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Total Population
The Surrey Business School
www.surrey.ac.uk/sbs
Probability sample and
population size (2)
350
300
Sample size
250
Sample size for 95%
certainty with 5% margin
of error
200
150
100
50
0
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
Total Population
population
5%
2%
10,000
370
1936
1,000,000
384
2395
10,000,000
384
2400
The Surrey Business School
www.surrey.ac.uk/sbs
Misconception: larger the
sample size greater the accuracy
0.05
0.04
0.03
0.02
0.01
0
0
population
2000
4000
6000
5%
2%
100
79
96
200
132
185
500
217
226
8000
10000
 The larger the probability sample size, the more confident can be
that represents the true population for a given population size.
The Surrey Business School
www.surrey.ac.uk/sbs
Different probability sampling
techniques would have different
implications (given knew total
population)
Sampling
Probability
Simple
random
Systematic
Stratified
random
Cluster
Multi-stage
The Surrey Business School
www.surrey.ac.uk/sbs
Probability sampling –following
a recipe
 How to do it most text books
-just follow the recipe…
-use practice exercise-justify choices
 Key issue is understanding why and implications of what do
-discuss case studies...
-use multiple choice questions to check
http://wps.pearsoned.co.uk/ema_uk_he_saunders_resmethb
us_5/111/28550/7309036.cw/index.html
The Surrey Business School
www.surrey.ac.uk/sbs
Probability samples …
statistical inference
 Inferential statistics allow
general statements to be made
about the population form the
sample providing data normally
distributed
 Betting about the probability of
being wrong or inferring invalid
conclusions
“It is a sad fact that if one knows nothing
about the probability of occurrence for a
particular sample of units for observation,
very little of the inferential machinery …
can be applied”
(Hays 1994: 227)
The Surrey Business School
www.surrey.ac.uk/sbs
But beware of the effect of
sample size on practical
significance…
 The larger the sample size the more likely
your results are statistically significant
 Statistically significant result - unlikely to
have occurred by chance
 Practically significant result – practically
meaningful in real world
Jacob Cohen
The Surrey Business School
www.surrey.ac.uk/sbs
Response rates
Most important aspect is sample is representative
of ...
High response rate can help this
Non-responses different from rest of population
as did not respond!
Non response due to 4 interrelated problems:
refusal to respond
ineligibility to respond
inability to locate respondent
respondent located but unable to make contact
The Surrey Business School
www.surrey.ac.uk/sbs
Bibliography (1)
 American Association for Public Opinion Research
(2008) Standard Definitions: Final Dispositions of Case
Codes and Outcome Rates for Surveys (5th edition)
Lenexa, KA: AAPOR
 Baruch Y and Holtom BC (2008) Survey response rate
levels and trends in organizational research Human
Relations 6.8 1139-60.
 Groves, R.M., Dillman, D.A., Eltinge, J.L. and Little,
R.J.A. (eds.) (2001) Survey Non-response. New York:
John Wiley.
 Guest, G., Bunce, A. and Johnson, L. (2006). How many
interviews are enough? An experiment with data
saturation and validity. Field Methods. Vol. 18, No. 1, pp.
59-82
 Lance, C.E. & Vandenberg, R. J. (2009), Statistical and
Methodological Myths and Urban Legends. Routledge,
London:Taylor & Francis.
The Surrey Business School
www.surrey.ac.uk/sbs
Bibliography (2)
 Meyer, J.H.F., Shanahan, M.P. & Laugksch, R.C. (2005),
“Students’ Conceptions of Research. I: A qualitative and
quantitative analysis”, Scandinavian Journal of Educational
Research, 49. 3, 225-244.
 Patton, M.Q. (2002) Qualitative Research and Evaluation
Methods. 3rd edn. Thousand Oaks, CA: Sage, Ch.5
 Rogelberg SG and Stanton JM (2007) Introduction:
Understanding and dealing with organizational survey nonresponse Organizational Research Methods 10.2 195-209
 Saunders, M.N.K., Lewis, P. and Thornhill, A. (2012)
Research Methods for Business Students. 6th edn.
London: FT Prentice Hall, Ch.7.
 Saunders M.N.K. (2012) ‘Choosing research participants’
in G Symons and C Cassell (eds) The Practice of
Qualitative Organizational Research: Core Methods and
Current Challenges.London: Sage 37-55
The Surrey Business School
www.surrey.ac.uk/sbs