Sample Selection – Essay Furious

Topic 7
Sample Selection
Research for Business and Tourism

Study Guide Topic 7
Textbook: Quinlan et al. (2015)
Business Research Methods Chapter 10
pp. 168-189.

Research as a process
The research process Quinlan et al. (2015, p. 3).
The four frameworks approach
Model of the four frameworks. Quinlan et al. (2015, p 7).
This Week’s Objectives
1. Discuss the role of sampling in social science research
2. Define sampling terminology
3. Distinguish probability and non-probability
sampling techniques
4. Discuss the range of probability and nonprobability sampling techniques and the
situations in which they are applied
5. Outline factors to consider in determining the
sample size
6. Recognise types of sampling errors
What is sampling and why do we need it?
The practice of studying ALL the units or cases in a
population is known as a
However, this is normally not possible because the
population may be
too large to survey everyone.
Studying a sample allows research to be conducted using
less resources than studying entire
Sampling is about selecting a small number of units or
from a larger collection or ‘population’ for
inclusion in a study

Why use a sample?
Studying all these passengers would be prohibitively expensive and time consuming
The purpose of this study is to determine passenger satisfaction levels with inflight service provided by the Australian low-cost airlines Virgin and
Jetstar between 1
st July and 31st December 2016
How many passengers would fly with Virgin and Jetstar during this period?
Tens of thousands, hundreds of thousands, millions…?
Australian domestic air travel 2015-16

We could design our study so that we randomly
select a sample of say, 3000 passengers
This would drastically reduce the resources needed
to conduct the study,
So long as we select our sample based upon accepted
probability sampling methods, the results would
representative of that wider population of
This means that studying a sample is effectively as
good as surveying every person
that has flown
with these two low-cost airlines during that period

Some sampling terms you need to know
Quinlan et al. (2015, p. 177)
Sampling Terminology
Population/Target population
A complete group (of persons, things, events – called
units/subjects/elements) sharing some common
ALL the units that are the focus of a particular research
E.g. every single passenger who has flown with Virgin or
between 1st July and 31st December 2016
Investigation of all the members of a population
E.g. we study every single passenger who has f lown with
Virgin or Jetstar
between 1st July and 31st December 2016
Sampling Terminology
A subset of a larger population.
The means by which units are selected for
inclusion in the sample
E.g. every 5th passenger on manifest lists on each
flight during the study period

Sampling Terminology
Sampling Ratio
The ratio of the sample size to the size of the target
Target population = 400,000
Sample size = 3,000
Sampling Ratio = (3,000/400,000) x 100 = 0.75%
This is the percentage of the population we
intend to study

Sampling Terminology
Sampling Frame
A list representing all units of a target
from which the researcher selects a
E.g. Virgin & Jetstar passenger lists from July to
December 2016
Other examples might be phone books,
electoral rolls, list of ticket holders at an

Sampling Terminology
Sample Unit
An individual unit selected from the sampling
for inclusion in the sample group
E.g. from our airline example…
A sampling unit = A passenger who flew with Virgin
or Jetstar between 1
st July and 31st December 2016
What is a representative sample?
A sample that accurately reflects the population from which
it was drawn.
If the researcher designs the sample carefully s/he can
obtain a
representative sample, which is one that allows
the researcher to produce accurate
generalisations about
the larger group.
This means that the researcher can say that the outcomes of
a study are
true of the wider population (within a specific
margin for error) from which the sample was drawn – even
though only a
small percentage of the entire population has
been studied

Based on mathematical theory that a random sample can be
representative of a largergroup
The word ‘random’ does not imply that the sample is selected
aimlessly but that each element has an equal chance of being
E.g. If I were to close my eyes and walk around the room and
tap 10 people on the head to participate in a study, that is
not a random technique – it is haphazard as I have no
in place to ensure that each person has an equal
of being included in the sample
Techniques include: simple random, systematic, stratified
random and multistage cluster sampling
Probability or Random Sampling
Probability Sampling Techniques
Simple Random Sampling
Ensures ALL elements in a
population have an
of being included in
the sample
Drawing names/numbers from a
For larger samples a random
numbers table
is commonly used,
or the sample is

61287 45055 49679 36251
58461 9182 73095 17734
49588 20104 74215 48480
95603 95559 2592 90874
76129 78145 34527 26170
87525 51118 53182 4321
39551 89221 3827 5851
53170 91435 77786 81074
22944 45992 54389 37900
39512 12888 84079 22020
54074 72689 38725 97027
46344 65052 65548 87960
77591 89487 84384 97329
83577 72528 60096 23866
87656 30817 27855 95997
23807 19873 51093 15889
13681 43462 11391 19290

Probability Sampling Techniques
Systematic Sampling
Selects units / subjects based on
pre- determined interval
E.g. every 5th name on a
passenger list
Often used in street / intercept
e.g. every fifth
house/passerby, to obtain a
random sample

4 5 10

Probability Sampling Techniques
Stratified Sampling
The researcherdivides the target
population into ‘
(strata) based upon certain
criteria (e.g.
age, gender, location) on which we want to
ensure correct representation in the
A random sample is then drawn from
each sub-population
using simple or
systematic sampling
Undertaken to reduce sampling error
and ensure that each subpopulation is proportionally
in the final sample.

Probability Sampling Techniques
Useful when no sampling frame is available (and constructing one is
too costly) or when the population is geographically dispersed yet we
want to visit the sample in person.
Involves drawing several different samples in order to obtain a final
Start by drawing a sample of clusters of elements. Then for each
cluster, draw a sample of anotherclusteror individual elements. If
the former, then draw another sample until individual elements are
Example: We want to study cafes in Australian cities. Using
multistage cluster sampling we might firstly draw a random sample
of cities (clusters). Then, for each city in the sample we choose a
random sample of cafes (population elements).

Non-Probability or Non-Random Sampling
In many instances it is not possible, practical or necessary to obtain a
random sample
A suitable sampling frame may be unavailable or too expensive to
generate (e.g. all people have evervisited a particular town)
Some research does not require the results to be generalised to the
, e.g. exploratory designs, pre-testing
In these cases a non-probability or non-random sampling approach
is taken
Such samples do not accurately represent the population of
This is because subjects are not being selected randomly – i.e. they do
not have an equal chance of being selected in the sample
Non-Probability Techniques
Convenience Sampling
Haphazardly selecting a sample or units / subjects because
they are
easily accessible
E.g. patrolling the baggage collection area in a Virgin / Jetstar
terminal and
selecting people who look friendly or approachable
Should be used as a last resort – capable of producing
ineffective and
highly unrepresentative samples.
Non-Probability Techniques
Purposive or Judgment Sampling
The researcher uses her/his judgment to deliberately select the
most appropriate person(s)
for inclusion in the study
Participants are selected based upon their ability to provide the most
pertinent information
Used most commonly in qualitative studies
This method is often used in order to select difficult-to-reach,
expert, deviant, extreme, marginalized, misunderstood or
unique populations
E.g. purposively selecting human resource managers (as experts) in
an exploratory study of
performance management of hotel
E.g. There is no list out there that identifies all problem gamblers
from which we can randomly sample. We can potentially recruit
people from counselling services to compile a ‘sample’ of problem

Non-Probability Techniques
Snowball Sampling
Another common sampling approach in
Builds a sample through referrals.
Used when members of a sample can only be
through their links with other
This often occurs with populations
that are not easily identified or accessed (e.g.
homeless people)
E.g. you interview someone and they tell you ‘my
mate Joe Smith could tell you more
about this’
You contact Mr Smith for an interview and he
recommend another friend – the snowball

 

Non-Probability Techniques
Quota Sampling
Is one (small) step up from pure convenience sampling
as the researcher attempts to introduce a degree of
to the sample by ensuring different
groups are included
Similar idea to stratified sampling but units within strata
are not chosen randomly, just chosen by convenience until
the required number of units has been met.

Once we have decided upon our
sampling technique, how do we know
how big our sample should be?
Sample Size – Quantitative Research
Most quantitative studies aim to obtain large sample sizes
where possible
To gain a higher degree of accuracy
To facilitate in-depth analysis of sub-groups within the
A census is the preferred option – but this is rarely possible
If a census is not possible, the sample size is often
determined relative to the size of the target population
and taking into account the homogeneity of the population.
Also need to consider cost, time, data analysis methods
Sample Size
There is no golden rule regarding how big a sample
should be
large sample without random sampling or with a
poor sampling frame is less representative than a
smaller one with random sampling and an excellent
sampling frame’
(Neuman, 2003, pp. 231-232)
Neuman, WL (2003) Social Research Methods: Qualitative and Quantitative Approaches, Allyn and Bacon, New York.
Sample Size – Quantitative Research
Increasing sample size increases accuracy but at a declining rate.
Neuman (2003) argues that sample size should be a function relevant
to the
size of the target population
Populations <1,000, sample size
= ~30% (Population 1000,
sample 300)
Populations ~10,000, sample size = ~10%
(Population 10,000, sample 1000)
Populations >150,000, sample
size = ~1% (Population 150,000,
sample 1500)
Also, the more homogenous the sample (i.e. most people are expected
toanswer in a particularway), the smaller the required sample size.

Sample Size – Quantitative Research
When we speak about sample sizes in quantitative research we are
referring to the
numberof returned, usable responses
E.g. if our target population is 6,000 and we post out 600
(10%) – then we have got it wrong!
We really needed around 600 responses
So we need to actually send out a lot more than 600 surveys to
allow for non- response
Sample size and response rates are two different things
We aren’tconcerned with how many went out – we want to
know about
how many came back
All surveys will experience a degree of non-response – some
small, others extremely large to the point where
data analysis
is not possible
Pitfalls of postal surveys – low (20% average) response rate
Sample Size – Qualitative Research
It is very difficult to plan how much data you will need to
collect in a
qualitative study
However, one point of guidance is the following: Data is
collected until the
saturation point is reached
This is the point where no new information is
/added to your study – it is said that sufficient
has been collected
Hence, when writing a research proposal the researcher
not pre- empt how much data will need to be
collected – they state that they will
continue until this
saturation point

Errors Associated with Sampling
Sampling frame error: Sampling frame may not fully
represent population.
Random sampling error: the extent to which the
sample differs from the population due to chance
Non-response error: differences due to not all
selected sample units responding. Also called nonresponse bias.
Plan to minimise these errors.
The steps in selecting a sample
1. Define the target population (draws on problem statement,
including scope)
2. Select (or compile) a suitable sampling frame (complete as
3. Determine whether probability or non-probability sampling
is required
4. Determine which sampling technique to use.
5. Determine the required sample size
6. Select sample units
Sampling & Your Assignment 4
In the sampling section you must:
State whether you are using probability or non-probability
You should explain explicitly why yourselected sampling
technique is suitable
foryour study
Good idea to include a paragraph on the sampling procedures that
could be applied to your study – noting their advantages and
Then narrow down to the one you have selected and justify your
with a reference or two. These references will most likely
be to research textbooks.
E.g. Neuman (2003) stated that stratified sampling was
appropriate under such and such circumstances, therefore
stratified sampling has been selected for the present study…

Sampling and Your Assignment 4
Specifically, you should cover the following:
Define your target population (draws on problem statement, including
Whether probability or non-probability sampling will be used and why.
Link the “why” to the research problem, the availability of a sampling
frame and the research design.
What sampling technique (e.g. simple random, stratified, purposive
etc.) will be used and why.
What the planned sample size will be and why.
Integrate references. In the sampling section, these would mostly be
references to research books when you are outlining advantages and
disadvantages and/or justifying choice of available/suitable methods.
You might also come across journal papers in your area that also assist with
sampling method (e.g. “This research will follow the sampling approach
used by Smith et al. (2010) in a study of the same concepts but in a
different context…”)