understanding research module theme 1

1. Learning
Unit 4:
Theme1
SAMPLING
AND DATA
COLLECTI
ON

2.  LO1: Describe an appropriate population as best suited to the needs of a
research study
 LO2: Explain whether or not a research study will make use of either
probability or non-probability sampling methods
 LO3: Justify the use of a particular method of sampling from the range of
sampling methods in relation to the needs of a research study

3. OVERVIEW:
 This learning unit will introduce you to the concepts of population and
sampling. This is where you try and figure out who the people are, or
which social artefacts, will be best suited to providing you with answers
that will help you meet your research aim. Often the number of people or
artefacts are too numerous to contact or analyse in one research project,
so you have to reduce this to a more manageable number. This is
sampling.
 There are different methods of reducing your population to a more
manageable number (sampling), each of which come with their own
considerations. This sampling choice is often influenced by decisions
made previously during the research process as both quantitative and
qualitative studies have their own selection of sampling methods.
 Once you have an understanding of where you will be getting your data,
decisions need to be made as to how you will be collecting the data from
those people or artefacts. There are several methods of doing this. This
learning unit will focus on the different types of experimental designs.
Each of these designs has advantages and disadvantages which need to
be taken into consideration. Choosing the right method for your research
aim is important as this will affect the type of data you collect as well as
the validity and reliability of the final analysis. You will explore these
methods related specifically to quantitative research projects, including
different types of surveys and the design of questionnaires.
 This learning unit also explores data collection methods related
specifically to qualitative research. Qualitative research tends to be
more explorative and is usually more relevant for education research
and as such the choice of which one to use should be carefully
considered. Often the aim of the research will guide this decision,
however, having an understanding of each will be beneficial as each has
its own advantages and disadvantages which will inform your final data
analysis.

4.  Often, research involves extremely large numbers of people or artefacts which have to be
reduced to a more manageable size. Choosing the right data collection method to achieve a
specific research aim is equally important as this will affect the type of data to be collected
as well as the validity and reliability of the final analysis. The ways in which this is done
is the main focus of this learning unit.
 In Learning Unit 4, you will be introduced to the concepts of population and sampling,
including different sampling and data collection methods, both of which depend on
whether quantitative or qualitative studies are involved. You will also explore the
advantages and disadvantages of specific data collection methods such as surveys and
questionnaires, content analysis, experiments and field research.

5. LO 1: Describe an appropriate
population as best suited to
the needs of a research study.
LO 2: Explain whether or not a
research study will make use
of either probability or non-
probability sampling methods.
LO 3: Justify the use of a
particular method of sampling
from the range of sampling
methods in relation to the
needs of a research study.
THEME 1:
POPULATION
AND
SAMPLING
IN AN
EDUCATION
SETTING

7. IN
RESEARCH,
WHAT IS
A POPULATI
ON?
 “A population is a complete set of persons or
objects that possess some common
characteristics that is of interest to the
researcher.”
 The population for a study usually is described
as being composed of two groups-
1. Target population
2. Accessible population

8. 1. We want to research whether more action shows are
aired on Channel Z than sitcoms. For this example, we
will use the following question: 'How many sitcoms
compared to action shows are aired on Channel Z?'
2. The first step is to decide what we need in order to get
the information that will answer this question. In this
case, we will have to turn to Channel Z and the
programmes aired on this channel. From this we can
deduce that we are studying social artefacts. We can
therefore say that our unit of analysis is social
artefacts, or television programmes.
3. We then need to determine what characteristics these
social artefacts will need to share in order to be a part
of the population of our study. Again, we return to our
research question. From our research question it is
clear that we are focusing on one particular television
channel - Channel Z. The question also specifically
states that we are only concerned with two specific
genres, namely sitcoms and action shows.
4. We can now define our population as any social
artefact that meets the requirements of being a
television programme belonging to either the sitcom or
action genre that is aired on Channel Z.
8

9. WHAT
ARE POPULAT
ION
PARAMETERS
?
 The shared characteristic and the number of people or social
artefacts in a population are referred to as the population
parameters of the study.
 It therefor refers to:
1. the nature (people or social artefacts),
2. size and
3. unique characteristics of the population.
 It is this that we use to define the population of our study.
 Once the population parameters have been set and we have
defined our populations appropriately, we need to distinguish
between the target population and the accessible population.
 The difference is that the target population is everyone or
everything that falls within the population parameters,
whereas the accessible population refers only top the section
of the population that we can actually include in our study.
 There will be times when the population is so large and
widespread that we will not be able to determine who all the
members of the population are. Only those that we can reach
will be our accessible population.

10. POPULATIO
N
DIFFER FR
OM AN
ACCESSIBL
E
POPULATIO
N?

11. HTTPS://SMALLBUSINESS.CHRON.COM/DIFFERENCE-BETWEEN-TARGET-POPULATION-EXPERIMENTALLY-ACCESSIBLE-POPULATION-
70637.HTML
Target Population
1. The target population is the group a researcher hopes to understand.
2. For example, suppose a company is launching a new product for senior citizens.
3. Analysing the target population - all senior citizens - could uncover insights that
allow the company to implement a variety of advertising campaigns suited to
different income levels and attitudes within that target population.
Accessible Population
1. The accessible population is the group that a researcher actually can measure.
2. Budgetary constraints, for example, often limit the number of consumers a
researcher can study, making the experimentally accessible population much
smaller than the target population.
3. Physical limitations also often force a researcher to study groups that are
smaller than the target population.
4. For example, interviewing every consumer distributed across a large region often
isn't feasible, meaning a researcher must select a smaller group for study.
11

12.  The target population which is also
called the universe is composed of
the entire group of people or objects
to which the researcher wishes to
generalise the findings of the study.
 The target population consists of
people or things that meet the
designated set of criteria of interest
to the researcher.
 It is aggregate of cases that confirm
to designated criteria and are also
accessible as subjects for study.
 By identifying the group from
which the study sample was
chosen, the investigator about
the conclusion of the
generalisability of research
findings.

14.  Population: Overall group
of interest to the researcher
 Sample: Set of individuals
selected from a population,
goal is to be representative
of a population
 Reasons to use a sample
rather than the whole
population: population is too
large, cost, feasibility
 Target population: Group
defined by research
interests
 Accessible population:
Subset of target population
that is accessible to
researcher

15. 1. Go back to the research question and
determine the nature of the
population by asking whether your
question will be answered best by
turning to people, groups,
organisations or social artefacts
(referred to as units of analysis).
2. Once you know the nature of the
population, determine the common
characteristic(s) between your units
of analysis.
3. You now have your population
parameters and can define the
population based on this information.
4. Now you can begin to distinguish
between your target and accessible
population.
15

17.  A subset of the population,
selected by either “probability”
or “non-probability” methods.
 If you have a “probability
sample” you simply know the
likelihood of any member of the
population being included (not
necessarily that it is “random.”)
 Here’s why…
1. A credible and
appropriate sampling
strategy will
strengthen your
research methodology.
2. Communicating a
critical awareness of
sampling when writing
your project or
dissertation will add
gravitas to your work.
17

18. How we choose our sample can
be important, depending on
the type of research we are
undertaking …

21. SAMPLE
Sample may be defined as representative unit of
target population which is to be worked upon
by researchers during their study.
Quantitative researchers often select samples
that will allow them to achieve stating
conclusion validity and generalise their result.
They therefore develop a sampling plan that
specifies in advance how study participants are
to selected and how many to include.
Qualitative researchers make sampling decision
during the course of data collection based on
information & theoretical need & typically do
not develop a formal sampling plan in advance.

22. Consideration Example
Target Population • General Characteristics
• Particular Characteristics
• Chinese business travelers
• Male, aged between 25-45
Accessible Population • Geographic / Time constraints • Business travellers flying from Shanghai
Pudong International Airport
• Travellers flying in May and June 2008
Inclusion Criteria • Specific criteria by which
participants will be selected
for the study
• Using Business Class lounges
Exclusion Criteria • Factors that will affect data
collection
• Ethical issues
• Frustrated delayed travellers.
• Non-English speaking
• Non-consenting in tape recording the
interview

24.  I want to generalise my findings
 I want the research to be repeatable
 I want to remove subjectivity and bias
 It’s important that the sample is
representative

25.  I want to focus on individual stories
and subjective experience
 I am interested in context and themes
 I am interested in ‘saturation’

33.  The concept of population
vs sample is an important
one, for every researcher to
comprehend.
 Understanding the
difference between a given
population and a sample is
easy.
 You must remember one
fundamental law of
statistics: A sample is
always a smaller group
(subset) within the
population.

34. HOW BIG S
HOULD A
SAMPLE
BE?

35. Sample size depends on:
 How much sampling error can be
tolerated—levels of precision
 Size of the population—sample size
matters with small populations
 Variation within the population with
respect to the characteristic of
interest—what you are investigating
 Smallest subgroup within the sample
for which estimates are needed
 Sample needs to be big enough to properly
estimate the smallest subgroup

37. REPRESEN
TATIVE
SAMPLE
 Quantitative research usually requires us to draw a representative
sample.
 A representative sample shares the characteristics of the larger
population (similar to the sample of blood).
 Their responses to your research should therefore be very closely
related to what the response would have been if the entire
population were included .
 For example, let us say we want to investigate people's opinions of a
new clothing store.
 The population for this research would be all the people who had
shopped at this store.
 The store has to date had 100 customers- 50 male and 50 female.
 If a sample of 20 people from this population is questioned, in order
for the sample to be representative of (similar to) the population, we
would need to question ten male customers and ten female
customers.
 If the sample is selected acceptably and is representative of the
population, we will be able to generalise the findings from our
research to the rest of the population.
 We will then be able to predict the possible opinions of all the other
people in the population, who were not included in the research.

38.  A representative sample is defined as a small quantity or a subset of
something larger, such that, it represents the same properties and
proportions as that of its larger population.
 A representative sample could be people or even chemical substance in
scientific studies, that can be taken to a laboratory and tested to
analyse the result of any particular chemical reaction.
 A representative sample allows the collected information to be
abstracted to a greater population. For most of market research and
psychological studies, it is unsuitable in terms of time, money and
resources to collect data on everyone, it is practically impossible to
collect data from each and every person especially, for a large population
such as an entire country.
 The good news is, “you don’t need to do it!”. What is more important
here is to get a good representative sample, so that the vast majority of
your time and energy will go into getting responses from a small group
of people who will represent a larger population.

39.  The extent to which we can generalise the findings to the rest of the population can be
measured using a calculation to determine the sampling error.
 The sampling error is an indication of how confident we are that a certain percentage of the
population would provide answers similar to that of the sample.
 The calculation to determine the sample error is not a complex one, but it is beyond the
scope of this particular chapter.
 Sampling error refers to differences between the sample and the population that exist only
because of the observations that happened to be selected for the sample.
 Increasing the sample size will reduce this type of error.
 There are 2 types of sampling errors:
1. Sample errors
2. Non-sample errors

40.  Error caused by the act of taking a
sample.
 They cause sample results to be
different from the results of census
 Differences between the sample and
the population that exist only because
of the observations that happened to
be selected for the sample.
 Statistical errors are sample error
 We have no control over this
 Not controlled by sample size
1. Non-response error - occurs when
units selected as part of the
sampling procedure do not respond
in whole or in part.
2. Response error – any systematic
bias that occurs during data
collections, analysis or
interpretation
 Respondent error, e.g. lying, forgetting
 Interviewer bias
 Recording errors
 Poorly designed questionnaires
 Measurement error

43. SAMPLI
NG
FRAME
 In order to draw a representative sample for a
quantitative study, you need a list of all the
people or objects in that particular population.
 We call this list our sampling frame.
 Examples of sampling frames are things such as a
telephone directory, a list of customers, tax
records, driver's license records, a mailing list and
so forth.
 The key in deciding on a sampling frame is that
the people or items on the list must match the
characteristics of our population.
 We would not use a telephone directory, for
example, if our population only included guests of
a specific hotel.
 The telephone directory would include people that
had not stayed at the hotel and would therefore
not be appropriate for the research.
 A guest list of the hotel would be more
relevant-this way, we could ensure that all the
people that form part of the sample have stayed at
the hotel.

45. • We start with the target population – all the people we want to know
something about.
• From the target population, we create the sampling frame.

46. • In a perfect world, our sampling frame is exactly like the target population.
• In the real world, this is rarely the case.
• Consider the example of veterans who suffer from PTSD. In that example, there are likely
some individuals who have not been diagnosed, and it is possible that some of those who
have been diagnosed with PTSD were misdiagnosed.
• It is possible that the sampling frame will not perfectly match the population because new
teachers are hired and others leave, and it can take time for all those changes to be logged in
all the relevant systems.

47. • From the sampling frame, we select our sample.
• The sample will always include individuals who exist on the sampling frame but
will rarely include all the individuals on the frame.
• The sample is the group of people who we invite to participate in our research
study.

48. 2ND HALF OF
THIS THEME

49. Once the population has
been identified, determine
what sampling method
will be most suited to our
research.
A number of choices are
available to identify the
most appropriate
sampling method to use.
Remember these key
points when choosing a
sampling method:
A sample is a subset of the
accessible population.
The sampling frame is a
list of the elements
included in the
population.
The final sample must
have the same relevant
characteristics as the
population in order to be
considered representative
of the population.

50. PROBABILITY
SAMPLING:
Quantitative
NON-
PROBABILITY
SAMPLING:
Qualitative
MIXED
SAMPLING
1. Simple Random sampling
2. Systematic sampling
3. Stratified sampling
4. Multi-cluster sampling
TYPES OF SAMPLING
1. Accidental sampling
2. Convenience sampling
3. Purposive sampling
4. Quota sampling
5. Snowball sampling
6. Volunteer sampling

51.  Probability sampling refers to whether or not each unit (whether an individual or social artefact) in the
population has an equal opportunity to be a part of the sample.
 If there are 150 000 people in our population, we need to make sure that each and every one of them
has the same chance of being included in the sample.
 This method is preferred and therefore often used in quantitative studies, because it removes human
bias from the sampling process by using methods that are random and systematic (following strict step-
by-step procedures).
 Human bias refers to researchers selecting a sample that does not reflect the population accurately.
 This can happen when we choose to draw a sample that favours some population parameters or
characteristics over others.
 If our population includes both males and females, but we want to focus more on women's opinions of a
particular topic, then we may choose to bias the sample by including more women in the sample than
men.
 However, if we want the sample to be truly representative of the population, systematic random
sampling will ensure that each element in the population has a fair chance of being selected as an
element in the sample.

52. PROBABIL
ITY
SAMPLING
LENDS
ITSELF TO
THOSE
RESEARCH
SITUATIO
NS WHERE
1.fits with the parameters
(shared characteristics) of
the research;
2.is drawn randomly from
the population;
3.requires little influence
from the researcher; and
4.leads to generalisable
findings.

53.  Non-probability sampling is used when it is nearly impossible to determine who the entire population is or when it is
difficult to gain access to the entire population.
 As explained previously, you might not always have access to a sampling frame that includes the entire population.
 For example, if we wanted to question recovering alcoholics on the effectiveness of a 12-step recovery programme, the
population will be hard to determine-the nature of the topic makes it a sensitive subject and getting a comprehensive
list of alcoholics in a particular area will be nearly impossible.
 Instead, we will carefully select our sample from an accessible population and through those who can recommend
other possible participants.
 The sample will still meet the population parameters for the study; however, the sample will be selected using the
researcher's judgement and the participants will therefore not be randomly selected from a list.
 Any inclusion in the sample will be based on coincidence or our ability to make contact with participants and not
necessarily on a random or systematic selection.
 These can be used when the findings of a study do not need to be generalised to the larger population, specifically in
exploratory and qualitative studies.

54. WHAT IS
THE
DIFFEREN
CE?
 A non-probability sample is different to one selected by means
of probability sampling, in that the elements in the
population will not all have an equal opportunity to form part
of the sample.
 However, the representativeness of the sample is not
considered important in non-probability sampling when
compared to probability sampling, especially in the case of
qualitative research.
 Rather, the focus is more on how many people we need to
interview or how many artefacts we need to analyse to allow
us to gain an in-depth understanding of the research problem
that we are exploring.
 If, for example, you interview students studying at a
particular university or college about their perceptions of the
value of higher education, you will find that, at some point,
your participants are no longer providing you with new
information and that everything that is being said has
already been said.
 This is referred to as data saturation.
 Thus, in qualitative research the emphasis is not so much on
ensuring that the sample size is big enough to be
representative of the entire population, but instead on
including enough participants in the sample so that the data
saturation point is reached.

55. ANOTHER
DIFFEREN
CE…
 The findings from non-probability sampling are often not
used to generalise results to the larger population.
 Moreover, the results for a non- probability sample is not
considered reliable in the same way that findings from a
probability sample would be.
 This is because there is greater opportunity for
researcher bias to influence the results.
 Since generalisation is not an aim of most qualitative
research studies, non-probability sampling methods are
most often used in qualitative studies.
 Non-probability sampling can be used when we want to
draw a sample:
1. that is in line with the parameters (shared
characteristics) of the research;
2. where not all individuals or social artefacts in the
population are easy to access or are known; and/or
3. where drawing a representative sample to generalise
results to a broader population is not the goal of the
study.

57. 1. SIMPLE
RANDOM
SAMPLING
 A simple random sample is the most basic type of sampling
method.
 It can be used when each element of the population has the
same and equal chance of being selected to be a part of the
sample.
 The sample is drawn following a precise procedure so as to
reduce the influence of bias.
 A popular example of a simple random sample is drawing
names from a hat.
 Let us assume the population of your study is small enough to
place each person's name in a hat.
 If you then need 50 people to form your sample, you could
randomly draw one name at a time out of the hat until you
have your sample of 50 people.
 By doing this, you are ensuring that everyone that forms part
of your population has had a fair chance of being included in
the sample.
 For larger populations that require a larger sample, numbers
can be allocated to each member of the population.
 This can often be done using computer software, which would
randomly select the sample for you.
 This method of sampling removes the chances of researcher
bias, as you will not be able to choose a sample that only
includes those elements that would provide answers favouring
the research question.

59. The gap, or period between successive elements is
random, uneven, has no particular pattern.

60. 2.
SYSTEMATI
C
SAMPLING
 In order to use the systematic sampling
method, each element in the population
needs to be numbered on the sampling
frame list.
 Each element of the sample will be
randomly chosen from this list using a
sampling interval.
 A sampling interval is the distance
between each element selected for the
sample.
 For example, if we need a sample size of
20 from a list of 100 names, the process
would be the following:

61.  1. We can work out our sampling
interval by dividing the number of elements
included in the population by the number of
elements to be included in the sample:
100/20 = 5. Our sampling interval is five.
 2. Next, we need to determine at which
number on the list to start. You can write
down the numbers one to ten each on a
separate piece of paper and then randomly
select a number. Let us say you select the
number three. You then start at the
element numbered three; the element listed
third on the list will be the first element in
our sample.
 3. You then need to count the same
number as your sample interval down the
list to get the next element in your sample.
If you start at 3, and count down five, the
next element in your sample will be the
element listed as 8, then 13, then 18, then
23 and so on until you have your sample of
20 elements.

63. Gaps between elements are equal and
Constant There is periodicity.

64. 3.
STRATIFIED
SAMPLING
 With stratified sampling, the first step is to
split the population into sub-units or strata.
 Strata (the singular form is stratum) are
groups of elements that share the same
characteristics within the same population.
 These strata will be decided based on
characteristics included in the population
parameters.
 Samples are drawn from each stratum using
either simple random sampling or systematic
sampling.
 This method can be used to ensure that the
final sample is representative of the
population in those situations where the
population has multiple characteristics that
are proportionately uneven.

66.  If we want to determine the popularity of a new nightclub, our population will include all the people
who have been to the club.
 The population, however, is split unevenly, with 60% of the nightclub-goers being women and 40%
being men.
 We therefore have at least two strata in this population: one stratum is all the women and the second
all the men from our population.
 The proportion will be 6:4; for every six women in the population there are four men.
 It is possible that men's and women's experiences of the club will differ and, as such, we will want to
make sure that both strata are represented proportionately.
 Our sample will therefore need to have the same ratio as the population in order to be considered
representative.
 As the two strata are based on gender, we will draw a larger sample from the female stratum than
from the male stratum.
 The final sample will match the population ratio and will have six women for every four men.

67. 4. MULTI-
STAGE
CLUSTER
SAMPLING
 Multi-stage cluster sampling is used
when the population for a study is
widespread (for example, across a
country) or the cost of reaching each
element of the sample is very high.
 If you want to look at, for example,
waiter satisfaction with wages in a
national restaurant chain, you will be
working with clusters, each separate
restaurant in the chain representing a
cluster.
 Here we would start by drawing a
sample from a broad category, thereby
creating a cluster of smaller categories.
 Another sample will then be drawn from
each of these smaller categories to get to
our final sample.

68.  An analysis of waiters' satisfaction with wages in a chain such as Spur would be a
very time-consuming and costly exercise if we tried to draw a sample from a list of
all the waiters working across the country.
 However, using multi-stage cluster sampling, we could do the following:
 We first create broad categories using each of the nine provinces of the country.
 We then draw a sample of restaurants from each province.
 Finally, we draw a random sample of waiters from each cluster (selected restaurant).
 This gradual narrowing of the population from one cluster to the next allows us to
obtain to a more manageable sample size.

69.  The size of the sample drawn from each cluster can be decided in one of two
ways:
Cluster 1:
 We can draw a sample from each cluster that is proportionately the same as
the cluster.
 Thus, if Restaurant A is larger than Restaurant B, the sample drawn from
Restaurant A will be proportionally larger than that of Restaurant B.
 We refer to this as probability proportionate to size sampling.
Cluster 2:
 The other option is disproportionate sampling and weighting.
 Here we may choose to have a larger sample size from one cluster than
from another, in order to include a sufficient number of elements.
 If Restaurant C (a Spur in a small rural town) is much smaller than
Restaurant D (a Spur in a big city), a proportionate sample might result in
only one or two waiters from Restaurant C being included in the sample.
 This will not help us get the answers we need, because the experience of
waiters working in rural areas could be very different from those of waiters
working in the city.
 We would thus draw a sample from Restaurant C that is proportionately
larger than what it is supposed to be, so that we can ensure the views of
waiters from rural areas are also incorporated. In this way, the waiters
from Restaurant C can be included in the research and provide a sample
that is likely to add more value to the research.

70. DESCRIPTION COST AND
DEGREE OF USE
ADVANTAGES DISADVANTAGES
1. Simple Random:
Researcher assigns each
member of the sampling frame
a number, then selects sample
units by a random method.
High cost, not
frequently used in
practice (except
random-digit
dialling)
• Only minimal advance
knowledge of
population needed;
• easy to analyse data
and compute error.
• Requires sample frame to work
from;
• does not use knowledge of
population that researcher may
have;
• larger errors for same sample size
than stratified sampling;
• respondents may be widely
dispersed, hence higher cost.
2. Systematic:
Researcher uses a natural
ordering or order of sampling
frame, selects an arbitrary
starting point, then selects
items at a pre-selected
interval.
Moderate cost,
moderately used
• Simple to draw
sample;
• easy to check.
• If sampling intervals are related to
a periodic ordering of the
population,
• may introduce increased
variability.
3. Stratified:
Researcher divides the
population into groups and
randomly selects sub-samples
from each group; variations
include proportional,
disproportional, and optimal
allocation of sub-sample sizes.
High cost,
moderately used.
• Assures
representation of all
groups in sample
• Characteristics of each
stratum can be
estimated, and
comparisons made
• Reduces variability of
sample size.
• Requires accurate information on
proportion in each stratum
• If stratified lists are not already
available, then can be costly to
prepare.

72. 1.
ACCIDENTA
L
SAMPLING
 This method of sampling does not use a sampling frame;
instead, the sample consists of elements that were included
purely because they happen to be in the right place at the
right time.
 Stopping people in a shopping mall is an example of
accidental sampling.
 Here, the people are included in the sample purely because
we were able to see them walking past and they were willing
to answer our questions.
 This non-probability sampling method is risky, as the results
obtained from the participants cannot be generalised to the
rest of the population.
 The reason for this is that some important elements of the
population may be left out, and the location could be biased
towards one particular segment of the population.
 Depending on the time of day and the day of the month, the
sample will include only certain segments of the population.
 For example, if you go to the mall in the morning on a
weekday, you are like to find mostly pensioners, students and
other individuals who are not full-time workers.
 This method is usually only used when a researcher wants to
pre-test questionnaires or during the conduction of a pilot
study on which a more comprehensive study will be based.

73. 2.
CONVENIEN
CE
SAMPLING
 Convenience sampling is also most often used
to pre-test questionnaires.
 The reason for this is that a sample gathered
using convenience sampling can be heavily
biased towards the social or professional
context of the researcher.
 As the name suggests, our sample consists
purely of elements that we know or that we are
able to get quick and easy access to.
 Where convenience sampling differs from
accidental sampling is that the people most
likely to be convenient are those that we
already know or have some form of contact
with.
 If we are doing research on the teaching styles
of lecturers, for example, a convenience sample
would be made up of the lecturers that we see
on a daily basis at university or college - they
are convenient because we know them and see
them every day.

75. 3.
PURPOSIVE
SAMPLING
 With purposive sampling, we purposefully choose the elements
that we wish to include in our sample, based on a set list of
characteristics.
 We would look at our population and our research question and
determine what characteristics from the population are important
for the research.
 We would then carefully select a sample from the population that
have these characteristics, and we would disregard those that do
not.
 Let us say that we are analysing films within two specific genres,
namely comedies and action films.
 Our population would include all films from the comedy and
action genres.
 In order to narrow our focus, we could choose to select only films
that fall within these genres and that were produced in South
Africa.
 We would then select those films that we need to make up our
sample, and disregard all films not from South Africa, even though
they are classified as comedies and action films.
 The advantage of this method of sampling is that we can ensure
that each element of our sample will assist with our research,
because each element fits with the population parameters of the
study.
 If an element does not fit, we can disregard it.

77. 4. QUOTA
SAMPLING
 Quota sampling is similar to purposive sampling in that we
purposefully choose our sample.
 Where quota sampling differs from purposive sampling is
how the sample is drawn to match the ratio of different
characteristics stipulated in the population parameters-we
make a list of the characteristics of the target population
and then allocate proportions to these characteristics.
 Quota sampling assists in making sure that the
characteristics stipulated in the population parameters are
represented proportionately in the final sample.
 Using our previous film genre example: if the number of
action films in the population is larger than that of the
comedy films, we would want this to be reflected in our
final sample.
 When choosing our sample, the ratio of action films to
comedy films will be the same as the ratio in the
population.
 Let us say action films outnumber comedy films by two to
one.
 We will then purposefully select one comedy film for every
two action films included in the sample.

79. 5.
SNOWBALL
SAMPLING
 This method of sampling is often used in qualitative research and, as with
the previous non-probability sampling methods, the results obtained from
such a sample cannot be generalised to the larger population.
 If you roll a snowball in snow, it collects more snow and gets bigger and
bigger as the snow accumulates.
 Snowball sampling, then, makes use of referrals to increase the sample
size.
 Participants in the study provide suggestions of others who also fit the
population parameters of the study, and who could and want to participate
in the research.
 We might be able to make contact with a few people who match the
population parameters of our study.
 After approaching these people to form part of our sample, we would ask
them to suggest names of others who may be interested in participating in
the research.
 We would continue to do this until we have reached the number required
for our sample.
 This method is often used when members of the population are difficult to
locate because they are not listed in databases or records.
 For example, if you wanted to gain an in-depth understanding of the lived
experiences of gangsters, you could make contact with one gangster that
you are aware of and ask him to put you in contact with another gangster.
 You could continue to do so until you have a big enough sample.

81. 6.
VOLUNTEE
R
SAMPLING
 Volunteer sampling, as the name suggests, is a sample put together
from people who volunteer to participate in the research.
 This method of sampling is not very reliable and tends to provide a
lot of erroneous research results.
 One reason for this is that people who volunteer to participate in
research often do so for a reason.
 An example of volunteer sampling is placing a questionnaire in a
magazine or newspaper and then requesting that people fill it out
and submit it.
 There is usually an incentive associated with this kind of research,
such as being entered into a competition to win a prize.
 The volunteers are therefore only motivated to participate in the
research because of the possibility of gaining something through
their participation.
 In other instances, people volunteer because they are unhappy about
something and want to voice their unhappiness.
 For example, convenience stores, fast food franchises, hotels and
restaurants often use short questionnaires to determine customer
satisfaction with their food or services.
 The only problem is that people only tend to fill these in when they
are displeased with something, and hardly ever when they are
satisfied with the food or service.
 In some instances, volunteers tend to provide what they believe is
the desired answer instead of what they truly think.
 Another problem with this method is that the people who volunteer
might not meet all the population parameters of the study.

82. DESCRIPTION COST AND
DEGREE OF USE
ADVANTAGES DISADVANTAGES
1. Convenience:
Researcher uses most convenient
or economical sample.
Very low cost,
extensively used.
• No need for list of
population.
• Variability and bias of estimates
cannot be measured or
controlled
• Generalising data beyond
sample inappropriate.
2. Purposive (Judgement):
An expert or experienced
researcher selects the sample to
fulfil a purpose, such as ensuring
all members have a certain
characteristic.
Moderate cost,
average use
• Useful for certain
types of forecasting
• Sample guaranteed
to meet a specific
purpose
• Bias due to experts' beliefs may
make sample unrepresentative
• Generalising data beyond
sample inappropriate.
3. Quota:
Researcher classifies population
by pertinent properties,
determines desired proportion of
sample from each class and fixes
quotas for each interviewer.
Moderate cost,
very extensively
used
• Introduces some
stratification of
population
• Requires no list of
population
• Introduces bias in researcher’s
classification of subjects
• Non-random selection within
classes means error from
population cannot be estimated
• Generalising data beyond
sample inappropriate.
4. Snowball:
Initial respondents are selected
by probability samples;
additional respondents are
obtained by referral from initial
respondents
Low cost, used in
special situations
• Useful in locating
members of rare
population.
• High bias because sample units
not independent
• Generalising data beyond
sample is inappropriate.

86.  Sampling methods are majorly divided into two
categories probability sampling and non-probability
sampling.
 In probability sampling, each member has a fixed,
known opportunity to belong to the sample, whereas
in non-probability sampling, there is no specific
probability of an individual to be a part of the sample.
 For a layman, these two concepts are same, but in
reality, they are different in the sense that
in probability sampling every member of the
population gets a fair chance of selection which is not
in the case with non-probability sampling.
 Other important differences between probability and
non-probability sampling are compiled below.

87. 1. Probability
Sampling
2. Non-probability
Sampling
87

89. 1. Budget. It is important to consider cost versus value, in that the sampling method
chosen will need to be cost effective while still providing valuable data. A large
sample might provide us with a lot of valuable data, but it will cost a lot of money to
get a team large enough to collect and analyse the data. The alternative is to choose
carefully a sampling method that will provide us with the same kind of data from a
smaller number of elements.
2. Time. Keep in mind that we need time to source our sample, contact them, collect the
data from them and then still analyse and write up our findings. We therefore need
to make sure that our sample size and sampling method are not too time consuming.
3. Resources. If we have a team of researchers, we will be able to take on a larger
sample than if we must manage the collection and analysis on our own. Access to
statistical programmes, pre-established questionnaires, computer equipment and
recording equipment will all play a part in the kind of sampling decisions we will
have to make.
4. Purpose. Some research is not designed to be generalisable to the larger population.
Exploratory research for the purposes of designing questionnaires or measurement
instruments, for example, does not need to be generalised beyond the sample.
5. Error allowance. When error control is not a main concern -as in the case of pilot
studies, for example - larger or more generalisable results are not required. In such
cases, sampling methods can be chosen according to the specific requirements.
89

91. SCENARIO 1:
 A researcher wants to gain a deeper
understanding of cultural differences in
humour by determining if South Africans
interpret humour in the comedy show
‘Anger Management’ in the same was as
Americans do.
 The researcher asks one American couple
and one South African couple to identify
eight other couples, each from amongst
their friends, forming a group of 36
participants in total.
 The participants are then divided into six
groups each consisting of three couples.
 Three of the groups are American and the
other three are South African.
 The participants are all aged between 28
and 32 years old and they are all married.
SCENARIO 2:
 Dr Kabanzi is conducting research into the
relationship between implementing a new
accounting computer system and employee
productivity.
 He selects all full-time employees who have
been with the company for more than five
years from a list of all employees assigned
to the finance department.
 He then numbers the 100 names on the list
and chooses every fifth name on the list to
form the sample.