Sampling
Sampling is an important step in the entire research process. Sampling involves
procedures used to select research participants. It simply means taking part of some
population to represent the whole population. Nearly every survey uses some form of
sampling.
Suppose the researcher has defined his research problem and has examined the
relevant literature to determine what theories and data are available to guide him. On
the basis of this knowledge the researcher has developed hypotheses to be tested, or
has stated research questions to guide the research. He has also operationally defined
his concepts and variables in the hypotheses to make this test possible. The next thing
the researcher has to do is to collect data from the population he wishes to investigate.
If the researcher could investigate every member in the population, he would surely
have answers to his research questions. But populations could be very large and for
practical reasons, it is usually impossible to study every element in the population.
(Kerlinger, 1973). It would be too costly, and many individuals or groups would not
be available for interview, or observation, or to complete questionnaires
(Descombe1998). The researcher resolves this problem by studying a sample and
generalising the findings from the sample to the population.
According to authoritative sources (Kerlinger, 1973; Babbie1999; Descombe, 1999),
this is the most efficient way to do research, because there are methods that allow
researchers to estimate characteristics of populations by measuring only a small
sample of population elements. The researcher has to ensure that the sample is
representative of the population from which it was selected otherwise, what he finds
in the sample may not be true for the population. Of course, there are times when a
researcher may want to study every element of some population. This is when the
population is small enough so that every element can be measured without much
additional cost and effort. It will also be the case where the researcher is not interested
in generalising to some larger group (Kerlinger, 1973; Descombe, 1999; Babbie,
1999).
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Some Terms Associated with Sampling
Population
In discussions of sampling, the term population and universe are often used
interchangeably. In this course I shall use the term population. In a layman’s
language, population is mainly used to describe people but in research terms it has a
wider meaning. Population refers to the entire group of people, events or
organisations, or things that the researcher wishes to investigate. (Descombe, 1999). If
the Librarian of the University of Ghana is interested in investigating use of the
library by students of the University, then all students of the University of Ghana will
be the population of the study. If a professor wishes to investigate the behaviour of
freshmen at lectures, his population will consist of all freshmen of the University.
However, if the professor is interested only in the behaviour of freshmen in his class,
then the freshmen in his will be his population. Thus the term population refers to all
members in the group that happen to be the focus of a study.
One goal of scholarly research is to describe the nature of a population. In some cases
this is achieved by studying an entire group. The process of examining every member
of a population is called a census. In many situations, however, the chance of
investigating an entire population is not feasible due to time and resource limitations.
The usual procedure in these instances is to select a sample from the population.
Sample
A sample is a subset of the population that is taken to represent that population. This
means that, some, but not all, members of the population would form the sample. If
1,000 students are selected from a population of 30,000, these 1,000 members would
form the sample for the study. By studying these 1,000 students, the researcher would
draw conclusions about the entire student population of 30,000 considered for the
study.
One important consideration in sampling is that the sample must be representative of
the population from which it is drawn. Being representative means to be typical of a
population, that is, to reflect the characteristics of the population (Kerlinger (1973).
From a research point of view, therefore, a representative sample means that the
sample has ‘approximately the characteristics of the population relevant to the
research in question (Kerlinger (1973). If for instance gender is the variable
(characteristic) relevant to the research, a representative sample will have
approximately the same proportions of men and women as the population. A sample
which is not representative of the population, regardless of its size, is inadequate for
testing purposes: the results cannot be generalised. (Kerlinger, 1973).
To generalise means to apply conclusions reached from studying the elements in a
sample to the population from which the sample was drawn. The researcher concludes
that the results of the study sample are the same as would have been if every member
of the entire population had been studied.
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Element and subject
A single member of a population is called an element, and a subject is a single
member of a sample. In the example above, the 1,000 members from the population
of 30,000 formed the sample for the study; each student in the sample is a subject and
each member of the population is an element.
Sampling frame
A list of all cases, objects, or groups in a population is called a sampling frame. Thus
the sampling frame contains the same number of units as the population of the study.
For example if a researcher wants to study the learning habits of current Level 400
mathematics students, the sampling frame would be the list of the names of all Level
400 mathematics students of the current year. Other examples of ready-made lists
which may potentially be used as sample frames are the payroll of an organisation, a
list of all students of a University, the electoral register and telephone directories.
Assuming that a sample is chosen according to proper guidelines and is representative
of the population, the results of the study using the sample can be generalised to the
population. Kerlinger (1973) has however, warned that generalising results must be
done with some caution because of the error that is inherent in all sample selection
methods.
Population Parameter and Sample Statistic
The population parameter is population characteristic you want to investigate, for
example, the average age of all Level 400 students or the average income of all Senior
Members of this University. A sample statistic is the finding of a survey on the basis
of information obtained from a sample, (for example the average age of Level 400
students calculated from information obtained from those who were selected to
provide information on their age); or the average income of Senior Members,
(calculated from the information obtained from those Senior Members who
participated in the study). The sample statistic then becomes the basis of estimating
the prevalence of a characteristic in the study population.
Sampling
The process by which samples are selected from a population is known as sampling.
The researcher selects a sufficient number of items from a population so that by
studying the sample, and understanding the properties or characteristics of the sample
subjects, he would be able to generalise the properties or the characteristics to the
population elements. All conclusions drawn about the sample being studied are
generalised to the population. In other words sample statistics are used as estimates of
the population parameters.
When to study the entire population
Ideally, it will be best to study every member of the population in any research project
so that the findings of the study can command a lot of respect. Since ideal conditions
are usually difficult to meet and still there is the desire to obtain results that are
respectable, it will be in place to consider the conditions under which a researcher
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should obtain the ideal, which is, studying the entire members of the population. The
following are some of the reasons why a researcher may study the entire population:

When the entire size of the population is small

When there is more time for the project

When the resources (human and material) available for the project
are adequate.

When the sole objective of the study is to make a complete count of
the population.
Why is it necessary to sample?
In research investigations involving several hundreds and even thousands of elements,
it would be practically impossible to collect data from, or to test or to examine every
element. Even if it were possible, it would be prohibitive in terms of time cost and
other human resources.
A sample may provide you with the needed information quickly. For example, you are
a Doctor and a disease has broken out in a village within your area of operation. The
disease is contagious and it kills within hours. Nobody knows what it is. You are
required to conduct quick tests to help save the situation. If you try a census of those
affected, they may be long dead when you arrive with your results. In such a case just
a few of those already infected could be used to provide the required information.
Studying a sample rather than the entire population is also sometimes likely to lead to
more reliable results, mostly because there will be less fatigue, and hence fewer errors
in collecting data, especially when elements involved are many in number.
In a few cases, it would also be impossible to use the entire population to know or test
something. Good examples of this occur in quality control. For example to test the
quality of a fuse to determine whether it is defective or not, it must be destroyed. To
obtain a census of the quality of a lorry load of fuses, you have to destroy all of them.
There will be no fuses to sell. This is contrary to the purpose served by quality-control
testing. In this case, only a sample should be used to assess the quality of the fuses.
Sample Selection Methods
Literature on research methods identifies two primary methods of selecting samples
from a population. These are probability sampling and non-probability sampling.
Each of these has several different techniques.
Probability sampling involves using selection techniques whereby the probability of
selecting each participant or element is known. These techniques rely on random
processes in the selection of the sample.
Examples of probability sampling techniques are simple random sampling,
stratified sampling, systematic sampling, and cluster sampling.
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Unlike probability sampling, in non-probability sampling, random processes are not
used in the selection of the sample, thus making it impossible to know the probability
of selecting a given participant or element from the population. As a result, it is more
difficult to claim that the sample is representative of the population, thus limiting the
ability of the researcher to generalise the results of the study. Non-probability
sampling techniques include purposive sampling, convenience sampling, quota
sampling and snowball sampling.
Probability / Random Sampling
As we have noted above, probability sampling is a sampling process that uses random
processes or chance procedures to select a sample. This is why it is also known as
random sampling. Probability sampling has one important characteristic, which is
that, every element in the population, more precisely the sampling frame, has a known
probability of being selected for the sample. For example if a sample of 10 students is
to be selected from a population of 50, each student will have 1 in 50 chance of being
selected. Furthermore, when random processes are used to select a sample it is
possible to estimate sampling error, or the chance variations that may occur in the
sampling process.
The term random in a layman’s sense usually means haphazard, as when an
interviewer picks out people as they come out from a shop. Those who look
approachable to the interviewer or those who do not seem to be in a hurry may be
chosen. This means the researcher has the tendency to be subjective. Besides, this
method has no sampling frame, therefore the chances of all those visiting the shop on
that particular day being selected are not known.
In research terms, however, the random selection of units in the population is carried
out according to a specified objective method such as giving each unit a number,
putting all the numbers in a box and picking out blindly one number at a time until the
required size of the sample is drawn. This method is popularly known as balloting or
the lottery method. As we shall see later there are more scientific ways to ballot or
pick samples.
Types of Probability / Sampling
There are four types of probability sampling methods, namely, simple random
sampling; systematic sampling; stratified sampling; and cluster sampling. Let’s
take a look at these one after the other.
Simple random sampling
A simple random sample is obtained when every individual or element in the
population has an equal chance of being selected, and the selection of one person does
not interfere with the selection chances of any other person. This process is
considered to be free from bias because no factor is present that can affect selection.
The random process leaves subject selection entirely to chance. (Moser and, Kalton,
1979 Kerlinger 1973; Babbie 1982).
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If the Head of Information Studies Department (HOD) is to select 30 out of 120 Level
400 students for an award, he may use different ways to obtain a random sample of 30
students. The HOD may use what is popularly known as the fishbowl technique,
(which is commonly referred to as balloting or the lottery method); or a table of
random numbers; or a computer- generated random numbers
Using the fishbowl technique
If the HOD chooses to use the fishbowl technique, he will do the following:

Write the names of all the 120 students, (elements of the population
which is the sampling frame), on slips of paper.

Place the slips of paper in a bow, box, hat, or similar container.

He will then mix up the slips of paper thoroughly, close his eyes, and
then dip his hand into the container and pick out a slip. The name of
the candidate is recorded. He continues this process until he selects
his sample of 30.
The question we need to ask at this point is what claim has this method of selection to
fairness? Notice that each time the HOD is about to pick a slip with his eyes closed,
only one student will be lucky to be selected, and all the students are competing on
equal footing for that chance. The same equal competition is repeated on further
picking of candidates until the required number is achieved. Mixing the slips
thoroughly ensures that no student’s slip is permanently on top or bottom, or side or
anywhere in such a manner as to give him or her advantage or a handicap.
This selection process will give the students not selected the feeling that the process
was fair and that they were not discriminated against, since they were given the same
chance to compete for the 30 awards. If the HOD were to use these students for a
study, the 30 selected, would constitute a random sample of the population.
The technique described above can be accomplished in two ways. If, after each name
is selected, it is put back into the container, the method is called random selection
with replacement. If the names are not replaced after they are picked, this is called
random selection without replacement.
Descombe (1999) has noted that there is not a lot of difference between the two
procedures when the population size is large, but sampling without replacement does
not strictly meet the definition of the random selection process. In the example above,
each student has 1 chance in 120 of being selected. If the names are not replaced, the
probability of each subsequent name being drawn increases. This means, for example,
that the second name to be picked will have a probability of 1 in 119, and the third, 1
in 118, respectively to be included in the sample. So, if a name is selected, it should
be replaced. If it comes up again, it should be put back into the container. Each
person in the population then continues to have the same opportunity, or percentage of
chance, to be selected.
It is important to point out that there is some controversy regarding which of the two
sampling methods is more appropriate. Do not worry about this at this level, since
authorities in the field acknowledge that where the population is large and only a
small proportion of it is to constitute the sample, (for example 10 per cent sample
from a population of 20,000) it will not matter which method is used because they
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will both result in samples that are quite similar (Baumgartner, 2002). Baumgartner
(2002) notes, however, that most researchers sample without replacement since an
individual cannot be used as a subject again after once being selected.
The method of sampling just described may be appropriate for small sampling frames.
With large sampling frames a table of random numbers or a computer package can
be used to draw samples. (Descombe,1999). These offer a more convenient and
sophisticated way to select a random sample.
Using a table of random numbers
Let us consider how a table of random numbers can be used to draw a simple
random sample. If you have understood the method just described above very well,
you can use a table of random numbers to select a random sample without much
difficulty.
Let me first begin by explaining what random numbers are. Suppose slips of paper,
each bearing a number from 1 to 120 are crumpled, thoroughly shuffled in a bag, and
then picked one after the other and listed. You will recognise that the numbers will
not be in any particular order whether the numbers are even, odd, small, or large. Such
a list of numbers which is not in any particular order is referred to as a Table of
Random Numbers. The numbers, being in random order, can be used for selecting
members of a sample. To do so, however, a more efficient method of generating
random numbers must be used than the over simplified method I have just described.
A computer can be programmed to generate a list of one-digit, two digits, or three or
four digits etc numbers in a random order. A list of random numbers can be found at
the back of any textbook on statistics. The table below is part of a table of three-digit
numbers in random order.
Table of Random Numbers
A
B
C
D
E
F
G
H
I
157
020
526
429
223
637
088
670
428
545
007
090
009
153
265
230
344
011
010
160
018
627
117
008
671
148
647
973
107
570
863
112
386
666
104
035
112
443
025
173
623
249
917
448
475
742
322
418
079
491
611
473
546
738
361
080
033
795
187
051
055
756
274
806
043
019
721
569
109
082
521
338
059
350
220
116
067
251
090
826
742
768
108
719
112
107
179
624
034
017
092
718
004
001
775
009
098
066
584
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310
381
110
071
041
751
744
144
065
082
478
537
055
692
095
322
489
298
119
210
300
109
320
350
280
390
114
352
181
404
232
137
005
194
862
532
048
633
051
871
329
558
015
567
996
243
392
755
049
115
555
118
687
687
012
081
339
362
264
105
044
230
536
732
360
016
027
003
579
932
002
034
097
050
268
108
020
529
066
543
438
070
150
190
099
200
210
069
410
230
201
062
290
063
340
360
003
043
490
315
118
403
480
102
320
316
001
209
260
120
150
170
084
210
300
041
030
Let’s go back to our previous example and see how the HOD would use a table of
random numbers to select the sample of 30 out of a population of 120 students. The
HOD will first of all assign a serial number to every Level 400 student from 1 to 120.
Candidate 1 becomes 001; candidate 2 is numbered 002; candidate 8 is numbered 008;
candidate 48 is numbered 048 and so on. He will then close his eyes, open a page of a
table of random numbers, place his finger at any point. The number on which the
finger has been placed is recorded as the first member of the sample. To select the
other members of the sample, the HOD will the go down the list of numbers,
recording the numbers which fall within 001 and 120 until the 30 members of the
sample have been selected.
Since the numbers are randomly listed, it would not matter if the HOD moved down
the table, or up the table, or from left to right or from right to left. If the HOD decides
to go down in the table from a random starting point of 010 until a sample of 30 has
been drawn, the sample will include students numbered 010; 112; 059; 092; and so
on.
It is possible for the HOD to meet the same number a second time. If this happens he
must ignore that number as he needs different cases. You may have noticed that this
is like the case of sampling without replacement that is, not putting the unit back into
the sampling frame. Alternatively the HOD might select a number which is outside
the range of those in his sampling frame. If he selects say 150 which is beyond 120 he
will simply have to ignore it and continue reading off numbers until he reaches his
sample size.
Going by this procedure, a researcher can select a set of random numbers for different
samples which are unlikely to be the same. This is because the number selected first
at random will dictate the selection of the others to follow. What this means in the
case of our example is that different samples of 30 students selected by the HOD are
likely to be different.
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For many research projects, the two techniques of random sampling described will
enable the researcher to obtain a representative sample for study. But there are
occasions in which representativeness is not necessarily guaranteed by simple random
sampling. Moser and Kalton (1979). I will discuss this later under stratified sampling.
Systematic Sampling
Simple random sampling is similar in some way to a procedure called systematic
sampling, in which every nth subject or unit is selected from a population.
Systematic sampling involves selecting units of the sample at regular intervals from
the sampling frame. To do this the researcher must follow this procedure:

Number the units - the population from 1 – N

Decide on the n, (sample size), that is required

Select an interval size k = N/n

randomly select any number between 1 and k

finally take every kth unit
Going back to our example, the HOD has a population that has N=120 people in it
and that he wants to take a sample of n=30.To use systematic sampling, the HOD will
list the units in the population in a random order. The sampling interval will be
120/30 = 4
In this case the interval size, k = 4
Now the HOD would select randomly any number between 1 and 4. Imagine that the
HOD selected 3. Now, to select the sample, he would start with the 3rd unit in the list
and take every k-th unit (every 4th unit because k = 4). With this start the HOD would
be sampling units 3 (David);7 (Dorcas); 11(Anna);15 (Agnes);19 (Frempong); and
so on to 120 and he would come up with 30 students in his sample. (See Table 3.2).
Systematic Sampling
1 Jerome
2 Felix
3 David
4 Caroline
5 William
6 Simon
7 Dorcas
8 Thomas
9 Boateng
10 Dorcas
11 Anna
12 Ebenezer
13 Daniel
14 Mike
15 Agnes
A
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Linda
Irene
Frank
Frempong
Micheal
Anthony
Joana
Eugenia
Martha
Sandra
Milicent
Jeannette
Emelia
Racheal
Humphrey
B
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
C
Lawrence 46
Vera
47
Golda
48
Augustine 49
Peal
50
Belinda
51
Stephen
52
Serwaa
53
Spurgeon 54
Joy
55
Charles
56
Judith
57
Rita
58
Daniel
59
Belinda
60
D
Worlase
61
Maame
62
Felicia
63
Naadu
64
Obesebea 65
Rabiatu
66
Florence
67
Henri
68
Eugenia
69
Francis
70
Freedom
71
Micheal
72
Pascal
73
Eric
74
Eugene
75
E
Selasi
76 Alex
Rhodaline 77 Alhassan
Gloria
78 Amofah
Aaron
79 Anastasia
Abdallah 80 Andrew
Abdul
81 Andy
Abigail
82 Claudia
Addai
83 Ansah
Adonis
84 Atta
Adubea
85 Augustine
Adwoa
86 Ayatulah
Agyapong 87 Baba
Agyekum
88 Babara
Akuorkor
89 Beatrice
Albert
90 Benjamin
F
91 Bernard
92 Bernice
93 Bessie
94 Bismark
95 Brandon
96 Brenda
97 Bright
98 Bryan
99 Caios
100 Catherine
101 Cephas
102 Charles
103 Christabel
104 Christiana
105 Colins
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106 Courage
107 Cynthia
108 Daniel
109 Daniella
110 Samuel
111 David
112 Dawuni
113 Delphina
114 Dennis
115 Diana
116 Divine
117 Dominic
118 Dorothy
119 Drumond
120 Douglas
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It is important to note that members of the sample are always determined by where the
random start begins from. In the example above if the systematic selection had started
with 2 (Felix), the sample would have been different.
In order for systematic sampling to work, it is essential to arrange the units in the
population in random order. Systematic sampling is fairly simple to do and is widely
used for its convenience and time efficiency. In almost all sampling situations,
systematic random sampling yields what is essentially a simple random sample
(Descombe,1998).
Stratified Random Sampling
Stratified sampling involves dividing the population into homogeneous groups, and
then conducting a simple random sampling in each group.
First of all, elements in the population (that is in the sampling frame) are distinguished
according to their value on some relevant characteristic such as army rank:(generals,
captains, privates etc) or gender: (male , female) or socio-economic status: (upper,
middle and lower class). These characteristics form the sampling strata. Each
stratum is homogenous in the sense that they portray similar characteristics or they
are identified by similar properties. In a heterogeneous population characteristics of
elements differ widely in those populations.
Next, elements are sampled randomly from within these strata: so many generals, so
many captains, etc. In order to use this method more information is required before
sampling than is the case with simple random sampling. Each element must belong to
one and only one stratum.
Stratified samples can be proportional or non-proportional. In proportional
stratified sampling, the number of elements sampled in each group is proportional to
its representation in the population.
The HOD is aware that the population comprises males and females. Assuming there
are 40 females and 80 males. The HOD needs a stratified sample of 30 students. If the
HOD draws a simple random sample, he may end up with somewhat
disproportionate numbers from each group. In this situation the sample may
comprise say 25 males and 5 females. This may occur purely by chance. But
considering the proportion of males and females in the population we can conclude
that the sample is dominated by males; females are underrepresented. The sampling
has resulted in what is called disproportionate random sampling. This scenario
brings to mind what I said early on that simple random sampling does not always
yield representative samples.
To correct this situation the HOD might, stratify the population along gender lines
and pick a proportional sample. That is, members represented in the sample from
each stratum of the population will be proportionate to the total number of elements
in the respective strata. This would mean that the HOD would sample 10 females and
20 males. This type of sampling is called proportionate stratified random
sampling.
Let’s see the steps involved in selecting a proportionate stratified sample.
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
Identify all elements or sampling units in the sampling population

Decide on the different strata (k) into which you want to stratify the
population

Place each element into the appropriate stratum

Number every element in each stratum separately

Decide on the total sample size (n)

Determine the proportion (p) of each stratum in the study population
= (Elements in the stratum divided by the total population size)

Determine the number of elements to be selected from each stratum
(sample size(n) x (p)

Select the required number of elements from each stratum by
simple random sample technique or systematic sampling technique.
Going by our example, this is how the HOD arrived at the sample of 10 females and
20 males:
Population
120 students
Number of strata =2
(male and female)
Sample size (n)
= 30
Proportion of males in population = 80 / 120 = 2/3
Proportion of females in population = 40 /120 = 1/3
No. of males to be selected = 2/3 x 30 = 19.99 (20 Males)
No. of females to be selected = 1/3 x 30 = 9.99 10Females)
Proportional stratified sampling is used when the researcher wants to generalise the
findings to the population as a whole In this case, the goal is to produce a sample that
is a representative of the population as possible. To the extent that we have included
important subgroups in the same proportions as in the population, we have guaranteed
that our sample is similar to the population, at least on the stratified variables.
(Baumgratner, 2002).
In non-proportional stratified sampling an equal number of elements are selected
for each group. This means that elements are selected in numbers that do not reflect
their proportions in the population. In our example, the HOD may select a nonproportional sample by picking 15 males and 15 females. In this case males are
underrepresented in the sample given that there are 80 males as against 40 females in
the population.
There are times when the researcher will want to use non-proportional sampling. One
such situation is when the researcher’s purpose is not to generalise to the population
as a whole but, rather, to make comparisons between subgroups. Another situation
calling for the use of non-proportional sampling is when the researcher wants to
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ensure that representatives of an important but very small subgroup within a
population are included in the sample (Baumgartner, 2002).
Cluster Sampling
Resources need to be taken seriously when it comes to the selection of samples.
Identifying units to be included, contacting relevant respondents and travelling to
locations can all entail considerable time and expense. The researcher, therefore, has
to weigh the advantages of a purely random selection against the savings to be made
by using alternative approaches. Some techniques can be cost effective without
compromising the principles of random selection and the laws of probability.
(Descombe, 1998).
The most common alternative, and one used frequently for large-scale surveys, is
cluster sampling. The logic behind it is that, it is possible to get a good enough
sample by focusing on naturally occurring clusters of the particular thing that the
researcher wishes to study. By focusing on such clusters the researcher can save a
great deal of time and money that would have otherwise been spent on travelling to
and fro visiting research sites scattered throughout the length and breadth of a very
wide geographical area. The selection of clusters for research follows the principles of
probability sampling as we have already discussed. The aim is to get a representative
cluster, and the means of getting it rely on random choices of stratified sampling.
This method, involves multiple stages in the selection of a sample. The population is
divided into clusters or units and then into sub-units. At each level, the units and subunits are randomly selected.
Let’s consider a countrywide study of Junior High School students’ attitudes toward
computer selection of successful candidates into Senior High Schools. The researcher
may use cluster sampling to obtain results which will represent the views of JHS
students countrywide.
Knowing that the country is already divided into ten regions, the researcher would
select this sample in stages as follows:
Stage 1
Randomly select say three regions: ( Eastern, Ashanti and
Northern) to represent the whole county.
Stage 2
Randomly select some Districts in each region
Stage 3
Randomly select towns in each District
Stage 4
Randomly select a number of schools from each town
Stage 5
Randomly select individuals from the appropriate class and ask
them to complete a questionnaire.
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Non-probability Sampling
Types of Non-Probability Sampling
Non-probability sampling designs include convenience sampling, purposeful
sampling, quota sampling and snowball sampling.
Convenience sampling
This is considered the weakest form of sampling because it does nothing to control
bias. In this procedure, participants are recruited as they become available or because
they happen to be convenient for the researcher. Such samples are often limited to
personal contacts of the researchers or to people who happen to be available at
meetings or in organisations or in a particular place and time.
The Purposive sample
Instead of obtaining information from those who are most conveniently available, it
might sometimes become necessary to obtain information from specific targets, that
is, specific types of people who will be able to provide the desired information, either
because they are the only ones who can provide that information, or because they
conform to some criteria set by the researcher.
A particular survey may target individuals who are particularly knowledgeable about
the issue under investigation. It may involve studying the entire population of some
limited group or a sub-set of a population. In this procedure, the researcher uses his
judgement and knowledge of the field to identify persons whom he considers to be
leaders and experts in this area. One of the first things the researcher is likely to do is
to verify that the respondent does in fact meet the criteria for being in the sample.
Consider a researcher who wants to investigate the management styles of women
managers. The only people who can give firsthand information are women managers
and important top level executives in work organisations. Having themselves gone
through the experiences and processes, they might be expected to have expert
knowledge, and perhaps be able to provide good data or information to the researcher.
Purposive sampling can be useful for situations where the researcher needs to reach a
target sample quickly and where proportional sampling is not the primary concern.
With a purposive sample the researcher is likely to get the opinions of his target
population.
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Quota Sampling
In quota sampling, you select people non-randomly according to some fixed quota. It
is a form of proportionate stratified sampling in which a predetermined proportion of
people are sampled from different groups but on a convenience basis. In quota
sampling you want to represent the major characteristics of the population by
sampling a proportional amount of each. For instance, if you have a population of 405
women and 605 men, and that you want a total sample size of 100. You will continue
sampling until you get these percentages and then stop. So if you get the 40 women
for your sample, but not the 60 men you will continue to sample men but even if
legitimate women come along you have already met your quota for women.
The problem here is that even when the researcher knows that a quota sample is
representative of the particular characteristics for which quotas have been set, he has
no way of knowing if the sample is representative in terms of any other
characteristics.
Snowball sampling
With this approach the researcher initially contacts a few potential subjects and then
asks them whether they know anybody with the same characteristics that he is looking
for. For example, if the researcher wants to interview a sample of vegetarians or
people who support particular political party etc., his initial contact may well have
knowledge of the others. So in snowball sampling the researcher identifies one
member of the population and then asks that member to identify others of the same
characteristics in the population and so on.
Snowball sampling is useful for hard-to-reach or hard-to-identity populations for
which there is no sampling frame but the members of which are somewhat
interconnected (at least some members of the population know one another). It can be
used to sample members of such groups as drug dealers, prostitutes, practicing
criminals, participants in alcoholics, anonymous groups, gang leaders, informal
organisational leaders etc. (Descombe,1998)
Although this method would hardly lead to representative samples, there are times
when it may be the best method available.
A team of researchers want to determine the attitudes of students about recreational
services available in the student union on campus. The team stops the first 100
students it meets on a street in the middle of the campus and asks questions about the
union of each of these students. What are the possible ways that this sample might be
biased?
Sample Size
It is generally accepted that the quality of a sample depends on its size and the way it
is selected. (Kerlinger, 1973;Babbie,1999; Descombe.1999). Samples will be
representative of the population if they are relatively large and selected through
probability sampling. But how large a sample must be to be representative of the
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population, or to provide the desired level of confidence in the results is a question
which remains unanswered.
According some authoritative sources (Kerlinger,1973; Babbie, 1999, Descombe,
1998) there is no simple answer to this question. Again while literature on sampling
provides information concerning mathematical and statistical techniques which are
used to calculate sampling size, no single sample size formula or method is available
for every research (Descombe, 1999). Besides the use of mathematical and statistical
techniques, there are factors which researchers need to consider when determining
sample size. (Descombe, 1998). Though these principles are not based on
mathematical or statistical theory, they provide a useful starting point for researchers.
Factors to Consider When Determining Sample Size
Cost and time
Sample size is almost invariably controlled by cost and time. Although researchers
may wish to use a large sample for a survey, the economics of such a sample are
usually restrictive. Research at any level is very expensive, and these costs have great
influence on a project.
The general rule is to use as large a sample as possible within the economic
constraints of the study. If a small sample is forced on a researcher, the results must
be interpreted accordingly, that is, with caution regarding generalisation. (Descombe,
1999).
Likely response rate
Descombe (1998) has noted that a survey rarely achieves a response from every
contact. Especially when using postal questionnaires and the like, the rate of response
from those contacted, is likely to be very low. As far as sample size is concerned, the
important thing for the researcher to consider is that the number in the original sample
may not equal the number of responses that are finally obtained. The researcher needs
to predict the kind of response rate he or she is likely to achieve, based on the kind of
survey being done, and build into the sample size an allowance for non-responses. For
instance if the researcher wants to use a sample of say 100 people for research and is
using a postal questionnaire survey for which a response rate of 30 per cent is
anticipated, the original sample size needs to be 334.
Agreeing with Descombe, Fraenkel and Wallen(2000 ) advise that researchers should
always select a larger sample than is actually required for a study, since non-response
must be compensated for. They note that subjects drop out of research studies for one
reason or another, and allowances must be made for this in planning the sample
selection.
Heterogeneous population
If a variable of interest to the researcher varies widely in a population it is advisable to
pick a higher than lower percentage sample of the population.
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The accuracy of the results
Any sample, by its very nature, might produce results which are different from the
‘true’ results based on a survey of the total population. Inevitably, there is an element
of luck in terms of who gets included in the sample and who gets excluded, and this
can affect the accuracy of the findings which emerge from the sample. Two different
samples of 100 people, chosen from the same population and using the same basic
method, will produce results that are likely to be slightly different. (Freankel and
Wallen, 2000).
It is generally acknowledged that, the larger the sample used the better. The larger a
sample becomes, the more representative of the population it becomes and so the
more reliable and valid the results based on it will become. (Babbie, 1999). This
means for instance that that a 50% sample is better than a 30% sample, which in turn
is better than a 10%. By this principle, the researcher should go for the higher sample
percentages for, in doing so, he is approaching the 100% sample size.
It has been pointed out, however, that a large unrepresentative sample is as
meaningless as a small unrepresentative sample, so researchers should not consider
numbers alone. Quality is always more important in sample selection than mere size.
(Babbie, 1999).
Careful planning
A well selected random sample, does yield results whose amount of error can be
reliably estimated through statistical techniques, and so can be as useful, as those of
larger samples whose members were not properly randomly selected . By this
principle, the researcher should exercise a great deal of patience and effort in planning
and choosing the members of his sample, as well as in the choice of data collecting
instruments. Where this has been done, what seems to have been lost through
studying a low percentage of the population can be regained through very good and
systematic data collection procedures (Descombe, 1999)
There are also certain practices among social researchers that the beginner can adopt.
One such practice suggests that if the population is a few hundreds, a 40% or more
sample will do; if many hundreds a 20% sample will do; if a few thousands a 10%
sample will do; and if several thousands a 5% or less sample will do. (Alreck and
Seatle, 1985).
Learning form others
Consulting the work of other researchers provides a base from which to start. If a
survey is planned and similar research indicates that a representative sample of 400
has been used regularly with reliable results, a sample larger than 400 may be
unnecessary.
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