Researchers use sampling techniques in situations where there are large populations to be tested
as, in most cases, testing the entire population is practically impossible and thus the need to use
sampling techniques to come up with a representative sample. Sampling is a technique
employed by a researcher to systematically select a relatively smaller number of representative
items or individuals (a subset) from a pre-defined population to serve as subjects (data source)
for observation or experimentation as per objectives of his or her (researcher) study. There are
many different types of sampling techniques but the essay is to focus on Simple random
sampling, Systematic sampling, Stratified sampling, Multi-stage sampling, Quota sampling,
Cluster sampling and Convenience sampling. The essay below is to give flesh to the sampling
techniques giving the pros and cons and the circumstances necessary for the use of the sampling
technique.
Simple random sampling is an unbiased surveying technique. Simple random sampling is a
basic type of sampling, since it can be a component of other more complex sampling methods.
The principle of simple random sampling is that every object has the same probability of being
chosen. Simple random sampling merely allows one to draw externally valid conclusions about
the entire population based on the sample. The entire process of sampling is done in a single
step with each subject selected independently of the other members of the population
(Gaganpreet Sharma 2017). Simple random sampling assumes that the units to be sampled are
included in a list, also termed a sampling frame. If the target population is defined as students
of 2017-18 at a specific school. It means only those students constitute the population who
study at the school during the mentioned period (M. H. Alvi 2016). This list should be
numbered in sequential order from one to the total number of units in the population. Because
it may be time-consuming and very expensive to make a list of the population, rapid surveys
feature a more complex sampling strategy that does not require a complete listing (Frerichs,
R.R. 2008). However simple random sampling is not efficient for large populations because of
its need of a complete list of all the members of the population and thus the need to use other
sampling techniques (Gaganpreet Sharma 2017).
In systematic sampling, the selection of the first unit determines the whole sample. This type
is called an every kith systematic sample. Unlike simple random sampling, there is not an equal
probability of every element being included. In systematic sampling, the elements are selected
at a regular interval. The interval may be in terms of time, space or order. For instance, element
appearing after every 30 minutes, or present at a distance of two meters, or every 5th element
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on a list therefore this regularity and uniformity in selection makes the sampling systematic
(M. H. Alvi 2016). The advantages of using systematic sampling are that it spreads the sample
more evenly over the population and easier to conduct than a simple random sample. However,
the process of selection can interact with a hidden periodic trait within the population. If the
sampling technique coincides with the periodicity of the trait, the sampling technique will no
longer be random and representativeness of the sample is compromised (Gaganpreet Sharma
2017). It may be very costly and time consuming especially in those cases when the participants
are widely spread geographically and difficult to approach and it needs a lot of efforts
especially for a large population. If the order of the list is biased in some way, systematic error
may occur (M. H. Alvi 2016).
Stratified sampling method is used when population is heterogeneous, that is every element of
the population does not match all the characteristics of the predefined criteria. Common
criterions used for stratification are gender, age, ethnicity, socioeconomic status. However, the
criterion vary greatly from investigation to investigation (M. H. Alvi 2016). Stratified sampling
is a method of sampling that involves the division of a population into smaller groups known
strata. In stratified random sampling, the strata are formed based on members shared attributes
or characteristics. A random sample from each stratum is taken in a number proportional to the
stratum’s size when compared to the population. These subsets of the strata are then pooled to
from a random sample. There are two techniques that are used to allocate sample from strata:
proportional allocation technique and equal allocation technique. Using proportional allocation
technique the sample size of a stratum is made proportional to the number of elements present
in the stratum. Using equal allocation technique same number of participants are drawn from
each stratum regardless of the number of elements in each stratum (M. H. Alvi 2016).
The aim of the stratified random sample is to reduce the potential for human bias in the
selection of cases to be included in the sample. As a result, the stratified random sample
provides a sample that is highly representative of the population being studied, assuming that
there is limited missing data. Since the units selected for inclusion in the sample are chosen
using probabilistic methods, stratified random sampling allows the researcher to make
generalizations (statistical inferences) from the sample to the population. This is a major
advantage because such generalizations are more likely to be considered to have external
validity (Gaganpreet Sharma 2017). For instance if an investigation is taking young adults into
account, so this population may need to be divided (of course, on the basis of what the
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investigate into sub groups like male young adults and female young adults, educated young
adults and uneducated young adults, high income young adults and low income young adults
etc. in this way each stratum is a different population (M. H. Alvi 2016).
However, stratified sampling is not useful when the population cannot be exhaustively
partitioned into disjoint subgroups. It would be misapplication of the technique to make
subgroups sample sizes proportional to the amount of data available from the subgroups, rather
than scaling sample sizes to subgroup sizes or to their variances, if known to vary significantly
(Gaganpreet Sharma 2017).
Multi-stage sampling is a more complex form of cluster sampling which contains two or more
stages in sample selection. In multi-stage sampling, large clusters of population are divided
into smaller clusters in several stages in order to make primary data collection more
manageable. It has to be acknowledged that multi-stage sampling is not as effective as true
random sampling; however, it addresses certain disadvantages associated with true random
sampling such as being overly expensive and time-consuming (John Dudovskly 2019). It can
be applied by choosing a sampling frame, numbering each group with a unique number and
selecting a small sample of relevant discrete groups.
Multi stage sampling is effective in primary data collection from geographically dispersed
population when face-to-face contact is required (e.g. semi-structured in-depth interviews). It
is cost-effective and time-effective and has high level of flexibility (John Dudovskiy 2019).
However, it is has a high level of subjectivity and research findings can never be 100%
representative of population. The presence of group-level information is required.
In quota sampling individuals are not randomly selected, instead they have to meet a number
of requirements and characteristics. The underlying reasoning behind quota sampling is that if
the sample effectively represents the population characteristics that have a greater correlation
with the study variable, this will also be correctly represented. There are a large number of
tasks behind quota sampling namely, census, probabilistic, multipurpose consumer
understanding and studies of issues such as water hardness (Julián Camarillo 2011). This type
of sampling method is used when population is heterogeneous that is every element of
population does not matches all the characteristics of the predefined criteria. Instead the
elements differ from one another on a characteristic. So the sub groups are formed that are
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homogenous i.e. all the elements within a group contains same kind of characteristic (M.H Alvi
2016). Researchers usually prefer non-probability sampling techniques such as convenience
sampling and quota sampling in situations where there are financial or time restrictions for
research. Also, in some cases where the speed of research is more precious than the precision
of the obtained results, this sampling method is relied upon (Brat.A 2019). This therefore shows
some of its shortfalls in terms of data efficiency because it does not represent the whole
population. It is essential to, firstly identify the variable which makes the target population
heterogeneous. On the basis of the identified variable sub groups are made, quota is set for each
sub group and then the sample is approached on the basis of set quota (M.H Alvi 2016)
Cluster sampling technique is particularly relevant when sampling frames are not readily
available or when the target population is widely dispersed geographically, making both service
provision and data collection costs relatively high. Typical clusters include hospitals, schools,
employment agencies, police areas, tribunals, etc. It is through these clusters that patients,
pupils, jobseekers or victims of crime are recruited for a given clinical trial or a social
experiment. It is also based on these clusters that inferences are made about the effect of a
treatment or intervention in the population of interest (Arnaud Vaganay 2016). The clusters
ought to be homogenous among them on the characteristic variable of the research. However
for being truly representative sample, the selected clusters must capture the heterogeneity of
population. For instance if in the selection of towns only small towns are selected leaving
behind the bigger towns, the sample is not going to be a true representative of the population
(M.H. Alvi 2016).
Cluster sampling bias is a type of sampling bias specific to cluster sampling. It occurs when
some clusters in a given territory are more likely to be sampled than others. It is related to but
distinct from subject sampling bias, which occurs when individuals sharing a specific
characteristic (e.g. a similar socio-economic background or health status) are oversampled.
Regardless of whether it occurs at cluster or subject level, sampling bias can be alleviated by
using probability sampling methods and larger samples. This can be difficult to achieve in
applied research, where limited resources and conflicting priorities often lead investigators to
make decisions that are ‘good enough’ rather than scientifically ‘optimal’. The prevalence of
these constraints suggests that sampling bias is common in applied research (Arnaud Vaganay
2016).
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Convenience Sampling is a non-probability or non-random sampling technique used to create
sample as per ease of access, readiness to be a part of the sample, availability at a given time
slot or any other practical specifications of a particular element. In convenience sampling, the
researcher chooses members merely on the basis of proximity and does not consider whether
they represent the entire population or not. Using this technique, they can observe habits,
opinions, and viewpoints in the easiest possible manner. Convenience sampling is the most
commonly used sampling technique as it’s extremely prompt, uncomplicated, economical and
also, members are readily approachable to be a part of the sample. It is used in situations where
the principal research has been done without any supplementary inputs. There are no criteria
that need to be considered to be a part of this sample and due to which it becomes extremely
simplified to include elements in this sample. Every element of the population is eligible to be
a part of this sample and is dependent on the proximity to the researcher to get included in the
sample (Adi Brat 2019).
Convenience sampling is also called as accidental sampling or opportunity sampling. The
researcher includes those participants who are easy or convenient to approach. The technique
is useful where target population is defined in terms of very broad category. For instance the
target population may be girls and boys, men and women, rich and poor (M.H Alvi 2016). This
therefore shows that it is not reliable as the researcher will not consider the insights of other
members of the population and thus create a bias.
In summation, sampling is a consideration in both qualitative and quantitative research. The
essay above has touched on sampling techniques including, simple random sampling,
systematic, stratified, multistage, quota, cluster and convenience sampling. These sampling
techniques are efficient but however they have their pros and cons but both of them share a
similar disadvantage which is that they do not represent the whole population and thus the
results might be biased resulting to inconveniences to the population not considered during the
research.
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REFERENCE
J.Camarillo (2O11), Quota Sampling TNS, Universitat Pompeu Fabra, Kantar WPP.
Brat.A (2019), Convenience Sampling: Definition, Methods and Examples, QuestionPro ; 800531-0228.
Vaganay.A (2016), Cluster Sampling Bias in Government-Sponsored Evaluations: A
Correlational Study of Employment and Welfare Pilots in England, Plos ONE 11(8): e0160652
Sharma.G (2017), Pros and cons of different sampling techniques, International Journal of
Applied Research; 3(7): 749-752.
Alvi.M. H. (2016, 70218), A Manual for Selecting Sampling Techniques in Research,
University of Karachi, Iqra University.
Dudovskiy.J (2019), Multistage Sampling, Research Methodology, Necessary Knowledge to
conduct a Business .
Frerichs, R.R. (2008), Simple Random Sampling, Rapid Surveys, Google Scholar.
Barreiro
P.L
&
Albandoz
J.P.
(2001).
Population
and
sample
Sampling
techniques,Management Mathematics for European Schools. Retrieved 5 March, 2016
McLeod, S. A. (2014). Sampling Methods. Retrieved 5 March, 2016.
Shi.J (2013), Reliability and Validity Assessment of Cluster Sampling on Multinomial
Sensitive Question by Monte Carlo Simulation, Communications in Computer and Information
Science book series (CCIS, volume 391).
Cochran, W.G. (1977), Sampling techniques 3rd edition, New York: Wiley Library.
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