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ISSN Print: 2394-7500

ISSN Online: 2394-5869

Impact Factor: 5.2

IJAR 2017; 3(7): 749-752

www.allresearchjournal.com

Received: 23-05-2017

Accepted: 24-06-2017

Gaganpreet Sharma

Research Scholar, Department

of Physical Education, Lovely

Professional University,

Phagwara, Punjab, India

Correspondence

Gaganpreet Sharma

Research Scholar, Department

of Physical Education, Lovely

Professional University,

Phagwara, Punjab, India

Pros and cons of different sampling techniques

Gaganpreet Sharma

Abstract

In the field of research different sampling technique are used for different fields. It is very essential to

choose the adequate technique of sampling. In this paper first we clarify the proper meaning of

sampling. Further we discus about the different techniques and types of sampling. We mainly

concentrate on two types of probability and non- probability and their sub categories. Further we discus

about the pros and cons of these techniques. Pros are the primary positive aspect of an idea process or

thing. Cones are the primary negative aspects. It is very necessary to choose the write sampling

technique for a specific research work. Before we choose the sampling technique it is necessary to

know about the ‘Pros’ and ‘Cons’ of sampling technique. If the researcher know about the ‘Pros’ and

‘Cons’ he/she will select the adequate technique of sampling for his research work.

Keywords: Sampling, Pros, Cons.

Introduction

Pros and Cons

“Pros” are the primary positive aspects of an idea, process or thing; “Cons” are the primary

negative aspects. The term Pros and Cons means both the primary positive and negative

aspects of an idea, process or thing and is often used to clarify or decide whether that idea,

process or thing is mainly positive or mainly negative.

Sampling

Sampling is a technique (procedure or device) 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 study. For example, if, by using some systematic device, you

pick up a group of 100 undergraduates from out of a total of 1500 on the rolls of a college for

testing their physical fitness, you have selected a desired sample from a particular

population. Researchers usually use sampling for it is impossible to be testing every single

individual in the population. Although it is a subset, it is representative of the population and

suitable for research in terms of cost, convenience and time. Still, every researcher must keep

in mind that the ideal scenario is to test all the individuals to obtain reliable, valid and

accurate results. If testing all the individuals is impossible, that is the only time we rely on

sampling techniques.

True to the science of research and statistics, the sampling procedures must be carried out in

consideration of several important factors such as (a) population variance, (b) size of the

universe or population, (c) objectives of the study, (d) precision in results desired, (e) nature

of the universe i.e. homogeneity or heterogeneity in the constituent units, (f) financial

implications of the study, (g) nature and objectives of the investigation, (h)techniques of the

sampling employed, (i) accuracy needed in making inference about the population being

studied, and so on.

Types of Sampling Techniques

1. Probability Sampling: - Probability sampling is any sampling scheme in which the

probability of choosing each individual is the same (or at least known, so it can be

readjusted mathematically). These are also called random sampling. They require more

work, but are much more accurate.

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2. Non-Probability Sampling: - Non-probability

sampling technique is totally based on judgement.

Probability Sampling Non-Probability Sampling

Simple Random Sampling Quota Sampling

Systematic Sampling Purposive Sampling

Stratified Sampling Self-Selection Sampling

Cluster Sampling Snowball Sampling

Probability Sampling

1. Simple Random Sampling

In this technique, each member of the population has an

equal chance of being selected as subject. The entire process

of sampling is done in a single step with each subject

selected independently of the other members of the

population.

Pros of Simple Random Sampling

 One of the best things about simple random sampling is

the ease of assembling the sample. It is also considered

as a fair way of selecting a sample from a given

population since every member is given equal

opportunities of being selected.

 Another key feature of simple random sampling is its

representativeness of the population. Theoretically, the

only thing that can compromise its representativeness is

luck. If the sample is not representative of the

population, the random variation is called sampling

error.

 An unbiased random selection and a representative

sample are important is drawing conclusions from the

results of a study. Remember that one of the goals of

research is to be able to make conclusions pertaining to

the population from the results obtained from a sample.

Due to the representativeness of a sample obtained by

simple random sampling, it is reasonable to make

generalizations from the results of the sample back to

the population.

Cons of Simple Random Sampling

One of the most obvious limitations of simple random

sampling method is its need of a complete list of all the

members of the population. Please keep in mind that the list

of the population must be complete and up-to-date. This list

is usually not available for large populations. In cases as

such, it is wiser to use other sampling technique.

2. Systematic Sampling

Suppose that the N units in the population are numbered 1 to

N in some order. To select a sample on N units, we take a

unit at random from the first K units and every kith unit

thereafter. For instance, if K is 15 and if the first unit drawn

is number 13, the subsequent units are numbers 28, 43, 58

and so on. The selection of the first unit determines the

whole sample. This type is called an every kith systematic

sample.

Pros of Systematic Sampling

 Spreads the sample more evenly over the population.

 Easier to conduct than a simple random sample.

Cons of Systematic Sampling

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.

3. Stratified Sampling

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.

Pros of Stratified Sampling

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 us with 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 us to make generalizations (i.e. 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.

Cons of Stratified Sampling

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 e.g. by means of an F test). Date representing

each subgroup is taken to be of equal importance if

suspected variation among them warrants stratified

sampling. If, on the other hand, the very variances vary so

much, among subgroups that the data need to be stratified

by variance, there is no way to make the subgroup sample

sizes proportional (at the same time) to the subgroups sizes

with in the total population. (What is the most efficient way

to partition sampling resources among groups that vary in

both their means and their variances?

Cluster Sampling or Multi-Stage Sampling

The naturally occurring groups are selected as samples in

cluster sampling. All the other probabilistic sampling

methods (like simple random sampling, stratified sampling)

require sampling frames of all the sampling units, but cluster

sampling does not require that. Once the clusters are

selected, they are compiled into frames. Now, various

probabilistic researches and observations are performed on

these frames and require conclusions are drawn.

Pros of Cluster Sampling

 Economy: - The two major concerns of expenditure

when it comes to sampling are travelling and listing.

They are greatly reduced when it comes to cluster

sampling. For example: Compiling research

information about every house hold in the city would be

a very difficult, whereas compiling information about

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various blocks of the city will be easier. Here travelling

as well as listing efforts will be greatly reduced.

 Reduced Variability: - When you considering the

estimates by any other method of probabilistic

sampling, reduced variability in results are observed.

This may not be an ideal situation every time. Increased

variability in results is observed in cluster sampling.

 Feasibility: - Again, as I mentioned before, cluster

sampling is such a method of probabilistic sampling

that takes into account large populations. Since these

groups are so large, devploying any other sampling

technique would be very difficult task. Cluster sampling

is very feasible when you are dealing with large

population.

Cons of Cluster Sampling

 Biased Sampling: - If the group in population that is

chosen as a cluster sample has a biased opinion then the

entire population is inferred to have the same opinion.

This may not be the actual case. This is a major

disadvantage as far as cluster sampling is concerned.

 Sampling Errors: - The other probabilistic methods

give less error than cluster sampling. For this reason,

cluster sampling is discouraged for beginners.

Non-Probability Sampling

1. Quota Sampling

With proportional quota sampling, the aim is to end up with

a sample where the strata (groups) being studied (e.g. males

vs. females students) are proportional to the population

being studied. If we were to examine the differences in male

and female students.

Pros of Quota Sampling

Quota sampling is particularly useful when you are unable

to obtain a probability sample, but you are still trying to

create a sample that is as representative as possible of the

population being studied. In this respect, it is the non-

probability based equivalent of the stratified random sample.

Unlike probability sampling techniques, especially stratified

random sampling, quota sampling is much quicker and

easier to carry out because it does not require a sampling

frame and the strict use of random sampling techniques (i.e.

probability sampling techniques). This makes quota

sampling popular in undergraduate and master’s level

dissertations where there is a need to divide the population

being studied into strata (groups).

The quota sample improves the representations of particular

strata (groups) within the population, as well as ensuring

that these strata are not over-represented. For example, it

would ensure that we have sufficient male students taking

part in the research (60% of our sample size of 100; hence,

60 male students). It would also make sure we did not have

more than 60 male students, which would result in an over-

representation of male students in our research.

The use of quota sample, which leads to stratification of a

sample (e.g. male and female students), allows us to more

easily compare these groups (strata)

Cons of Cluster Sampling

In quota sampling, the sample has not been chosen using

random selection, which makes it impossible to determine

the possible sampling error. Indeed, it is possible that the

selection of units to be included in the sample will be based

on ease of access and cost considerations, resulting in

sampling bias. It also means that it is not possible to make

generalizations (i.e. statistical inferences) from the sample

to the population. This can lead to problems of external

validity.

Also, with quota sampling is must be possible to clearly

divide the population into strata; that is, each unit from the

population must only belong to one stratum. In our example,

this would be fairly simple, since our strata are male and

female students. Clearly, a student could only be classified

as either male or female. No student could fit into both

categories (ignoring transgender issues).

Furthermore, imagine extending the sampling requirements

such that we were also interested in how career goals

changed depending on whether a student was an

undergraduate or postgraduate. Since the strata must be

mutually exclusive, this means that we would need to

sample four strata from the population: undergraduate

males, undergraduate females, postgraduate males and

postgraduate females. This will increase overall sample size

required for the research, which can increase costs and time

to carry out the research.

2. Purposive Sampling

Purposive sampling, also known as judgmental, selective or

subjective sampling, reflects a group of sampling techniques

that rely on the judgement of the researcher when it comes

to selecting the units (e.g. people, case/organisations, events,

pieces of data) that are to be studied. These purposive

sampling techniques include maximum variation sampling,

homogeneous sampling and typical case sampling; extreme

(deviant) case sampling, total population sampling ad expert

sampling.

Pros of Purposive Sampling

 Whilst the various purposive sampling techniques each

have different goal, they can provide researchers with

the justification to make generalisations from the

sample that is being studied, whether such

generalisations are theoretical, analytic and logical in

nature. However, since each of these types of purposive

sampling differs in terms of the nature and ability to

make generalisations you should read the articles on

each of these purposive sampling techniques to

understand their relative advantages.

 Qualitative research designs can involve multiple

phases, with each phase building on the previous one.

In such instances different types of sampling techniques

may be required at each phase. Purposive sampling is

useful in these instances because it provides a wide

range of non-probability sampling techniques for the

researcher to draw on. For example critical case

sampling may be used to investigate whether a

phenomenon is worth investigating further, before

adopting an expert sampling approach to examine

specific issues further.

Cons of Purposive Sampling

 Purposive samples, irrespective of the type of purposive

sampling used, can be highly prone to researcher bias.

The idea that a purposive sample has been created

based on the judgement of the researcher is not a good

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defence when it comes to alleviating possible researcher

biases, especially when compared with probability

sampling techniques that are designed to reduce such

biases. However, this judgemental subjective

component of purpose sampling is only a major

disadvantage when such judgements are ill-conceived

or poorly considered; that is, where judgements have

not been based on clear criteria, whether a theoretical

framework, expert elicitation or some other accepted

criteria.

 The subjectively and non-probability based nature of

unit selection (i.e. selecting people, cases/organisations

etc.) in purposive sampling means that it can be

difficult to defend the representativeness of the sample.

In other words, it can be difficult to convince the reader

that the judgement you used to select units to study was

appropriate. For this reason, it can also be difficult to

convince the reader that research using purposive

sampling achieved theoretical/analytic/logical

generalisation. After all, if different units had been

selected, would the results and any generalisations have

been the same?

3. Self-Selection Sampling

Self-selection sampling is appropriate when we want to

allow units or cases, whether individuals or organisations to

choose to take part in research on their own accord. The key

component is that research subjects volunteer to take part in

the research rather than being approached by the researcher

directly.

Pros of Self-selection Sampling

 This can reduce the amount of time necessary to search

for appropriate units (or cases); that is, those individuals

or organisations that meet the selection criteria needed

for your sample.

 The potential units or cases are likely to be committed

to take part in the study, which can help in improving

attendance and greater willingness to provide more

insight into the phenomenon being studied.

Cons of Self-selection Sampling

Since the potential research subjects (or organisations)

volunteer to take part in the survey:

 There is likely to be a degree of self-selection bias. For

example, the decision to participate in the study may

reflect some inherent bias in the characteristics/traits of

the participants (e.g. an employee with a ‘chip of his

shoulder’ wanting to give an opinion).

 This can either lead to the sample not being

representative of the population being studied or

exaggerating some particular finding from the study.

4. Snowball Sampling

In sociology and statistics research, snowball sampling or

chain sampling, chain-referral sampling is a non-probability

sampling technique where existing study subjects recruit

future subjects from among their acquaintances. Thus the

sample group appears to grow like a rolling snowball. As

the sample builds up, enough data is gathered to be useful

for research. This sampling technique is often used in

hidden populations which are difficult for researchers to

access.

Pros of Snowball Sampling

 It can be difficult to identifying units to include in your

sample, perhaps because there is no obvious list of the

population you are interested in. For example, there are

no lists of drug users or prostitutes that a researcher

could get access to, especially lists that could be

considered representative to the population of drug

users or prostitutes.

 There may be no other way of accessing your sample,

making snowball sapling the only viable choice of

sampling strategy.

Cons of Snowball Sampling

Since snowball sampling does not select units for inclusion

in the sample based on random selection, unlike probability

sampling technique, it is impossible to determine the

possible sampling error and make generalizations (i.e.

statistical inferences) from the sample to the population. As

such, snowball samples should not be considered to be

representative of the population being studied.

References

1. http://en.wikipedia.org/wiki/sampling

(singal_processing)

2. http://en.wikipedia.org/wiki/snowball_sampling

3. http://occupytheory.org/advantages-and-disadvantages-

of-random-sampling/

4. http://blog.verint.com/conveniences-samples-pros-and-

cons

5. Dr. Kamlesh ML. Methodology of Research in Physical

Education and Sports.

6. Dr. Kamlesh ML. UGC-NET digest on papers I and II

Physical Education second edition in, 2.