Sampling Techniques for doing research

1. 1
Sampling
Research Methods for Business
Definition…
Sampling is the process
of selecting a small number of elements
from a larger defined target group (Population)
of elements such that
the information gathered
from the small group will allow judgments
to be made about the larger groups.
Sampling is the act, process, or technique of selecting a suitable sample, or a representative part of a
population for the purpose of determining parameters or characteristics of the whole population.
Definition…
Purpose Of Sampling …
To draw conclusions about populations from samples, which enables us to determine
a population`s characteristics by directly observing only a portion (or sample) of the population. We obtain a
sample rather than a complete enumeration (a census ) of the population for many reasons.
6 MAIN REASONS FOR SAMPLING…
o . Economy
o . Timeliness
o . The large size of many populations
o . Inaccessibility of some of the population
o . Destructiveness of the observation
o . Accuracy
REASONS FOR SAMPLING…
 Economy - taking a sample requires fewer resources than a census.
 Time factor -a sample may provide you with needed information quickly.
 The very large populations -many populations about which inferences must be made are quite large
 The partly accessible populations- There are some populations that are so difficult to get access to that
only a sample can be used.
 The destructive nature of the observation-sometimes the very act of observing the desired characteristic
of a unit of the population destroys it for the intended use.
 Accuracy and sampling- A sample may be more accurate than a census. A sloppily conducted census can
provide less reliable information than a carefully obtained sample.
Important terminologies...

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 . Population
 . Element
 . Sample
 . Sampling Unit
 . Subject
Population
The population refers to the entire group of people, events or things of interest that the researcher wishes to
investigate.
o If an organizational consultant is interested in studying the effects of a four-day work week on the white-
coller workers in a telephone company in Ireland. Then all white-coller workers in that company will make
up the population.
o If regulators wants to know how patients in nursing homes run by a company in France, then all the patients
in all the nursing homes run by them will form the population. If however, the regulators are interested only
in one particular nursing home run by that company, then only the patients in that particular nursing home
will make the population.
Element
An element is the
single member of the population.
 If 1000 blue-coller workers in a particular organization are working and an researcher is interested to know
the satisfaction level of these workers then each member (blue-coller) of the particular organization will be
considered as element.
 Census is a count of all elements in the human population.
Sample
A sample is a subset of the population. it comprises some members from it.
 . If 200 members are drawn/selected from a population of 1000 blue-coller workers to
study the desire outcome, then 200 members form the sample for the study.
 . If there are 145 patients in a hospital and 40 of them are to be surveyed by the hospital administrator to
assess there level of satisfaction with the treatment received, then these 40 members will be called the
sample.
A sample is thus a subgroup or subset of the population. By studying the sample, the researcher should be
able to draw conclusions that are generalizable to the population of interest.
Sampling Unit

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The sample unit is the element or the set of elements that is available for selection in some stage of the
sampling process.
Example of sampling units in a multi stage sample are city blocks, house hold, and individuals with in the
households.
Subject
A subject is a single member of the sample just as an element is a single member of the population.
 . If 200 members from the total population of 1000 blue-coller workers form the sample for the study. Then
each blue-coller worker in the sample is a subject.
 . If there are 145 patients in a hospital and 40 of them are to be surveyed by the hospital administrator to
assess there level of satisfaction with the treatment received, then each member from sample of 40 will be
called the subject.
Representative of Sampling...
 Choosing the right sample cannot be overemphasized.
 If we choose the sample in a scientific way, we can be reasonably sure that sample statistics (Mean,
Standard Deviation, (S) Variation in the sample ) and population parameters (Mean (u), Standard Deviation,
Variation in the sample ) are close to each others.
Acknowledgments to Uma Sekaran
What is a Good Sample?
 . Accurate: absence of bias
 . Precise estimate: sampling error
Sampling error is any type of bias
that is attributable to mistakes
in either drawing a sample or
determining the sample size.
Sampling Process…
Define the Population
Determine the Sampling Frame
Select Sampling Technique(s)
Determine the Sample Size
Execute the Sampling Process
Defining Population of Interest…
Population of interest is entirely dependent on Management Problem, Research Problems, and Research
Design.
Some Bases for Defining Population:

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 . Geographic Area (Pakistan, Punjab, Banking sector, Our Institute etc.)
 . Demographics (Gender, Age, Color, Height etc.)
 . Usage/Lifestyle
 . Awareness
Sampling Frame …
A list of population elements (people, companies, houses, cities, etc.) from which units to be sampled can be
selected.
 Difficult to get an accurate list.
 Sample frame error occurs when certain elements of the population are accidentally omitted or not included
on the list.
Sampling Methods/Techniques
Probability
Sampling
Nonprobability
Sampling
Sampling Methods/Techniques/Types
Sampling Techniques
Nonprobability
Sampling Techniques
Probability
Sampling Techniques
Convenience
Sampling
Judgmental
Sampling
Quota
Sampling
Snowball
Sampling
Systematic
Sampling
Stratified
Sampling
Cluster
Sampling
Other Sampling
Techniques
Simple Random
Sampling
Probability Sampling Designs
A probability sample is one that gives every member of the population a known chance of being selected.
All are selected randomly.

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o Simple random sampling - anyone
o Systematic sampling
o Stratified sampling - different groups (ages)
o Proportionate
o Cluster sampling - different areas (cities)
Simple Random Sampling
Simple random sampling is a method of probability sampling in which every unit has an equal nonzero
chance of being selected
 Each element in the population has a known and equal probability of selection.
 This implies that every element is selected independently of every other element.
Systematic Sampling
Systematic Random Sampling is a method of probability sampling in which the defined target population
is ordered and the sample is selected according to position using a skip interval.
 The sample is chosen by selecting a random starting point and then picking every ith
element in succession
from the sampling frame.
 The sampling interval, i, is determined by dividing the population size N by the sample size n and rounding
to the nearest integer.
 For example, there are 100,000 elements in the population and a sample of 1,000 is desired. In this case the
sampling interval, i, is 100. A random number between 1 and 100 is selected. If, for example, this number is
23, the sample consists of elements 23, 123, 223, 323, 423, 523, and so on.
Stratified Sampling
Stratified Random Sampling is a method of probability sampling in which the population is divided into
different subgroups and samples are selected from each
 A two-step process in which the population is partitioned into subpopulations.
 Divide the target population into homogeneous subgroups or strata
 Draw random samples fro each stratum
 Combine the samples from each stratum into a single sample of the target population
 A major objective of stratified sampling is to increase precision without increasing cost.
Cluster Sampling

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 The target population is first divided into mutually exclusive and collectively exhaustive subpopulations, or
clusters.
 Then a random sample of clusters is selected, based on a probability sampling technique.
 For each selected cluster, either all the elements are included in the sample (one-stage) or a sample of
elements is drawn probabilistically (two-stage).
 Elements within a cluster should be as heterogeneous as possible, but clusters themselves should be as
homogeneous as possible. Ideally, each cluster should be a small-scale representation of the population.
 In probability proportionate to size sampling, the clusters are sampled with probability proportional to size.
In the second stage, the probability of selecting a sampling unit in a selected cluster varies inversely with the
size of the cluster.
Nonprobability Sampling
…Nonprobability sample is an arbitrary grouping that limits the use of some statistical tests. It is not
selected randomly.
Classifications of Nonprobability Sampling
 Convenience Sampling
 Judgment Sampling
 Quota Sampling
 Snowball Sampling
Convenience Sampling
Convenience sampling attempts to obtain a sample of convenient elements. Often, respondents are selected
because they happen to be in the right place at the right time.
o Use of students, and members of social organizations
o Mail intercept interviews without qualifying the
respondents.
o “people on the street” interviews
Judgmental Sampling
Judgmental sampling is a form of convenience sampling in which the population elements are selected
based on the judgment of the researcher.
o Test markets
o Engineers selected in industrial marketing research
o Expert witnesses used in court

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Quota Sampling
Quota sampling may be viewed as two-stage restricted judgmental sampling.
o The first stage consists of developing control categories, or quotas, of population elements.
o In the second stage, sample elements are selected based on convenience or judgment.
Population Sample
composition composition
Control
Characteristic Percentage Percentage Number
Sex
Male 48 48 480
Female 52 52 520
____ ____ ____
100 100 1000
Snowball Sampling
In Snowball Sampling, an initial group of respondents is selected, usually at random.
o After being interviewed, these respondents are asked to identify others who belong to the target population
of interest.
o Subsequent respondents are selected based on the referrals.
Factors to Consider in Sample Design
Research objectives
Degree of accuracy
Resources
Time frame
Knowledge of
target population
Research scope
Determining Sample Size
 How many completed questionnaires do we need to have a representative sample?
 Generally the larger the better, but that takes more time and money.
 Answer depends on:
o How different or dispersed the population is.
o Desired level of confidence.
o Desired degree of accuracy.
Conclusion

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In conclusion, it can be said that using a sample in research saves mainly on money and time, if a suitable
sampling strategy is used, appropriate sample size selected and necessary precautions taken to reduce on
sampling and measurement errors, then a sample should yield valid and reliable information.
Source:
https://docs.google.com/presentation/d/1QRmt7fiEOsaJi-i5v9h7EyXu6LA9o2iiIR8jkKJx4Q4/htmlpresent