Sampling Techniques in Statistics Sampling techniques are methods used to select a subset of individuals or observations from a larger population for the purpose of statistical analysis. The goal of sampling is to obtain a representative sample that accurately reflects the characteristics of the population, allowing researchers to make inferences about the population based on the sample data. Types of Sampling Techniques There are several types of sampling techniques, each with its own strengths and weaknesses. Some of the most common sampling techniques include: 1. *Random Sampling*: Random sampling involves selecting individuals or observations from the population using a random process, such as a random number generator. This technique ensures that every member of the population has an equal chance of being selected. 2. *Stratified Sampling*: Stratified sampling involves dividing the population into distinct subgroups or strata and then sampling from each stratum. This technique is useful when the population is heterogeneous and the researcher wants to ensure that each subgroup is adequately represented. 3. *Systematic Sampling*: Systematic sampling involves selecting individuals or observations from the population at regular intervals, such as every 10th individual. This technique is useful when the population is large and a random sample is not feasible. 4. *Cluster Sampling*: Cluster sampling involves selecting groups or clusters of individuals or observations from the population and then sampling from each cluster. This technique is useful when the population is spread out over a large geographic area. 5. *Convenience Sampling*: Convenience sampling involves selecting individuals or observations that are easily accessible or convenient to study. This technique is often used in exploratory studies or pilot studies. 6. *Purposive Sampling*: Purposive sampling involves selecting individuals or observations that are specifically chosen for their relevance to the research question. This technique is often used in qualitative research studies. 7. *Quota Sampling*: Quota sampling involves selecting individuals or observations that meet specific criteria or quotas, such as age, sex, or occupation. This technique is often used in market research studies. Advantages and Disadvantages of Sampling Techniques Each sampling technique has its own advantages and disadvantages. Some of the advantages of sampling techniques include: - *Cost-effective*: Sampling techniques can be more cost-effective than studying the entire population. - *Time-efficient*: Sampling techniques can be more time-efficient than studying the entire population. - *Increased accuracy*: Sampling techniques can provide more accurate results than studying the entire population, especially when the population is large and diverse. Some of the disadvantages of sampling techniques include: - *Sampling bias*: Sampling techniques can be subject to sampling bias, which occurs when the

1. Sampling

2. 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.

3. 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…

4. 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.

5. 6 MAIN REASONS FOR SAMPLING…
●. Economy
●. Timeliness
●. The large size of many populations
●. Inaccessibility of some of the population
●. Destructiveness of the observation
●. Accuracy

6. 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.

7. Important terminologies...
●. Population
●. Element
●. Sample
●. Sampling Unit
●. Subject

8. Population
The population refers to the entire group of people,
events or things of interest that the researcher
wishes to investigate.
●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.
●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.

9. 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.

10. 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.
Sample

11. Sampling Unit
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.

12. 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.

13. 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.

14. Acknowledgments to Uma Sekaran

15. 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.

16. Sampling Process…
Define the Population
Determine the Sampling Frame
Select Sampling Technique(s)
Determine the Sample Size
Execute the Sampling Process

17. Population of interest is entirely dependent on
Management Problem, Research Problems,
and Research Design.
Some Bases for Defining Population:
● . Geographic Area (Pakistan, Punjab, Banking sector, Our Institute etc.)
● . Demographics (Gender, Age, Color, Height etc.)
● . Usage/Lifestyle
● . Awareness
Defining Population of Interest…

18. 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 Frame …

19. Sampling Methods/Techniques
Probability
Sampling
Nonprobabil
ity
Sampling

20. Sampling Methods/Techniques/Types
Sampling Techniques
Nonprobability
Sampling Techniques
Probability
Sampling Techniques
Convenienc
e
Sampling
Judgmenta
l
Sampling
Quota
Samplin
g
Snowbal
l
Samplin
g
Systemati
c
Sampling
Stratified
Samplin
g
Cluster
Samplin
g
Other
Sampling
Techniques
Simple
Random
Sampling

21. 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.
● Simple random sampling - anyone
● Systematic sampling
● Stratified sampling - different groups (ages)
● Proportionate
● Cluster sampling - different areas (cities)

22. 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.

23. 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.

24. 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.

25. Cluster Sampling
●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.

26. 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

27. 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.
● Use of students, and members of social
organizations
● Mail intercept interviews without qualifying the
respondents.
● “people on the street” interviews

28. Judgmental Sampling
Judgmental sampling is a form of convenience
sampling in which the population elements are selected
based on the judgment of the researcher.
● Test markets
● Engineers selected in industrial marketing research
● Expert witnesses used in court

29. Quota Sampling
Quota sampling may be viewed as two-stage
restricted judgmental sampling.
1.The first stage consists of developing control
categories, or quotas, of population elements.
2.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

30. Snowball Sampling
In Snowball Sampling, an initial group of
respondents is selected, usually at random.
● After being interviewed, these respondents
are asked to identify others who belong to
the target population of interest.
● Subsequent respondents are selected
based on the referrals.

31. Factors to Consider in Sample Design
Research objectives Degree of accuracy
Resources Time frame
Knowledge of
target population Research scope

32. ●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:
●How different or dispersed the population is.
●Desired level of confidence.
●Desired degree of accuracy.
Determining Sample Size

33. Sampling Error​
● A statistical error
that occurs when
the analyst selects​
a sample that is
not representative of
the population being
studied.​
● This can lead to
incorrect
conclusions being
drawn about the
population based
on the sample.​

34. Types Of Sampling Error​
● RANDOM SAMPLING
ERROR​
● SYSTEMATIC
SAMPLING ERROR​
● NON SAMPLING
ERROR​

35. Causes Of Sampling Error​
Small Sample Size​
Biased Sampling
Method​
Non Response​
Data Entry Errors​

36. Conclusion
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.