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SAMPLING
STRATEGIE
S
MATH 417
Research Trends And Needs In
Mathematics
LLANDA, MARY GRACE G.

Basic Terminologies
Sampling
o Need of Sampling
o Advantages of Sampling
o Disadvantages of Sampling
o Characteristics of a good Sample
Types of sampling
o Probability Sampling
o Non-Probability Sampling
Contents to be Covered

 Population:
It refers to the group of people, items or units
under investigation and includes every
individual.
 Target Population:
Entire groups of people. Objects to which the
researchers wishes to generalize the finding of
the study.
 Sample:
a collection consisting of a part or subset of
the objects or individuals of population which
is selected for the purpose, representing the
population.
 Sample Size:
refers to the number of participants or
observations included in a study. This number
is usually represented by n.
Some Terminologies

 Sampling frame:
The list from which the potential respondents are drawn. It’s a
complete list of everyone or everything you want to study.
For example,
• The population could be “People who live in Jacksonville,
Florida.” The frame would name all of those people, from
Adrian Abba to Felicity Zappa.
• Population: People in STAT101.
Sampling Frame: Adrian, Anna, Bob, Billy, Howie, Jess, Jin, Kate,
Kaley, Lin, Manuel, Norah, Paul, Roger, Stu, Tim, Vanessa, Yasmin.
Some Terminologies

 It is the process of selecting a sample
from the population. For this population is
divided into a number of parts called
Sampling Units.
 a process in statistical analysis where
researchers take a predetermined number of
observations from a larger population.
 Sampling allows researchers to conduct
studies about a large group by using a small
portion of the population.
SAMPLING

Teaching Strategy sampling strategy -.pptx

 Large population can be conveniently
covered.
 Time, money and energy is saved.
 Helpful when units of area are
homogenous.
 Used when the data is unlimited.
Need of Sampling

 Economical: Reduce the cost compare to entire
population.
 Increased speed: Collection of data, analysis and
Interpretation of data etc take less time than the
population.
 Accuracy: Due to limited area of coverage,
completeness and accuracy is possible.
 Rapport: Better rapport is established with the
respondents, which helps in validity and reliability of the
results
Advantages of Sampling

 Biasedness: Chances of biased selection leading to
incorrect conclusion
 Selection of true representative sample:
Sometimes it is difficult to select the right
representative sample
 Need for specialized knowledge: The
researcher needs knowledge, training and
experience in sampling technique, statistical analysis
and calculation of probable error
 Impossibility of sampling: Sometimes
population is too small or too heterogeneous to
select a representative sample.
Disadvantages of Sampling

 A true representative of the population.
 Free from error due to bias.
 Adequate in size for being reliable.
 Units of sample should be independent and
relevant
 Units of sample should be complete precise
and up to date
 Free from random sampling error
 Avoiding substituting the original sample for
convenience.
Characteristics of a Good Sample

Probability Sampling
 Selection of a sample from a population, when
this selection is based on the principle of
randomization, that is, random selection or
chance.
 More complex, more time-consuming and
usually more costly than non-probability
sampling.
 Each member of the population has an equal
chance of being selected.
Types of Sampling

Non-Probability Sampling
 Nonprobability Sample a particular member of the
population being chosen is unknown.
 In Non-probability sampling, it relies on personal
judgment.
 This type of sample is easier and cheaper to
access, but it has a higher risk of sampling bias.
That means the inferences you can make about
the population are weaker than with probability
samples, and your conclusions may be more
limited
Types of Sampling

 Non-probability sampling techniques are often
used in exploratory and qualitative research. In
these types of research, the aim is not to test a
hypothesis about a broad population, but to
develop an initial understanding of a small or
under-researched population.
Types of Sampling
T

It refers to the procedures of
obtaining units or members
who are most conveniently
available. It consists of units
which are obtained because
cases are readily available.
Convenience Sampling
Disadvantage: Selection bias

In selecting the incidental sample, the researcher determines the
required sample size and then simply collects data on that number
of individuals who are available easily.
Convenience Sampling

The selection of the sample is made
by the researcher, who decides the
quotas for selecting sample from
specified sub groups of the
population.
Quota Sampling
Technique
 Quotas set using some characteristic of the population thought to
be relevant
 Subjects selected non-randomly to meet quotas (usu.
convenience sampling)
Disadvantage
selection bias
Cannot set quotas for all characteristics important to study

Widely used in opinion polls and market research.
For example,
An interviewer might be need data from 40 adults and
20 adolescents in order to study students’ television
viewing habits.
Selection will be
 20 Adult men and 20 adult women
 10 adolescent girls and 10 adolescent boys
Quota Sampling

In this sampling method, the
researcher selects a "typical group" of
individuals who might represent the
larger population and then collects
data from this group.
JUDGEMENTAL (PURPOSIVE) Sampling
When using this method, the researcher must be
confident that the chosen sample is truly
representative of the entire population.

JUDGEMENTAL (PURPOSIVE) Sampling
For Example
You want to know more about the opinions and experiences of
disabled students at your university, so you purposefully select a
number of students with different support needs in order to gather a
varied range of data on their experiences with student services.

In snowball sampling, the researcher Identifying and selecting
available respondents who meet the criteria for inclusion.
After the data have been collected from the subject, the researcher
asks for a referral of other individuals, who would also meet the
criteria and represent the population of concern.
chain sampling, chain-referral, sampling referral sampling
snowball Sampling

snowball Sampling
For Example
You are researching experiences of homelessness in your city.
Since there is no list of all homeless people in the city, probability
sampling isn’t possible. You meet one person who agrees to
participate in the research, and she puts you in contact with other
homeless people that she knows in the area.

Techniques
• Lottery method
• Table of random numbers
Advantage
• Most representative group
Disadvantage
• Difficult to identify every member of a population
Random method provides an
unbiased cross selection of the
population.
Simple random Sampling

For Example,
We wish to draw a sample of 50 students from a population of
400 students. Place all 400 names in a container and draw out
50 names one by one.
Simple random Sampling

• Systematic sampling, sometimes called interval sampling,
means that there is a gap, or interval, between each selection.
• Often used in industry, where an item is selected for testing
from a production line (say, every fifteen minutes)
Systematic random Sampling

This technique requires the first item to be selected at random
as a starting point for testing and, thereafter, every nth item is
chosen.

For Example,
 The employees of the company are listed in alphabetical
order. From the first 10 numbers, you randomly select a
starting point: number 6. From number 6 onwards, every
10th person on the list is selected (6, 16, 26, 36, and so on), and
you end up with a sample of 100 people.
 If a systematic sample of 300 students were tobe carried out
in UMS with an enrolledpopulation of 15,000, the sampling
interval would be: I = N/n = 15,000/300 =50.
This meaning that 1 element (student) will be selected in
every 50 students from the list of 15,000 UMS students
until the 300th student.

Stratified random Sampling
The population is divided into
homogeneous subgroups (strata) based on
certain characteristics (e.g., age, gender,
income), and then random samples are
taken from each stratum proportionally to
their size in the population.

Technique
• Divide population into various strata
• Randomly sample within each strata
• Sample from each strata should be proportional
Advantage
• Better in achieving representativeness on control variable
Disadvantage
• Difficult to pick appropriate strata
• Difficult to Identify every member in population

For Example,
The company has 800 female employees and 200 male employees.
You want to ensure that the sample reflects the gender balance of
the company, so you sort the population into two strata based on
gender. Then you use random sampling on each group, selecting 80
women and 20 men, which gives you a representative sample of 100
people.

cluster Sampling
• Cluster sampling is a sampling technique where the entire
population is divided into groups, or clusters.(such as
geographical areas or organizational units)
• Then a random sample of these clusters are selected using
SRS.
• All observations/individuals in the selected clusters are
included in the sample.

cluster Sampling

cluster Sampling
For example,
The company has offices in 10 cities across the country (all with
roughly the same number of employees in similar roles). You
don’t have the capacity to travel to every office to collect your
data, so you use random sampling to select 3 offices – these are
your clusters.

Cluster Vs Stratified Sampling

Other Sampling Techniques
Other
sampli
ng
techni
que

 /slideshow/sampling-techniques-and-types/100883371
 /slideshow/the-sampling-design-process/75943202
 https://www.youtube.com/watch?app=desktop&v=Sqbpr08Q8jg
 https://ksumsc.com/download_center/Archive/3rd/432/Female/Research/Lecture%2019-S
ampling%20Techniques.ppt
 https://www.ums.edu.my/ppps/files/SamplingMergeScience.pdf
 https://www.scribbr.com/methodology/sampling-methods/
References

THANK YOU!
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