This document discusses different sampling methods used in research. It explains that sampling is selecting a subset of a population to study and is important for obtaining accurate results. There are two main types of sampling: probability sampling, which uses random selection to make statistical inferences about a whole group; and non-probability sampling, which uses non-random selection for exploratory research. Some specific sampling methods covered include simple random sampling, systematic sampling, stratified sampling, cluster sampling, convenience sampling, and snowball sampling. The document provides details on how each method is conducted and when each is most appropriate to use.

1. Presentation by
JONATHAN PASCUAL, LPT.
What is sampling methods?
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2. Sampling is one of the most important factors which
determines the accuracy of your research/survey result. If anything
goes wrong with your sample then it will be directly reflected in the
final result (Sigh, 2018).
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3. Population can be explained as a
comprehensive group of individuals,
institutions, objects and so forth which
have a common characteristic that are
the interest of a researcher
(Rafeedali, n.d.).
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4. Sample is the subset of the
population. The process of selecting a
sample is known as sampling. Number
of elements in the sample is the sample
size (Singh, 2018).
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5. CGCI
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Probability Sampling Non-probability Sampling
Involves random selection, allowing you to
make statistical inferences about the
whole group.
• Involves non-random selection based on
convenience or other criteria, allowing you
to collect initial data.
• It is often used for exploratory and
qualitative research
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When you conduct research about a group of people, it is rarely possible to collect data from
every person in that group. Instead, you select a sample. The sample is the group of individuals
who will actually participate in the research. To draw valid conclusions from your results, you
have to carefully decide how you will select a sample that is representative of the group as a
whole (McCombes, 2020).

6. Simple Random Sample
Systematic
Sample
Stratified Sample
Cluster
Sample
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In a simple random sample, every member of the
population has an equal chance of being selected
Your sampling frame should include the
whole population.
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Systematic sampling is similar to simple random sampling,
but it is usually slighter easier to conduct.
Every member of the population is listed with
a number, but instead of randomly
generating numbers, individuals are chosen
at regular intervals
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This sampling method is appropriate when the population
has mixed characteristics, and you want to ensure that
every characteristic is proportionally represented in the
sample.
You divide the population into subgroups
(called strata0 based on the relevant
characteristics (e.g. gender, age range,
income bracket, job role)
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Cluster sampling also involves dividing the
population into subgroups, but each
subgroup should have similar
characteristics to the whole sample.
Instead of sampling individuals from each
subgroup, you randomly select entire
subgroups.
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11. Convenience Sample
Voluntary Response Sample
Purposive Sample
Snowball Sample
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12. You simply include the individuals who happen to
be most accessible to the researcher. This is an
easy and inexpensive way to gather initial data, but
there is no way to tell if the sample is representative
of the population, so it cannot produce
generalizable results.
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13. Similar to a convenience sample, a voluntary
response sample is mainly based on ease of
access. Instead of the researcher choosing
participants and directly contacting them, people
volunteer themselves. (e.g. by responding to a
public survey). Voluntary response samples are
biased.
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14. This type of sampling involves the researcher using
their judgment to select a sample that is most
useful to the purposes of the research. An effective
purposive sample must have clear criteria and
rationale for inclusion.
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15. If the population is hard to access, snowball
sampling can be used to recruit participants via
other participants. The number of people you have
access to “snowballs” as you get in contact with
more people.
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Write A if statement 1 and statement 2 are true.
Write B if statement 1 and statement 2 are false.
Write C if only one statement is true.
1. (1)Sampling design determines the accuracy of the result of your study because
(2) sampling design is where you select your participants who will participate in
your study.
2. (1) Population determines the geographical location of the participants, while
(2) sample is the specific group of individuals that you will collect data.

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3. (1) Probability sampling is to quantitative as (2) non-probability sampling is to
qualitative.
4. (1) You can draw valid and reliable result if you choose the right sampling
design. Thus, (2) you should first consider your research

18. CGCI
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TRUE OR FALSE
Write A if statement 1 and statement 2 are true.
Write B if statement 1 and statement 2 are false.
Write C if only one statement is true.
5. (1) Cluster sample is usually utilized if you will study large sample of population,
while (2) stratified sample is usually used if you want to determine the different
characteristics of a sample.
6. (1) Selecting a sample from a population is optional to the researchers, as (2) it
will not affect the findings or results of the study.

19. CGCI
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TRUE OR FALSE
Write A if statement 1 and statement 2 are true.
Write B if statement 1 and statement 2 are false.
Write C if only one statement is true.
7. (1) The number of elements in the sample is called sample size while (2) sample
is the subset of the population.
8. (1) If you used stratified sampling, the sample is called strata, while in (2) simple
random, the sample is called randomization.

20. CGCI
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TRUE OR FALSE
Write A if statement 1 and statement 2 are true.
Write B if statement 1 and statement 2 are false.
Write C if only one statement is true.
9. (1) Stratified sampling is suitable if all my respondents are teachers or doctors,
while (2) cluster sampling if I randomly select numbers inside the box.
10. (1) Simple random sampling and systematic sampling are similar since every
sample of the population has an equal chance to be selected in the study, while
(2) convenience sampling and voluntary response sampling are also similar
because it is mainly based on ease access.
BONUS: Give me a word that rhymes with search

21. Presentation by JONATHAN PASCUAL, LPT
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