2. POPULATION
•includes all of its elements from a set
of data. The size of the population is
the number of observations in the
population.
3. SAMPLE
•consists of one or more data drawn from the
population. It is a subset or an incomplete set taken
from a population of objects or observations. Taking
samples instead of the population is less time-
consuming and cost-effective.
6. RANDOM SAMPLING
•is a sampling method of choosing
representatives from the population wherein
every sample has an equal chance of being
selected. Accurate data can be collected using
random sampling techniques.
7.
8. Use your understanding of the previous activity to
identify whether the following situations illustrate
simple, systematic, stratified or cluster random
sampling.
1. A researcher writes the name of each student on
a piece of paper, mixes the papers in a bowl, and
draws 7 pieces of paper.
9. 2. A researcher selects every 7th
student from a random list.
Use your understanding of the previous activity to
identify whether the following situations illustrate
simple, systematic, stratified or cluster random
sampling.
10. 3. A researcher tells the class to count off
and then selects those students who
count a multiple of 7 numbers.
Use your understanding of the previous activity to
identify whether the following situations illustrate
simple, systematic, stratified or cluster random
sampling.
11. 4. A researcher separates the list of boys
and girls, then draws 7 names by
Gender.
Use your understanding of the previous activity to
identify whether the following situations illustrate
simple, systematic, stratified or cluster random
sampling.
12. 5. A researcher surveys all students
from 3 randomly selected classes out of
7 classes.
Use your understanding of the previous activity to
identify whether the following situations illustrate
simple, systematic, stratified or cluster random
sampling.
13.
14. SIMPLE RANDOM
SAMPLING TECHNIQUE
•is the most basic random sampling wherein each element
in the population has an equal probability of being selected.
They are usually represented by a unique identification
number that is written on equal-sized and shaped papers
and then selection of samples is possible through the lottery
method.
15.
16. SYSTEMATIC RANDOM
SAMPLING
•is a random sampling that uses a list of all the
elements in the population and then elements are
being selected based on the kth consistent intervals.
To get the kth interval, divide the population size by the
sample size.
17.
18. STRATIFIED RANDOM
SAMPLING
•is a random sampling wherein the population is
divided into different strata or divisions. The
number of samples will be proportionately picked
in each stratum that is why all strata are
represented in the samples.
19.
20. CLUSTER SAMPLING
•is a random sampling wherein population is
divided into clusters or groups and then the
clusters are randomly selected. All elements of the
clusters randomly selected are considered the
samples of the study.
21.
22. •The sampling techniques that involve
random selection are called
probability sampling. Likewise,
simple random, systematic, and
stratified and cluster sampling are all
probability sampling techniques.
23. •There are also sampling
techniques that do not involve
random selection of data.
They are called non-
probability sampling.
24. •Purposive sampling is also not
considered a random sampling
since the respondents are being
selected based on the goal of the
studies of the researcher.
Editor's Notes
For example, if ABS-CBN network has 11,000 employees having the required blood type in a certain study, then we have a population of size 11,000
If a researcher opts to use a sample rather than a population, he must take considerations on the number of samples and how these samples can be chosen out of the target population.
.
B
A
D
C
Situation 1 illustrates simple random sampling. The pieces of papers correspond to each student as elements of the population. All of them have an equal chance of being selected as a sample by randomly picking 7 pieces of paper in a bowl
Situations 2 and 3 illustrate systematic random sampling because samples are being selected based on the kth consistent intervals. Selecting every 7th student on the random list of names creates an equal chance for all of the students to become samples.
The same thing happened in selecting students who count multiples of 7 or 14, 21, and so on.
Situation 4 illustrates stratified random sampling because the students are divided into two different strata or groups, boys and girls. With a proportional number for each group, samples are then selected at random from these two groups.
Situation 5 illustrates cluster sampling since all students are divided into clusters or classes, then 3 classes are selected at random out of the 7 classes. All of the students of these three classes comprised the samples of the study. Take note that each cluster is mutually homogeneous yet internally heterogeneous
. Random numbers are selected to decide which elements are included as the sample. The number of papers to be drawn is based on the desired number of samples.
An example of this is convenience sampling wherein the researcher gathers data from nearby sources of information exerting minimal effort. Convenience is being used by persons giving questionnaires on the streets to ask the passers-by.
If the study is about the students who are children of OFW, the researcher will get samples who are children of OFW. This excludes other students from being a sample.