Probability_Research

1. SamplingProcedure
inQuantitative
Research

2. Learning Objectives
define sample,
population, sample size,
and sampling frame;
1
calculate sample size
using Slovin’s formula;
and
2
design a sampling plan
based on a hypothetical
population.
3

3. PRETEST
1. What do you call a group of individuals that the researcher is interested to study and
usually has common or similar characteristics?
A. Population C. Sampling frame
B. Sample D. Sampling Techniques
2. What do you call an individual that represents the entire population of the target
respondents of the study?
A. Population C. Sampling frame
B. Sample D. Sampling Techniques

4. PRETEST
3. Which of the following statements is NOT true about the reason of using of samples in
research?
A. Using sample saves money because it is less costly.
B. Using sample can reduce sampling error in a survey.
C. Using sample saves time compared to complete census.
D. Using sample can reduce the validity of the research result.
4. A researcher conducted his study at Rizal High School, Pasig City. The total number of students
in Rizal High School is 11,100 according to its master list. The researcher uses a five percent
margin of error to come up with the total number of participants in his study which is 387.
What is the sample size of the researcher based on the given data?
A. 11,100 C. 5%
B. 0.05 D. 387

5. PRETEST
• 5. Suppose that you have a group of 10,000 target students for your research study and
you want to survey them to find out the effectiveness of K-12 curriculum in enhancing
their life skills. Find the sample size of your target respondents using a 0.02 margin of
error?
A. 1,000
B. 1,500
C. 2,000
D. 2,500

6. ANSWERS
1.
2.
3.
4.
5.

7. In your Practical Research 1, you have learned how to
determine sample size in a qualitative type of research.
Let’s have a short activity as a review. Below are some
questions to help you recall how researchers determined
the sample size for their qualitative research.
•
•
•

8. Definition of
Terms Sample. It refers to an individual that represents the
entire population of target respondents of the study. This
is the subgroup of the population
Population. It refers to a group of individuals that the
researcher is interested in studying and usually has
common or similar characteristics.
Sample size. It refers to the number of elements in the
population that is included in the study.
Sampling frame. It refers to a complete list of all cases in
the population from which the sample will be drawn (e.g.
master list of Grade 12 students in a certain school).

9. Reason of using Sample in Research
The researchers must know the sample size of their study. It
is a waste of resources if the researchers include all the elements
in their target population. According to Prieto, Naval and Carey
(2017), the following are some reasons for the use of samples:
• Using sample saves time compared to complete census.
• Using sample saves money because it is less costly.
• Using sample allows more particular attention to be given to
several elements than doing a census.
• Using sample can lessen the sampling error in the survey.

10. SamplingStrategy
inQuantitative
Research

11. Sampling strategy is an important step to ensure that your data truly
reflects the characteristics of your target population. Mukherjee (2019)
cites steps in formulating sampling plan in quantitative research.
1. Define your
sample and
target
population.
2. Define your
sample size.
3. Define your
sampling
technique.

12. 1. Define your sample and target population.
• Most of the time, it is hard to survey all elements of your target
population, so you may come up with a smaller number that
may represent your target population.
• For example, it may not be feasible to visit all 10,000 students
in your target school. Instead, you’d want to choose a smaller
sample that would be representative of the population and
reflect its characteristics

13. 2. Define your sample size.
• You need to establish the confidence interval (margin of error)
and confidence level of your sample.
• The confidence level tells you how sure you want to be and is
expressed as a percentage. It represents how often the
responses from your selected sample reflect the responses of
the total population. Thus, a 95% confidence level means you
can be 95% certain. The lower the confidence level, the less
certain you will be. There are many formulas used in computing
your sample size. And one of those is the Slovin’s formula.

14. 3. Define your sampling
technique. Sampling techniques is
divided into two types:
a. probability sampling
(random sampling) which
gives equal chances of
selection to all elements of the
population; and
b. non-probability sampling
(non-random sampling) is an
unequal selection of samples
from the population

15. Slovin’s Formula
• Slovin’s formula is a statistical formula used to obtain an accurate sample size
(n) given the population (N) and margin of error (e). The margin of error (e) is
the allowable error margin in research. Slovin's Formula calculates the number
of samples required when the population is too large to directly sample every
member.

21. Probability
Sampling
Techniques

22. Probability Sampling
• The selection of components of the sample that will give a representative view of the whole is
known as the sampling technique. Selecting samples can be biased or unbiased. This is called
probability sampling.
• Probability Sampling refers to a sampling technique in which samples are obtained using some
objective chance mechanism, thus involving randomization. Please take note that
(1) if your population is LESS THAN 50, go away from probability sampling and
(2) your sample size should be AT LEAST 30. Probability sampling techniques give all elements of
the population an equal chance of being selected but using this technique may consume a lot of
time and effort of the researchers.
• After having your target population and sample, it is time to decide how to select the sample of
the study. There are different types of selecting samples under probability sampling.

23. Types of Probability Sampling Techniques
According to Prieto, Naval, and Carey (2017)
and Faltado et al. (2017), below are the types
of probability sampling techniques used in
quantitative research.
• 1. Simple Random Sampling.
• 2. Systematic Random
Sampling.
• 3. Stratified Random Sampling.
• 4. Cluster Sampling.
• 5. Multi-Stage Sampling.

24. 1. Simple Random Sampling.
• the chance of selection is the same for every member of the population.
To conduct this sampling technique, the researcher should ensure first
that he/she has the complete list of all the elements (sampling frame) of
his/her target population. From the list, the sample is drawn so that all
elements have equal number of chances to be selected.
• Here are ways of selecting samples:
• By utilizing a TABLE OF RANDOM NUMBERS
• By using the LOTTERY TECHNIQUES / FISHBOWL METHOD
• By using DIGITAL RANDOM PICKER APPLICATION

25. Example
• Example: The researcher’s target
respondents are all Grade 12 students.
Suppose there are 800 Grade 12 students
and he only needs to select 470 as the
sample. Using simple random sampling,
the researcher puts the 800 names of
Grade 12 students in a box and then pick
only 470 names to participate in his study

26. 2. Systematic Random Sampling.
It is a sampling that follows regular intervals from a list. It has specific steps and
procedures in doing the random selection of the samples. With this sampling technique, it
may spread the selected samples evenly across the entire population than simple random
sampling
Here are the steps to follow in doing this technique:
1. Number all the participants in the population from 1 to N (N is the total population).
2. Compute for the sample size.
3. Divide the population to the desired sample size (population ÷ sample size = number
interval).
4. Randomly pick a number between 1 to the value you obtain from step 3.
5. Start counting from the number you get in step 4 using the interval you get from step 3.

27. Systematic Random Sampling.

28. Example
• For instance, the population size of your study is 500 and you come up with 100
as your sample size. You decided to use the systematic sampling technique in
your study.
• Step 3: 500 ÷ 100 = 5 (your interval will be every 5th in the list);
• Step 4: The number you obtain in first step is 5, then you must choose one
number from 1 to 5 as your starting point. Let us say you choose number 3.
• Step 5: Since you choose number 3, then you are going to start counting from
number 3 and follow by every 5th in the list. Therefore, your respondents will be
the students listed as number 3, 8, 13, 18, 23… until it reaches the maximum
number which is 500.

29. 3. Stratified Random Sampling.
• The population is divided into groups called strata and
then simple random sampling is applied in selecting
samples from each group. This is the best random
sampling method when the researcher wishes to have a
representative sample of population.

31. Example
• The target population of the researcher is 1200 junior high school students with the
desired sample size of 800. The researcher gets the number of students per level. And
then divide each number of students per level by the total population of 1200 and
multiply by the desired sample size of 300. Using the illustration below, it clearly defines
that the researchers would get 233 respondents from 1st year high school,
200 respondents from 2nd year high school, 187 respondents from 3rd year high school,
and 180 respondents from 4th year high school.

32. 4. Cluster Sampling
• The largest scale surveys used the cluster sampling method. It is
used when the target respondents in a research study are
spread across a GEOGRAPHICAL LOCATION. In this method,
the population is group into what we called CLUSTER. Simple
random sampling is used in selecting the cluster.

34. Example
• Mr. X wants to explore the performance of LGU
employees across various parts of NCR. Mr. X creates 16
clusters of LGU units. He then randomly selects 5
clusters to conduct his research. And all the employees
of the selected clusters are included in the study.

35. 5.. Multi-Stage Sampling.
•This refers to the procedure as in cluster sampling
which moves through a series of stages.

37. Example
• An organization intends to conduct a survey to analyze the
performance of smartphones across Philippines. They
divided the entire country’s population into cities (clusters)
and randomly selected five cities out of all the cities. And
then the organization randomly picks only one barangay in
each city and filters all the people in each selected
barangay who use smartphones.