business methodlogy

1. Population and
Method of Sampling
A s s o c . P r o f D r . N a z a t u l F a i z a h H a r o n

2. Agenda
Overview
01
02
03
What is the Target
Population of the Study?
What is the Unit of
Analysis?
Method of Sampling

3. Target Population of the Study
The target population is the entire population, or group, that
a researcher is interested in researching and analysing. A
sampling frame is then drawn from this target population.
For example, if the research was to identify approximately
how many parents read a particular article in their child’s
school newsletter, the target population would be all parents
of children at the school.
The target units would then be the individual parents, and the
school could provide a list of parent contact details which
would serve as a sampling frame.

4. Sampling
Sampling may be defined as the selection of some part of the
population on the basis of which a judgement or inference
about the entire population is made.

5. Why
Sampling?
The population is dynamic, i.e. the component
of the population could change over time.
Thus, it is practically impossible to check all
items in the population.
The cost of studying the entire population
could be very high. A sample study is usually
less expensive than a census study.
Contacting the whole population would often
be time-consuming. Sampling can save time
as the results can be produced at a relatively
faster speed.

6. Determining
the sample
size
Factors affecting decisions on sample size as:
1. The research objective.
2. The extent of precision desired (the confidence
interval).
3. The acceptable risk in predicting that level of
precision (confidence level).
4. The amount of variability in the population
itself.
5. The cost and time constraints.
6. In some cases, the size of the population itself.

7. Defining Basic Terminology
Each member of the population is known as element.
The total number of elements in the population is
known as population size and it is denoted by “N”.
The sample is the subset of the population. Each
member of the sample is known as subject. The total
number of subjects in the sample is known as sample
size and it is denoted by “n”.
Population, Element and Population Size

8. Defining Basic Terminology
The characteristics of the population are known as
parameters whereas the characteristics of the sample
are known as statistics.
Parameter and Statistics
Sampling Frame
It is a complete listing of the population of interest
from which the sample is drawn. All members of the
sampling frame have a probability of being selected.

9. Sampling Techniques
Sampling is the process of selecting a sufficient
number of elements from the population, so that a
study of the sample and understanding of its
properties or characteristics would make it possible for
us to generalize such properties or characteristics to
the population elements.

10. Sampling Techniques
Simple random sampling 
Stratified sampling 
Systematic sampling 
Cluster sampling 
Probability Sampling Techniques

11. Sampling Techniques
Every element in the population has a known and equal chance of
being selected as a subject.
Random sampling is a fundamental sampling technique where each
member of a population has an equal chance of being selected. It
ensures that the sample is unbiased and representative of the entire
population, making it a key method in research for collecting data
that can be generalized.
Simple random sampling

12. Sampling Techniques
There are different types of random sampling techniques, such as:
1.Simple Random Sampling: Each individual is selected purely by chance.
2. Systematic Sampling: The researcher selects every "nth" individual after randomly
choosing a starting point.
3. Stratified Random Sampling: The population is divided into subgroups (strata),
and individuals are randomly selected from each group to ensure representation.
4. Cluster Sampling: The population is divided into clusters, and entire clusters are
randomly selected.
Simple random sampling

13. Example: Let’s say you are conducting a study on students' academic
performance at your university. You want to survey 100 students from a
total population of 1,000 students.
Simple Random Sampling Example: You could assign a unique number to each of the
1,000 students, and then use a random number generator to select 100 of them.
Systematic Sampling Example: If the list of students is alphabetized, you could randomly
pick a starting point (e.g., the 7th student) and then select every 10th student from there.
Stratified Random Sampling Example: Suppose you know that 60% of the students are
female and 40% are male. You could divide the population into two groups (male and
female) and randomly select 60 females and 40 males to ensure gender representation
in your sample.
Cluster Sampling Example: If students are grouped by dormitories, you could randomly select 5
dormitories and survey all students within these dormitories.

14. Advantages of probability sampling:
1.Unbiased Selection:
Since every individual in the population has an equal or known chance of being
selected, probability sampling minimizes bias, leading to more objective and
accurate results.
2.Representative of the Population:
Probability sampling ensures that the sample is representative of the entire
population, making the findings more generalizable to the population as a
whole.
3.Allows for Statistical Inference:
Because the sample is randomly selected, researchers can apply statistical tests
and confidently make inferences about the population from the sample data.

15. Advantages of probability sampling:
4.Minimizes Sampling Error:
Random selection reduces the likelihood of sampling error, providing more
reliable and precise results. Larger probability samples generally result in smaller
margins of error.
5.Replicability:
Probability sampling techniques can be easily replicated in other studies, which
is important for verifying research findings.
6.Supports Advanced Data Analysis:
With random samples, researchers can use sophisticated statistical models (e.g.,
regression, hypothesis testing) to analyze the data and make robust conclusions.

16. Disadvantages of probability sampling:
1.Time-Consuming and Expensive:
Implementing probability sampling often requires significant time, effort, and
financial resources, especially when dealing with large, dispersed populations.
2.Difficult to Achieve in Large Populations:
For large, geographically dispersed populations, drawing a random sample can
be logistically challenging and impractical.
3.Complexity in Sampling Frame:
Probability sampling requires a complete and accurate sampling frame (a list of
all members of the population). In some cases, obtaining this list is difficult or
impossible.

17. Disadvantages of probability sampling:
4.Possibility of Non-Response:
Even though a random sample is selected, not all individuals may respond to the
study. Non-response can introduce bias and affect the representativeness of the
sample.
5.Limited Use in Exploratory Research:
In exploratory research, where the goal is to gather preliminary insights or
identify patterns, probability sampling may not be necessary and could even
hinder the research process due to its complexity and cost.
6.May Not Be Necessary in Small Populations:
When working with small or homogeneous populations, probability sampling
may not offer significant benefits over non-probability sampling, as the
differences within the population might be minimal.

18. Sampling Techniques
Convenient sampling 
Judgement sampling 
Quota sampling 
Snowball sampling
Non-probability Sampling

19. Sampling Techniques
Non-probability sampling refers to sampling techniques where not
every individual in the population has an equal or known chance of
being selected.
Non-probability sampling relies on the researcher's judgment or other
non-random criteria to choose participants.
This method is often used when random sampling is impractical or
when the research is exploratory in nature, where the aim is to gain
insights rather than generalize results to a larger population.
Non-probability Sampling

20. Sampling Techniques
Convenience Sampling: Participants are selected based on availability and willingness to take
part. It is the easiest and quickest method, often used in exploratory research.
1.
2. Purposive (Judgmental) Sampling: The researcher uses their judgment to select participants who
they believe will provide the most relevant information for the study.
3. Snowball Sampling: Current participants recruit future participants from among their
acquaintances. This method is useful when the population is hard to reach, such as in studies on
marginalized groups.
4. Quota Sampling: The researcher selects participants based on specific characteristics or quotas,
such as age, gender, or income level, to ensure representation of certain groups within the sample.
5. Self-Selection Sampling: Individuals volunteer to participate in the study. This is common in online
surveys or studies where participants sign up.
There are several types of non-probability sampling methods:

21. Examples of Non-Probability Sampling Methods:
Convenience Sampling: If you're conducting a survey on students’ study habits, you might choose to
survey students who are readily available in the library at a specific time, as they are convenient to
reach.
Purposive Sampling: If you're studying the effects of leadership on employee motivation, you might
specifically choose experienced managers with a proven track record of leading teams to participate in
your study.
Snowball Sampling: If you're conducting a study on drug addiction, you could start with a small group
of addicts and then ask them to recommend other addicts they know, thereby growing your sample
through referrals.
Quota Sampling: Let’s say you are doing a market survey and want your sample to reflect the general
population’s demographics. You might decide to include a specific number of males and females or
participants from different income brackets to ensure these groups are represented.
Self-Selection Sampling: In an online survey about customer satisfaction, you might post a link on a
company’s social media page and invite customers to voluntarily participate in the study. Only those
who are interested will take part.

22. Advantages of Non-Probability Sampling Methods:
Quick and Easy: Non-probability sampling is less time-consuming and cheaper than probability
sampling.
Flexible: Researchers can use their judgment to target specific populations, making it easier to study
specialized groups.
Useful in Exploratory Research: Helps to generate insights, hypotheses, and theories in early stages of
research.
Disadvantages of Non-Probability Sampling Methods:
Bias: Since it doesn’t rely on random selection, there is a higher risk of bias, making the results less
generalizable.
Lack of Representativeness: The sample may not be representative of the population, limiting the
ability to generalize findings.
Subjective Selection: The researcher’s judgment or convenience may introduce subjectivity into the
process.

23. Determining the Sample Size
Precision refers to how close our estimate is to the
true population characteristics. We estimate the
population parameter to fall within a range based on
the sample estimate.
Level of Precision

24. Determining the Sample Size
The smaller the dispersion or variability, the greater
the probability that the sample mean will be closer to
the population mean.
Variability in Data
Level of Confidence
The level of confidence denotes how certain we are
that our estimates will really hold true for the
population.

25. Determining the Sample Size
Precision and confidence play a vital role in sampling as we use
sample data to draw inferences about the entire population.
Sample Data, Precision and Confidence in Estimation
Sample Size
Let us suppose, the Branch Manager of the Shah Alam branch of CIMB
bank wants to be 95% confident that the expected monthly
withdrawals in the bank will be within a confidence interval of ±RM400.

26. Factors Affecting the Decision on
Sample Size
Sample size 
Where samples are to be broken
into sub-samples 
Type of analysis employed (e.g.
multivariate analysis)

27. Types of Error in Business Research
Sampling Error—This is the error which occurs due to the
selection of some units and nonselection of other units
into the sample. 
Non-sampling Error—This is the effect of various errors
made by the interviewers, data entry operator or the
researcher himself when doing the sampling.

28. Thank You