The document provides an overview of sampling in research, defining it as the selection of a subset from a larger population for easier analysis. It discusses the importance of sampling, types including probability and non-probability sampling, and their respective merits and demerits. Understanding these sampling methods is essential for enhancing research quality and reliability.

1. Understanding Sampling in
Research
• Definition, Types, Merits, and Demerits
• Your Name
• Date

2. Introduction to Sampling
• Sampling is the process of selecting a subset
of individuals or items from a larger
population to make inferences about the
whole population.

3. Importance of Sampling
• Sampling is essential in research for cost-
effectiveness, time-saving, and feasibility
when studying large populations.

4. Types of Sampling
• Overview of Types:
• - Probability Sampling
• - Non-Probability Sampling

5. Probability Sampling
• Definition: Every member of the population
has a known chance of being selected.
• Examples:
• - Simple Random Sampling
• - Stratified Sampling
• - Cluster Sampling
• - Systematic Sampling

6. Simple Random Sampling
• Definition: A method where every individual
has an equal chance of being selected.
• Example: Drawing names from a hat to select
participants for a study.
• Merits: Reduces selection bias; easy to
analyze.
• Demerits: Requires a complete list of the
population; not always practical.

7. Stratified Sampling
• Definition: The population is divided into
subgroups (strata), and random samples are
taken from each stratum.
• Example: Surveying students from different
grades in a school.
• Merits: Ensures representation of all
subgroups; increases precision.
• Demerits: More complex; requires knowledge
of population strata.

8. Cluster Sampling
• Definition: The population is divided into
clusters, and entire clusters are randomly
selected.
• Example: Selecting entire schools in a district
for a study on education quality.
• Merits: Cost-effective; easier to administer.
• Demerits: May lead to higher sampling error if
clusters are not homogeneous.

9. Systematic Sampling
• Definition: Selecting every nth member from a
list after a random start.
• Example: Surveying every 10th person in a line
at a grocery store.
• Merits: Simple to execute; ensures spread
across the population.
• Demerits: Risk of bias if the list has a pattern.

10. Non-Probability Sampling
• Definition: Not all individuals have a chance of
being selected; relies on the subjective
judgment of the researcher.
• Examples:
• - Convenience Sampling
• - Judgmental Sampling
• - Snowball Sampling
• - Quota Sampling

11. Convenience Sampling
• Definition: Sampling based on ease of access.
• Example: Surveying people in a mall because
they are readily available.
• Merits: Quick and easy to gather data.
• Demerits: High risk of bias; not representative
of the population.

12. Judgmental Sampling
• Definition: The researcher selects subjects
based on their judgment and knowledge.
• Example: Choosing experts in a field for
interviews.
• Merits: Useful for exploratory research; saves
time.
• Demerits: Highly subjective; may introduce
bias.

13. Snowball Sampling
• Definition: Existing study subjects recruit
future subjects from among their
acquaintances.
• Example: Researching a hidden population,
like drug users.
• Merits: Effective for hard-to-reach
populations; builds rapport.
• Demerits: Can lead to a non-representative
sample; potential bias.

14. Quota Sampling
• Definition: The researcher ensures equal
representation of certain characteristics in the
sample.
• Example: Surveying 50 males and 50 females
regardless of population proportions.
• Merits: Ensures specific characteristics are
represented.
• Demerits: Risk of bias; may not represent the
population accurately.

15. Conclusion
• Sampling is a crucial aspect of research, with
various methods each having unique
advantages and disadvantages. Understanding
these can enhance research quality and
reliability.