SAMPLE-AND-SAMPLING-TECHNIQUE
Introduction to Sampling
Definition: A population is a group of individuals or objects sharing a common trait or characteristic.
Purpose of Sampling: Researchers collect data from a population to study and make conclusions about the target population.
Types of Population Samples
Sample Population
Concept: The sample population is a subset of the population that is studied to represent the entire population.
Sampling Techniques
Types of Sampling Procedures
Deliberate Sampling
Simple Random Sampling
Systematic Sampling
Stratified Random Sampling
Cluster Sampling
Non-Probability Sampling
Also referred to as Deliberate/Purposive Sampling.
Characteristics: Samples are predetermined by researchers based on convenience, making it easier to gather data.
Limitations: This method is highly susceptible to sampling biases.
Convenience Sampling
Definition: A sample that includes individuals who are easy to reach, such as asking people if they have pets at home.
Simple Random Sampling (SRS)
Definition: An unbiased sampling method where every individual in the population has an equal chance of being selected.
Method: Often involves a lottery method.
Systematic Sampling
Concept: Involves selecting samples based on a fixed, periodic interval.
Procedure:
Determine Population Size (N) and desired number of samples (n).
Calculate Interval Size (k): Divide N by n.
Select a Random Start to pick the first sample.
Select Subsequent Samples: Choose every kth member of the population.
Stratified Random Sampling
Purpose: Used to create representative samples from heterogeneous populations by grouping into strata based on similar characteristics.
Types: Can be proportionate (sizes of groups reflected in samples) or disproportionate (different sample sizes taken from various groups).
Cluster Sampling
Definition: Subjects are grouped into smaller subpopulations or clusters based on specific factors or existing groupings.
Application: Allows for easier management of the subjects being sampled.