Research Methods in Sampling

Sampling and Population

• A representative sample is crucial for valid research findings.
• Populations consist of all subjects under study, which can include varied entities like hospitals or schools.
• A sample is a subgroup selected from a population for analysis.
• Populations can be categorized into two groups: Target population and Accessible population.

Target vs. Accessible Population

• Target population: The entire group to which a researcher seeks to generalize findings (e.g., all institutionalized individuals with psychiatric issues).
• Accessible population: The actual group available for study, such as pregnant women in a specific clinic.

Sample Selection

• A sample represents the population and aids in making generalizations about the larger group.
• Conducting a census includes studying every member of a population.

Advantages of Sampling

• Reduces costs and manpower requirements.
• Facilitates quicker data collection.
• Allows for more comprehensive data and potentially greater accuracy.

Sampling Methods

• Two main sampling procedures: Probability sampling and Non-probability sampling.

Probability Sampling

• Involves random selection, ensuring each member of a population has a chance to be included.
• Aims to examine representative elements of populations.
• Types of probability sampling include Simple Random Sampling, Systematic Random Sampling, Stratified Random Sampling, Cluster Random Sampling, and Multistage Sampling.

Simple Random Sampling (SRS)

• Every member has an equal chance of selection.
• Requires a sampling frame (list of the population) with unique identifiers.
• Selection methods include shuffling cards or using random number generators.

Systematic Random Sampling

• Involves selecting members based on a calculated sampling proportion (e.g., 1 in 10).
• Begin with a random starting point and select every nth individual.

Stratified Random Sampling

• Best for heterogeneous populations with significant variability.
• The population is divided into strata, and simple random samples are drawn from each to ensure representation.

Multistage Random Sampling

• Utilizes multiple stages for selection, ideal for entire communities (e.g., regions to households).
• Each stage involves random sampling on different levels.

Cluster Random Sampling

• Effective for geographically dispersed populations.
• Clusters may be formed by drawing samples from broader regions, then narrowing down to specific groups.

Conclusion

• Proper sampling techniques are vital in research to ensure that findings reflect the true characteristics of a population.