Probability Sampling Techniques

Probability Sampling

• A sampling technique where the selection of a sample from a larger population is guided by the theory of probability.
• The most important requirement is that every member of the population has an equal and known chance of being chosen.

Simple Random Sampling

• A straightforward method where individuals are assigned numbers, and these numbers are then randomly selected through automated processes, like lottery systems or random number generators.

Stratified Sampling

• Divides a larger population into smaller, mutually exclusive groups based on specific characteristics (e.g., sex, age, ethnicity).
• Simple random sampling is then used to select members from these groups.

Cluster Sampling

• Used when participants are geographically dispersed.
• Instead of selecting individuals directly, areas (e.g., cities, counties) are randomly chosen, and then participants are randomly selected from within those areas.
• Typically used when analyzing a population consisting of multiple elements (e.g., cities, families, universities).

Systematic Sampling

• Every nth individual is selected for the sample.
• For example, choosing every 5th person in a list.

Non-Probability Sampling

• A sampling technique where the researcher selects samples based on their subjective judgment rather than pure randomization.
• Not all members of the population have an equal chance of being included in the study.
• Most suitable for exploratory studies or when time or cost constraints make probability sampling impractical.

Convenience Sampling

• Selection of samples based solely on their availability and ease of access to the researcher.
• Researchers don't strive for representativeness of the entire population.
• Example: using student volunteers known to the researcher.

Quota Sampling

• Aims to create a sample that reflects the proportions of different subgroups within the population of interest.
• For example, if studying the career goals of employees in an organization, the sample should proportionally represent the number of male and female employees.

Judgmental/Purposive Sampling

• Samples are selected based on the researcher's expertise and judgment about who would be the most appropriate participants for the study.
• Used when time constraints are present, and researchers prefer their own knowledge over other methods.

Snowball Sampling

• Also known as chain-referral sampling.
• Used when the sample size is small and difficult to access.
• Example: studying a rare disease, where researchers rely on initial participants to refer other individuals with the same condition.