Part B (Q6): What do you mean by Sampling? Discuss its various Types.

Sampling is the process of selecting a subset of individuals, cases, or units from a larger population to study. Because it is usually impossible, too costly, or too time-consuming to study an entire population (a census), researchers study a sample and then generalize the findings back to the whole population.

Types of Sampling

Sampling methods are broadly divided into two categories: Probability and Non-Probability Sampling.

A. Probability Sampling

In this type, every member of the population has a known, non-zero chance of being selected. This allows for statistical generalization and reduces researcher bias.

1. Simple Random Sampling: Every individual has an equal chance of being chosen, like drawing names from a hat or using a random number generator.
2. Systematic Random Sampling: The researcher selects a starting point at random and then selects every nth element from the population list (e.g., choosing every 10th person on a voter list).
3. Stratified Random Sampling: The population is divided into distinct subgroups (strata) based on a specific characteristic (e.g., gender, income level). Then, a simple random sample is drawn from each stratum. This ensures minority groups are adequately represented.
4. Cluster Sampling: The population is divided into naturally occurring clusters (e.g., schools, city blocks). A random sample of clusters is selected, and all individuals within those chosen clusters are studied. Useful for widely dispersed populations.

B. Non-Probability Sampling

Selection is based on the subjective judgment of the researcher, not random chance. It is often used in exploratory or qualitative research where statistical generalization is not the primary goal.

1. Convenience Sampling: Selecting individuals who are simply easiest to reach (e.g., surveying people as they walk out of a specific clinic). Highly prone to bias but quick and cheap.
2. Purposive (Judgmental) Sampling: The researcher uses their expert judgment to select individuals who are specifically relevant to the study's purpose (e.g., a social worker specifically selecting single mothers who have experienced domestic violence for a study on resilience).
3. Snowball Sampling: Used for hard-to-reach or hidden populations (e.g., undocumented immigrants, drug users). The researcher finds a few individuals, interviews them, and then asks them to recommend others from their network.
4. Quota Sampling: Similar to stratified sampling, the researcher ensures certain subgroups are represented in specific proportions (quotas). However, unlike stratified sampling, the selection within those quotas is not random; it is usually done by convenience.