UGC -NET JRF SET | Paper 1 | Research Aptitude | Types of Sampling | One Shot Revision | Kanchan Solani

 

Sampling

Sampling is a methodological process in research where a subset of individuals or items (known as a sample) is selected from a larger group or population to represent that population. The primary objective of sampling is to make inferences about the population characteristics based on the analysis of the sample. By using appropriate sampling techniques, researchers can ensure that the sample accurately reflects the diversity and properties of the entire population, enabling reliable and valid conclusions.

Sampling is essential for efficient data collection, especially when dealing with large populations, limited resources, or time constraints. It plays a crucial role in ensuring the generalizability and accuracy of the research findings.

Probability Sampling

Simple Random Sampling:

• Definition: Every member of the population has an equal chance of being selected. It is used for small homogeneous population.
• Example: Drawing names from a hat.

Systematic Sampling:

• Definition: Selecting every kth individual from a list of the population. It is also known as fixed interval method.
• Example: Choosing every 10th person on a list.

Stratified Sampling:

• Definition: Dividing the population into strata (subgroups) and randomly sampling from each stratum. It is used for Heterogeneous population. It is divided into a homogeneous group or strata and from each homogeneous group, a random sample is drawn. Stratified random sampling can be classified into the following:- (i)Proportionate stratified sampling (ii) Disproportionate stratified random sampling
• Example: Dividing a population by gender and selecting random samples from each group.

Cluster Sampling:

• Definition: Dividing the population into clusters, randomly selecting some clusters, and then sampling all members within those clusters. It is used in socio-economic surveys, public opinions polls, ecological studies, farm management services, rural credit services, demographic studies and large scale surveys of political and social behavior, attitude surveys etc. 
• Example: Dividing a city into districts and randomly selecting districts, then surveying everyone in the chosen districts.

Area Sampling: Area sampling in research is a method where the overall study area is divided into smaller, identifiable sub-areas, and then a sample of these sub-areas is selected for data collection. This approach is particularly useful when a complete list of population units (e.g., households, buildings) is not available, or when the population is geographically dispersed.

Multi-Stage Sampling:

• Definition: Combining several sampling methods, usually involving multiple stages of sampling.
• Example: Using cluster sampling to select districts, then using simple random sampling within selected districts.

Non-Probability Sampling

Convenience Sampling:

• Definition: Sampling individuals who are easiest to reach.
• Example: Surveying people in a shopping mall.

Judgmental (Purposive) Sampling:

• Definition: Selecting individuals based on the researcher’s judgment about who will provide the best information.

Example: Choosing experts in a field for a study.

Snowball Sampling:

• Definition: Participants recruit other participants from their acquaintance. Snow-ball sampling is a technique of building up a list or a sample of a special population by using an initial set of its members as informants 
• Example: Studying a hidden or hard-to-reach population like drug users.

Quota Sampling:

• Definition: Ensuring that the sample represents certain characteristics of the population.
• Example: Interviewing a specific number of people from different age groups.

Panel Sampling: Panel sampling in research methodology involves recruiting a specific group of individuals to participate in multiple research studies or surveys over time. This method allows researchers to track changes and trends in opinions, behaviours, or attitudes within a particular population, as they can repeatedly collect data from the same individuals. 

Technical Vocabulary and Keywords of Sampling :

• Population: The entire group of individuals or items that the researcher is interested in studying.
• Sample: A subset of the population selected for the study.
• Sampling Frame: A list or database from which the sample is drawn.
• Bias: Systematic error introduced by the sampling method that affects the validity of the results.
• Randomization: The process of making something random, in context, ensuring each member of the population has an equal chance of being selected.
• Strata: Subgroups within a population that share similar characteristics.
• Clusters: Groups within a population that can be sampled as a whole.
• Generalizability: The extent to which the results of a study can be applied to the broader population.