Name the sampling design in which cases are selected on the basis of their ability to contribute to theory development by disproving or elaborating the theory:

Name the sampling design in which cases are selected on the basis of their ability to contribute to theory development by disproving or elaborating the theory: 1. Probability Sampling 2. Theoretical Sampling 3. Multi-stage Sampling 4. Cluster Sampling

The correct answer is (2) Theoretical Sampling.

Theoretical sampling is a non-probability sampling design in which cases are selected on the basis of their potential to contribute to theory development. The goal of theoretical sampling is to select cases that will help the researcher to refine, disprove, or elaborate on a particular theory.

Theoretical sampling is often used in qualitative research, but it can also be used in quantitative research. When using theoretical sampling, the researcher will typically start with a small sample of cases that are theoretically relevant. As the researcher learns more about the data, they will select additional cases that can help them to further develop their theory.

Here is an example of theoretical sampling:

A researcher is interested in developing a theory about the factors that contribute to homelessness. They might start with a small sample of homeless individuals who are living in a particular city. As the researcher learns more about the experiences of these individuals, they might select additional cases that have different experiences of homelessness. For example, the researcher might select cases who have been homeless for different lengths of time, cases who have different mental health histories, and cases who have different socioeconomic backgrounds.

By selecting cases on the basis of their theoretical relevance, the researcher can hope to develop a theory that is more comprehensive and nuanced than a theory that is based on a random sample of cases.

The other sampling designs listed in the question are not specifically designed for theory development:

• Probability sampling: Probability sampling is a sampling design in which every member of the population has a known chance of being selected for the study. Probability sampling is often used in quantitative research to ensure that the sample is representative of the population.
• Multi-stage sampling: Multi-stage sampling is a sampling design in which the population is divided into smaller and smaller groups until the desired sample size is reached. Multi-stage sampling is often used in quantitative research to reduce the cost and time of data collection.
• Cluster sampling: Cluster sampling is a sampling design in which groups of individuals (clusters) are selected from the population instead of individuals. Cluster sampling is often used in quantitative research when it is difficult or expensive to sample individuals directly.