Non-Probability Sampling
Non-Probability-Sampling is a technique in Statistics and Research-Methodology where the researcher selects samples based on subjective judgment rather than random selection. Unlike Probability-Sampling, this approach does not provide every member of the population with a known or equal chance of being included. It is commonly utilized in Qualitative-Research and exploratory studies where the primary goal is to understand specific contexts rather than achieve statistical generalizability to a broad population.
Common Methodologies
Several specific methods are categorized under this sampling type:
- Convenience-Sampling: This involves collecting data from a population that is close at hand and easy to reach.
- Quota-Sampling: Researchers divide the population into subgroups and then non-randomly select participants until a specific proportion is met.
- Purposive-Sampling: Also known as judgmental sampling, the researcher uses their expertise to select a sample that is most useful to the purposes of the research.
- Snowball-Sampling: Often used for hard-to-reach populations, where initial participants help recruit additional participants from their social network.
The main challenge of Non-Probability-Sampling is the high risk of Sampling-Bias, which can lead to results that are not representative of the total population. Despite these limitations, these methods are often more cost-effective and faster to implement. Organizations like the Pew Research Center provide extensive analysis on the reliability of non-probability surveys in modern Social-Sciences. Further technical details on implementation can be found via Scribbr.