Sample selection methods depend heavily on the research goals and the characteristics of the population being studied. The two primary approaches are probability and non-probability sampling.
Probability Sampling (Random Sampling)
Probability sampling, also known as random sampling, ensures every member of the population has a known, non-zero chance of being selected. This reduces bias and allows for generalizations to the larger population. Several techniques exist within probability sampling:
- Simple Random Sampling: Each member is assigned a number, and numbers are randomly selected. This is the most basic form.
- Stratified Random Sampling: The population is divided into subgroups (strata), and random samples are taken from each stratum. This ensures representation from all subgroups.
- Cluster Sampling: The population is divided into clusters (e.g., geographical areas), and some clusters are randomly selected for sampling. This is efficient for large populations spread across a wide area.
- Systematic Sampling: Every kth member of the population is selected after a random starting point. This is simple to implement but requires a well-ordered population list.
Non-Probability Sampling
Non-probability sampling does not give every member of the population a known chance of selection. This introduces potential bias but can be useful in specific situations, such as exploratory research or when accessing the entire population is difficult. Examples include:
- Convenience Sampling: Selecting participants readily available, such as those who volunteer or are easily accessible. This is prone to bias.
- Quota Sampling: Researchers select a sample that mirrors the characteristics of the population (e.g., age, gender). However, selection within each quota isn't random.
- Purposive Sampling: Researchers intentionally select participants based on specific criteria relevant to the research question. This is useful for in-depth case studies.
- Snowball Sampling: Participants recruit other participants, useful when studying hard-to-reach populations.
As mentioned in the provided reference, the fundamental difference lies in the use of randomization. Probability sampling utilizes randomization to minimize bias and enable generalizations, whereas non-probability sampling methods employ other selection criteria. The choice of sampling method depends significantly on the research objectives and resources available.
