RM2

• Sampling is the process of selecting a subset of individuals or items from a larger population in order to make inferences about the entire population.
• Sampling is the fundamental aspect of research across various fields, allowing researchers to study a manageable portion of the population.
• Probability Sampling are characterized by the fact that every member of the population has a known chance of being selected for the sample.
• Probability Sampling rely on random selection processes ensuring that each member of the population has equal opportunity to be included in the sample.
• Probability Sampling methods are often preferred in research because they provide a basis for statistical inference and allow researches to estimate sampling error.
• Non-probability Sampling do not involve random selection processes.
• In non-probability Sampling, individuals or items are chosen based on factors other than probability, such as convenience or judgement.
• Non-probability sampling methods are often more practical and easier to implement, they may introduce bias into the sample and limit the generalizability of the findings to the larger population.
• Random Sampling involves selecting individuals from a population in such a way that each member has an equal chance of being chosen.
• Random sampling is considered the gold standard in sampling as it helps ensure that the sample is representative of the population.
• In random sampling, every member of the population is assigned a number then a random process is used to select the sample.
• Systematic Sampling involves selecting every nth member from a population after a random starting point is chosen.
• Systematic Sampling is less time-consuming and easier to implement than simple random sampling.
• Stratified Sampling involves dividing the population into subgroups based on certain characteristics that are important to the research.
• Cluster Sampling dividing the population into clusters or groups and then randomly selecting entire clusters as the sampling units.
• Cluster Sampling is useful when its impractical or expensive to sample individuals directly.
• Convenience Sampling involves selecting individuals who are readily available and easily accessible to the researcher
• Convenience Sampling is based on convenience rather than random selection process.
• Convenience sampling is quick and easy to implement and is suitable for exploratory studies or when resources are limited.
• Convenience sampling is highly susceptible to selection bias because it may not represent the entire population and results may not be generalizable to the broader population.
• Snowball sampling is known as chain referral sampling.
• Snowball Sampling involves recruiting participants through referrals from initial participants.
• Snowball sampling is often used when the population of interest is difficult to reach or locate.
• Snowball sampling is useful for studying hard to reach populations and allow access to individuals who may not be identifiable through other sampling methods.
• Snowball sampling is subject for potential bias if participants refer others who are similar to themselves, and difficulty in estimating the sampling error and generalizing findings.
• Quota sampling is dividing the population into quotas based on certain characteristics and then selecting participants to fulfill those quotas.
• Quota Sampling ensures that the sample reflects the distribution of key characteristics in the population.
• Quota sampling allows researchers to ensure diversity in the sample and is more flexible and less time consuming than probability sampling methods.
• Quota sampling is subject for potential bias if quotas are not accurately representative of the population and it requires careful selection of quotas and monitoring to avoid oversampling or undersampling.