Sampling theory and provide examples to illustrate your definition

Describe sampling theory and provide examples to illustrate your definition. Discuss generalizability as it applies to nursing research.

Describe sampling theory and provide examples to illustrate your definition.

Sampling theory allows for the selection of a representative sample of the target population that can be analyzed in a more cost-effective and efficient manner. The two types of sampling are probability and non-probability sampling. Probability sampling involves random selection of the sample and this allows making strong statistical inferences about the entire group. On the other hand, non-probability sampling involves non-random selection of the sample and this allows easy collection of data. The probability sampling methods include systematic, simple random, cluster, and stratified sampling. In simple random sampling, all members of the population have equal chances to be selected for the study and systematic sampling involves selecting individuals in a sample at regular intervals (McCombes, 2023). On the other stratified sampling involves dividing the populations into small groups with different characteristics and cluster sampling involves dividing populations into groups of similar characteristics and selecting the group to study. The non-probability sampling methods are purposive, convenience, and snowball sampling. Convenience sampling involves the selection of research participants that are easily accessible. Purposive sampling is also referred to as judgment sampling and it involves the researcher selecting a sample that has high levels of expertise. On the other hand, snowball sampling is where other research participants are used to recruit others (McCombes, 2023).

Discuss generalizability as it applies to nursing research.

Generalizability of research refers to extending the research findings and conclusions from the sample population to the entire population. Given that the generalizability of the research findings is based on the probability, it is not difficult to accept other data from the large population and this provides the best foundation for research generalizability (Yarkoni, 2022). Large samples selected from research make the results to be more generalizable. For example, if a large sample is used to examine the effectiveness of drugs in treating Type 1 Diabetes, the generalizability of research results is enhanced.

References

McCombes, S. (2023). Sampling Methods | Types, Techniques & Examples. Scribbr. https://www.scribbr.com/methodology/sampling-methods/#:~:text=Probability%20sampling%20involves%20random%20selection,you%20to%20easily%20collect%20data

Yarkoni, T. (2022). The generalizability crisis. Behavioral and Brain Sciences, 45, e1. https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/generalizability-crisis/AD386115BA539A759ACB3093760F4824