Final answer:
Techniques like cluster sampling and multistage sampling maintain external validity through their use of random selection, ensuring that each individual has an equal chance of being included in the sample. This randomized approach is the basis for a representative sample that mirrors the overall population.
Step-by-step explanation:
Cluster sampling and multistage sampling are just as externally valid as simple random sampling because they all contain elements of random selection. Simple random sampling gives each individual in the population an equal chance of being included in the sample, which is also a fundamental principle followed in both cluster and multistage sampling methods. This randomness in selection ensures that the sample does not display bias towards a certain group within the population, which could otherwise affect the external validity of the results.
With cluster sampling, the population is divided into clusters, and a random selection of clusters is chosen to be sampled. Within those clusters, data can be collected from all members or a random sample of members. Multistage sampling is similar but involves multiple layers of random sampling, such as selecting a random sample of clusters and then within those clusters, selecting a random sample of individuals.
Another important aspect is that external validity doesn't inherently rely on every member of the population being measured or on using large samples; rather, it depends on the method of sample selection. A randomized method of sample selection gives each potential participant an equal chance of being chosen, which is the key to achieving a sample that's representative of the population at large.