![]() The main alternative to random sampling is quota sampling. This involves specifying required sub-samples, and obtaining these in a cost-effective way (e.g., obtaining 50 males under 30, 50 females under 30, 50 males 30 or older, and 50 females 30 or older). It is common practice to use as much randomization as possible when employing these techniques, in the hope that the resulting sample approximates the qualities of a random sampling. Specific types of non-random sampling include quota sampling, convenience sampling, volunteer sampling, purposive sampling, and snowball sampling. A sample that is not a random sample is known as a non-random or non-probability sample. For example, in surveys involving humans, it is usually not practical to contact most people, let alone to compel them to participate if randomly selected.Ĭonsequently, many alternatives exist to random sampling. Although the concept of random sampling is central to much of statistical theory, in practice it is rare. ![]() Sampling refers to the process of selecting a sample. If the researcher commits mistakes in allotting sampling fractions, a stratum may either be overrepresented or underrepresented which will result in skewed results.A random sample is a subset of individuals selected at random from a larger population, where each individual in the population has a known and non-zero chance of being chosen. The precision of this design is highly dependent on the sampling fraction allocation of the researcher. With disproportionate sampling, the different strata have different sampling fractions. The only difference between proportionate and disproportionate stratified random sampling is their sampling fractions. Disproportionate Stratified Random Sampling It is much like assembling a smaller population that is specific to the relative proportions of the subgroups within the population. The important thing to remember in this technique is to use the same sampling fraction for each stratum regardless of the differences in population size of the strata. ![]() Then, the researcher must randomly sample 50, 100 and 150 subjects from each stratum respectively. And the researcher chose a sampling fraction of ½. This means that the each stratum has the same sampling fraction.įor example, you have 3 strata with 100, 200 and 300 population sizes respectively. ![]() The sample size of each stratum in this technique is proportionate to the population size of the stratum when viewed against the entire population. Types of Stratified Sampling Proportionate Stratified Random Sampling This is because the variability within the subgroups is lower compared to the variations when dealing with the entire population.īecause this technique has high statistical precision, it also means that it requires a small sample size which can save a lot of time, money and effort of the researchers. With this technique, you have a higher statistical precision compared to simple random sampling.This allows the researcher to sample the rare extremes of the given population. With stratified sampling, the researcher can representatively sample even the smallest and most inaccessible subgroups in the population.With a simple random sampling technique, the researcher is not sure whether the subgroups that he wants to observe are represented equally or proportionately within the sample. Researchers also employ stratified random sampling when they want to observe existing relationships between two or more subgroups.This technique is useful in such researches because it ensures the presence of the key subgroup within the sample. Stratified random sampling is used when the researcher wants to highlight a specific subgroup within the population.
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