However, there are obviously times when one sampling method is preferred over the other. The following explanations add some clarification about when to use which method. Stratified sampling would be preferred over cluster sampling, particularly if the questions of interest are affected by time zone.
What's an example of stratified random sampling? By Steven Nickolas Updated April 23, — Simple random sampling is a sample of individuals that exist in a population ; the individuals are randomly selected from the population and placed into a sample. This method of randomly selecting individuals seeks to select a sample size that is an unbiased representation of the population.
However, it's not advantageous when the samples of the population vary widely. Stratified random sampling is also called proportional random sampling or quota random sampling.
Stratified random sampling divides a population into subgroups or strata, and random samples are taken, in proportion to the population, from each of the strata created. The members in each of the stratum formed have similar attributes and characteristics. This method of sampling is widely used and very useful when the target population is heterogeneous.
A simple random sample should be taken from each stratum. The research team has difficulty collecting data from all 21 million college students; it decides to take a random sample of the population by using 4, students.
Suppose it finds that students are English majors, 1, are science majors, are computer science majors, 1, are engineering majors, and are math majors. The team wants to use a proportional stratified random sample where the stratum of the sample is proportional to the random sample in the population.
Assume the team researches the demographics of college students in the U. Thus, five strata are created from the stratified random sampling process. The team then needs to confirm that the stratum of the population is in proportion to the stratum in the sample; however, they find the proportions are not equal.
The team then needs to resample 4, students from the population and randomly select English, 1, science, computer science, engineering, and mathematics students. With those, it has a proportionate stratified random sample of college students, which provides a better representation of students' college majors in the U.
The researchers can then highlight specific stratum, observe the varying studies of U.This article presents a discussion of mixed methods (MM) sampling techniques. MM sampling involves combining well-established qualitative and quantitative techniques in creative ways to answer research questions posed by MM research designs.
In sociology and statistics research, snowball sampling (or chain sampling, chain-referral sampling, referral sampling) is a nonprobability sampling technique where existing study subjects recruit future subjects from among their acquaintances.
Thus the sample group is said to grow like a rolling snowball. A sampling method where the researcher selects the research sample based on ease and proximity to the researcher. A sampling method where the sample is randomly selected to ensure unbiased results.
A sampling frame is a list or other device used to define a researcher's population of interest. The sampling frame defines a set of elements from which a researcher can select a .
Two general approaches to sampling are used in social science research. With probability sampling, all elements (e.g., persons, households) in the population have some opportunity of being included in the sample, and the mathematical probability that any one of them will be selected can be calculated.
Before sampling, the population is divided into characteristics of importance for the research. For example, by gender, social class, education level, religion, etc.
Then the population is randomly sampled within each category or stratum.