Comparison of Simple Random, Stratified, Quota and Area Sampling Methods
Simple random sampling (SRS) is a probability sampling procedure that gives every element in the target population, and each possible sample of a given size, an equal chance of being selected. As such, it is an equal probability selection Method (Daniel, 2011).
Eg: Selection of 5 cards randomly from a pack of cards
Stratified sampling is a probability sampling procedure in which the target population is first separated into mutually exclusive, homogeneous segments (strata), and then a simple random sample is selected from each segment (stratum). The samples selected from the various strata are then combined into a single sample (Daniel, 2011).
Eg: Selection of 30 respondents each, randomly from low, medium and high income earning households in Organization X of Texas.
An area probability sample is type of cluster sampling in which geographic areas are sampled with known probability. In survey research an area probability sample is usually one in which areas are selected as part of a clustered or multi-stage design (Scheaffer et al.,2011)
Eg: Selection of 500 students from District X of the State Y by grouping district into small block and then drawing each block randomly. Then divide selected blocks into sub blocks and continue progressive blocking till you get school as randomly selecting unit.
Quota sampling is a method in which fixed proportion of respondents are drawn from two or more strata of given sample in non-random manner
Eg: Selection of 30 male and 30 female employees who first enter into canteen of the office.
Differences between selected sampling methods
In case of SRS, Samples are drawn without dividing the population into strata. Whereas in case of Stratified random sampling, Quota sampling and Area probability sampling; samples are drawn only after samples are drawn only after grouping of the respondents into different strata, and in case of area sampling, samples are drawn only after grouping of the respondents into clusters.
SRS method is more effective when population is more homogenous, whereas rest of the three sampling methods is more effective when the population is relatively more heterogeneous.
Next major difference between these sampling methods is that, in case of Stratified random sampling and quota sampling it requires each stratum to be as homogenous as possible internally and two strata should be as heterogeneous as possible. Whereas in case of cluster sampling it is desirable to have each cluster as heterogeneous as possible internally and two clusters should be as homogenous as possible (Sukhatme, 1957).
SRS requires no advanced auxiliary knowledge about the respondents in the population, whereas rest of the three sampling methods demands the better knowledge about the population. Additionally sampling methods like SRS and stratified sampling require a well defined sampling frame whereas methods like quota sampling and cluster sampling do not require a well-defined sampling frame (Daniel, 2011).
One of the advantages of SRS is that it eliminates selection bias whereas rests of the methods suffer from various degree of selection bias. But Simple random sampling generally have larger sampling errors whereas Stratified and cluster samples generally yield relatively smaller random sampling errors. Another limitation associated with SRS is that it doesn’t facilitate inter-group inferences and comparisons across strata, whereas methods like quota sampling, stratified sampling and area sampling facilitate inter-subgroup comparison
Daniel, J. (2011). Sampling essentials: Practical guidelines for making sampling choices. Sage Publications.
Scheaffer, R., Mendenhall III, W., Ott, R., & Gerow, K. (2011). Elementary survey sampling. Cengage Learning.
Stopher, P. R., & Meyburg, A. H. (1979). Survey sampling and multivariate analysis for social scientists and engineers (pp. 263-258). Lexington, Massachusetts: Lexington Books.
Sukhatme, P. V. (1957). Sampling theory of surveys with applications.