If the population is very dispersed, the costs for data collection may be higher than for other designs in the probability sample.Simple random sampling (also referred to as random sampling or method of chances) is the purest and the most straightforward probability sampling strategy. The statistical procedures needed to analyze data errors and statistics software are easier. If subgroups of the population have particular interests they can not be included with a sufficient number in the sample. It tends to produce representative samples. You may have larger sampling errors and less accuracy than other probabilistic sampling designs with the same sample size. It does not take advantage of the knowledge that the researcher could have of the population.Įasier to understand and communicate to others. In comparison with other probabilistic sampling proceduresĮach possible combination of sampling has an equal probability of being selected. In comparison with other probabilistic sampling procedures: Strengths and weaknesses of simple random sampling Advantages Simple random sampling may not produce a sufficient number of elements from small subgroups. This would not make simple random sampling a good option for studies that require a comparative analysis of the small categories of a population with much broader categories of the population. Respondents can be very dispersed, therefore, the costs of data collection may be higher than those of other probability sample designs, such as cluster sampling. Simple random sampling tends to have larger sampling errors and less stratified sampling precision of the same sample size. One of the most obvious limitations of simple random sampling is the need for a complete list of all members of the population. You must bear in mind that the list of the population must be complete and updated. This list is generally not available in large populations. In these cases, it is more prudent to use other sampling techniquesĪmong the disadvantages are that a sampling frame of elements of the target population is required. An appropriate sampling frame may not exist for the target population, and it may not be feasible or practical to build one. In this case, the sampling by conglomerates does not require a sampling of the elements of the target population. The statistical procedures required to analyze the data and calculate errors are easier than those required in other probabilistic sampling procedures. In general, it is easier than other probabilistic sampling procedures (such as conglomerate sampling) to understand and communicate to others. In order to draw conclusions from the results of a study, an impartial random selection and a representative sample are important. Remember that one of the objectives of the research is to draw conclusions regarding the population from the results of a sample. Due to the representativeness of a sample obtained by simple random sampling, it is reasonable to make generalizations from the results of the sample with respect to the population.Īmong its strengths are that it tends to produce representative samples and allows the use of inferential statistics in the analysis of collected data .Įach selection is independent of other selections All possible combinations of sampling units have the same opportunity to be selected. In systematic sampling, the possibilities of being selected are not independent of each other. One of the best things about simple random sampling is the ease of assembling the sample. It is also considered a fair way to select a sample from a population, since each member has equal opportunities to be selected.Īnother key feature of simple random sampling is the representativeness of the population. In theory, the only thing that can jeopardize its representativeness is luck. If the sample is not representative of the population, the random variation is called sampling error . Strengths and weaknesses of simple random sampling.Disadvantages of simple random sampling.
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