Please arrange four random sampling methods according to the sampling error.
1. Simple random sampling
Simple random sampling is a completely random method, which extracts some observation units from the population to form samples (that is, the probability of each observation unit being selected into the sample is the same). The common method is to number all the observation units in the population first, and then draw some observation units from them to form samples by lottery, random number table or computer-generated random number.
Its advantages are simple and intuitive, and the calculation of mean (or rate) and its standard error is simple and convenient; The disadvantage is that when the group is large, it is difficult to number the individuals in the group one by one, and the samples taken are scattered, so it is difficult to organize the investigation.
2. Systematic sampling
Systematic sampling is also called equidistant sampling or mechanical sampling, that is, all the individuals in the group are sorted and numbered according to the characteristics unrelated to the research phenomenon; Then, according to the sample content, the sampling interval k is specified; Randomly select the first i(i
The advantages of systematic sampling are: easy to understand, simple and easy to operate; It is easy to get a uniformly distributed sample in the population, and its sampling error is less than that of simple random sampling. Disadvantages are: the samples are scattered and it is difficult to organize the investigation; When the observation unit as a whole shows a periodic trend or a monotonous increasing (decreasing) trend, it is easy to produce bias.
3. Nested sampling method
Cluster sampling is to divide the population into K "groups", each group contains several observation units, and then randomly select K groups (K
The advantages of cluster sampling are that it is convenient to organize investigation, save money and control the quality of investigation; The disadvantage is that the sampling error is greater than that of simple random sampling when the sample content is constant.
4. Stratified sampling
Stratified sampling is to divide all individuals in the population into several "layers" according to some characteristics that have great influence on the main research indicators, and then randomly select a certain number of observation units from each layer to form samples.
The advantage of stratified random sampling is that the sample is representative and the sampling error is small. After stratification, different sampling methods can be adopted for different layers according to specific conditions.
The sampling errors of the four sampling methods are generally: cluster sampling ≥ simple random sampling ≥ systematic sampling ≥ stratified sampling.
In actual investigation and study, two or more sampling methods are often combined for multi-stage sampling.