Sampling: the process of extracting individuals or samples from the population, that is, the process of testing or observing the population. There are two kinds of random sampling and non-random sampling.
Extended data:
1. Random sampling: a sampling method that takes samples from the population according to the principle of randomization, without any subjectivity, including simple random sampling, systematic sampling, cluster sampling and stratified sampling.
1, simple random sampling: the most basic sampling method. Divided into repeated sampling and non-repeated sampling. In repeated sampling, the units extracted each time still return to the population, and the units in the sample may be extracted several times. In non-repeated sampling, the extracted units will not be put back into the population, and the units in the sample can only be extracted once. Non-repeated sampling was used in social survey.
The specific methods of pure random sampling are as follows: ① Draw lots. ② Random number table method.
2. Stratified sampling: firstly, the population is divided into several sub-populations according to one or several characteristics, and each sub-population is called a layer; Then randomly select a sub-sample from each layer, and these sub-samples add up to the total sample.
There are three methods to determine the number of samples in each layer: ① Stratification ratio. That is to say, the ratio of the number of samples in each layer to the total number of layers is equal. ② Naiman method. That is to say, the number of samples to be sampled in each layer is directly proportional to the product of the total number of the layer and its standard deviation. ③ Non-proportional distribution method.
3. Systematic sampling: also known as equidistant sampling, it is a variant of pure random sampling. In system sampling, the population is numbered from 1 ~ n, and the sampling distance K=N/n is calculated. Where n is the total number of units and n is the sample size. Then extract a random number k 1 ~ K as the first unit of the sample, and then take k 1+k, K 1+2k ... until enough n units are drawn.
4. Cluster sampling: also known as cluster sampling. Firstly, the crowd is divided into groups according to certain standards, each group is a sampling unit, and several groups are randomly selected to investigate all units in the selected sample group.
2. Non-random sampling: a method of sampling according to the opinions, experience or relevant knowledge of researchers, which is obviously subjective.
Third, representativeness: the basic requirements of sampling. Similarity between sample and matrix. The more similar the main features of the two, the stronger the representativeness of the sample to the matrix, and the more reliable the result of inferring the matrix from the sample. ?