What are the characteristics of probability sampling?

Characteristics of probability sampling:

(1) Simple random sampling:

1. Simple random sampling can be divided into two methods: simple random sampling with substitution and simple random sampling without substitution.

2. Simple random sampling is the most basic random sampling method.

(2) stratified sampling:

1. stratified sampling: it refers to dividing people into different layers according to certain rules, and then randomly sampling samples at different layers, so the samples obtained are called stratified samples.

2. Features:

① Stratified sampling can estimate not only the overall parameters, but also the parameters of each layer.

(2) Organization convenient for sampling work: draw sampling boxes in layers.

(3) Each layer should extract a certain sample unit, so that the samples are evenly distributed in the population, which can reduce the sampling error.

(3) Systematic sampling:

1. System sampling: it refers to arranging all the units in the population in a certain order, randomly extracting an initial unit within a specified range, and then extracting other sample units according to the pre-specified rules. The simplest systematic sampling is equidistant sampling.

2. Features

The advantages of (1) system sampling are:

The operation is simple, because only the starting unit needs to be randomly determined, and the whole sample is naturally determined.

② The requirement for sampling frame is relatively simple: only the whole population is required to be arranged in a certain order.

(2) The disadvantage of systematic sampling is the complexity of variance estimation, which brings some difficulties to the calculation of sampling error.

(4) Cluster sampling:

1. Cluster sampling: all basic units in a group are divided into non-overlapping groups according to certain rules. When sampling, select groups directly, investigate all basic units for the selected groups, and do not investigate the unselected groups.

2. Features:

(1) Advantages of cluster sampling:

① The investigation is convenient, which can save cost and time.

(2) The sampling frame is simplified, and only the sampling frame of the group is needed, instead of all the sampling frames of the basic unit.

(2) The main disadvantage of cluster sampling is that the sampling error is relatively large.

(5) Multi-stage sampling:

Sampling methods that have gone through two or more sampling stages. In reality, sampling design is often a combination of various sampling methods.

For example, two-stage sampling, the first stage adopts stratified random sampling, and the second stage adopts systematic sampling. For another example, when stratified sampling is adopted, different sampling methods can be adopted for different layers, some layers adopt simple random sampling, and some layers adopt systematic sampling.