What are the methods of probability sampling? What are the main sampling methods of probability samp

1) Simple random sampling: n samples are directly taken from the total according to the principle of equal probability.

Advantages: easy to operate;

Disadvantages: There is no guarantee that the sample will be a perfect representation of the population;

Application: Scenarios where individuals are evenly distributed.

2) Equidistant sampling: First, each individual in the population is numbered sequentially, and then the sampling interval is calculated, and then the individuals are selected according to a fixed number.

Advantages: easy to operate;

Disadvantages: It is easy to deviate when the distribution law is obvious;

Application: Scenes with uniform individual distribution, showing obvious uniform distribution rules.

3) Stratified sampling: all individual samples are divided into several categories according to certain characteristics, and then individuals are selected from each category using random sampling or equidistant sampling.

Advantages: reduce sampling error, conduct separate studies for different types of data samples;

Cons: No disadvantages;

Application: Data with attributes, labels, etc., with classification logic.

4) Cluster sampling: first divide all samples into several subgroup sets, and then randomly sample several subgroup sets to represent the population.

Advantages: easy to operate;

Disadvantages: The distribution is limited by the division of small group sets, and the sampling error is large;

Application: The characteristics of small group sets are relatively small, and there are higher requirements for dividing small group sets.

1. Simple random sampling.

2. Equidistant sampling.

3. Stratified sampling.

4. Cluster sampling.

Generally speaking, the basic organizational forms of probability sampling surveys are divided into two categories: single-stage sampling and multi-stage sampling. Single-stage sampling refers to a process that requires only one sampling. It includes simple random sampling, systematic sampling, stratified sampling, etc.

Multi-stage sampling is the process of stratifying the population and then taking samples layer by layer. Multi-stage sampling is used when the population is particularly large.

Probabilistic sampling includes simple random sampling, systematic sampling (equidistant sampling), stratified sampling (type sampling), cluster sampling, multi-segment sampling, PPS sampling, and indoor sampling.

Simple random sampling: If each individual in the population has an equal chance of being sampled (i.e., the randomness of the sampling), and the composition of the population remains unchanged after one individual is sampled (i.e., the independence of the sampling), this sampling method is called simple random sampling.

Simple random sampling generally uses the following three methods:

1) Lottery method: each individual in the population is numbered, and made into a lot, and a part of it is randomly selected after full mixing, and the individuals corresponding to this part form a sample.

2) Look-up table method: check the random number table to determine the number of the individual drawn from the population, then the individual corresponding to the number will enter the sample. The random number table can be arbitrarily started from any area or any number, and proceeded in order in all directions.

3) Computer number method: use the computer to make up a random number program, and use the random number as the number of the individual in the population to enter the sample.

The basic principle of principle probability sampling is that the larger the sample size, the smaller the sampling error, and the larger the sample size, the higher the cost. According to the law of mathematical statistics, when the sample size increases in a straight line (doubling the sample size, the cost also doubles), and the sampling error is only a decrease in the square root of the relative growth rate of the sample size.

Therefore, the design of the sample size is not always better, and is often constrained by economic conditions.

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There are simple random sampling.

Equidistant sampling. Stratified sampling.

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Characteristics of probability sampling:

a) Simple random sampling:

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

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

2) Stratified sampling:

1.Stratified sampling: It refers to dividing the population into different layers according to a certain rule, and then independently and randomly sampling samples in different strata, so that the resulting samples are called stratified samples.

2.Features: Stratified sampling can estimate not only the population parameters, but also the parameters of each layer.

Facilitate the organization of the sampling exercise: hierarchical preparation of sampling frames.

A certain sample unit should be drawn for each layer, so that the sample is more evenly distributed in the population, which can reduce the sampling error.

iii) Systematic sampling:

1.Systematic sampling: It refers to the first arrangement of all units in the population in a certain order, randomly selecting an initial unit within the specified range, and then selecting other sample units according to the pre-specified rules. The simplest systematic sampling is isometric sampling.

2.Peculiarity. (1) The advantages of systematic sampling are:

It is easy to operate, because it only needs to randomly determine the starting unit, and the entire sample is naturally determined.

The requirements for the sampling frame are also relatively simple: only the population is required to be arranged in a certain order.

2) The disadvantage of systematic sampling is that the variance estimation is more complicated, which brings certain difficulties to the calculation of sampling error.

iv) Cluster sampling:

1.Cluster sampling: all the basic units in the population are divided into non-overlapping groups according to certain rules, and the groups are directly extracted during sampling, and all the basic units of the selected groups are investigated, and the groups without the selected groups are not investigated.

2.Features: (1) Advantages of cluster sampling:

Surveys are easy to implement and can save money and time.

The preparation of sampling frames has been simplified, and only the sampling frames of the group are required for sampling, rather than the sampling frames of all the basic units.

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

v) Multi-stage sampling:

The general term for sampling methods that have gone through two or more sampling stages, the sampling design in reality is often a combination of multiple sampling methods.

For example, in the two-stage sampling, the first stage uses stratified random sampling, and the second stage uses systematic sampling. For example, when stratified sampling is used, different sampling methods can be used in different layers, simple random sampling is used in some layers, and systematic sampling is used in some layers.

1. Probability sampling.

Also known as random sampling, it refers to the sampling that excludes the subjective factors of people in the population and gives each individual a certain opportunity to be extracted. It is characterized by the fact that the sample is representative and the population can be inferred from the survey results; The operation is more complex, takes more time, and often requires more expense.

2. Non-probability sampling

Also known as non-random sampling, it refers to the sampling of samples according to certain subjective criteria, so that each individual in the population is selected not according to its own chance, but completely depends on the willingness of the researcher.

It is characterized by the fact that it does not have the function of inferring the population from the sample, but can reflect the characteristics of a certain group of people, and is a fast, easy and economical method of data collection. This method can be used when the researcher has a good understanding of the population.

Or when the population is too large and complex, and it is difficult to adopt the probabilistic method, non-probability sampling can be used to avoid the probability sampling that is easy to draw samples that cannot be implemented or are "poor", so as to avoid affecting the representation of the population.

Sample surveys can be divided into two categories: probability sampling and non-probability sampling.

Probability sampling: The basic organizational forms of probability sampling surveys are divided into two categories: single-stage sampling and multi-stage sampling.

The sampling methods included in probability sampling are as follows:

1. Convenience sampling

The sample was limited to a fraction of the population that was easily drawn before the noise. The most common convenience sampling is chance sampling, in which the researcher members a sample of every population unit encountered at a given time and in the environment. The "street stop-and-go law" is a kind of chance sampling.

Some surveys are unpleasant and cumbersome for the respondents, and in this case, for the sake of convenience, the method of using voluntary respondents as the survey sample is adopted. Convenience sampling is the simplest method of non-random sampling, saving time and money, but sample representativeness is not guaranteed because it is too affected by chance factors.

2. Judgment sampling

Refers to the purposeful selection of what he considers to be a "representative sample" at the discretion of an expert. For example, when sociologists study the general family situation in a country, they often use expert judgment to select "medium-sized towns".

3. Quota sampling

It refers to the classification of population elements according to certain control indicators or characteristics, and then the selection of sample elements according to convenience sampling or judgment sampling.

It is equivalent to two stages of limited judgment sampling. In the first stage, it is necessary to determine the distribution of characteristics (control features) in the population, and usually, the proportion of elements with these control features in the sample is the same as the proportion of elements with these characteristics in the population, and the quota of the first step ensures that the composition of the sample on these characteristics is consistent with the composition of the population.

In the second stage, the sampling of samples is controlled according to the quota of the wild photo, and the selected elements are required to be suitable for the characteristics controlled. For example, quota sampling in fixed-point street visits.

Probability sampling includes simple random sampling of breakfast samples, systematic sampling (equidistant sampling), stratified sampling (quasi-hidden sampling), cluster sampling, multi-segment sampling, PPS sampling and indoor sampling. Probability sampling is when each unit in the sample of the survey population has an equal probability of being drawn. Also known as random sampling, probability sampling is based on probability theory and random principles to take samples, so that each unit in the population has a pre-known non-zero probability of Lu carrying silver to be sampled.

The probability that a population unit will be selected can be specified by a sample design, through some kind of randomization operation, although a random sample will generally not be exactly the same as the population.