There are several methods of random sampling, and there are four basic methods of random sampling

1. Simple random sampling.

Also known as random sampling. Method:

Straight sampling. In the drawing method or drawing lots, all the sampling units are numbered, and the numbers are written on the negative film and rolled into a ball.

Random number table method.

Randomness is guaranteed).

2. Equidistant random sampling (mechanical random sampling).Firstly, the sampling frame is compiled, and the sampling units in the sampling frame are arranged and numbered according to a certain mark, and secondly, the total number of sampling units in the sampling frame is divided by the number of samples to obtain the sampling interval distance. Again, a number is randomly drawn for each sample during the first sampling interval; Finally, according to the sampling interval distance, the survey samples are drawn at equal distances, and the survey samples are drawn at equal distances until the last sample is drawn.

3. Classification random sampling, also known as type random sampling. Firstly, a sampling frame is compiled, and each sampling unit in a number of sample frames is divided into several categories (or layers) according to a certain standard; Secondly, according to the ratio of the sampling units contained in each category to the total number of sampling units, the number of sample units of each type is determined. Finally, the survey samples were selected from various categories according to the simple random sampling method or equidistant random sampling method.

4. Cluster random sampling, also known as collective random sampling. Firstly, the sampling units in the sampling frame were divided into many groups according to a certain standard, and each group was regarded as a sampling unit. Then, according to the principle of randomness, a number of groups of people were selected from these groups as the survey sample. Finally, each sampling unit in the sample population was investigated on a case-by-case basis.

5. Multi-stage random sampling is also known as multi-level random sampling or segmented random sampling.

Determine the sampling unit.

Samples were taken at all levels.

The final sample units were surveyed one by one.

1) Simple random sampling, also known as pure random sampling. It is mainly used for surveys where nothing or little is known about the situation of the subject of the survey. Common lots, dice rolls, and coin rolls are all examples of this method.

This means that the investigator can go through a certain way without any box. A certain unit or a certain object is arbitrarily selected for investigation. Due to the large amount of work and high cost of this method, the adoption rate in practice is relatively low.

2) Stratified sampling, also known as categorical sampling. That is, the overall investigation is divided into several levels, types, and parts according to certain criteria, and these levels, types, and parts are clearly different from each other, and are roughly the same within them, and then samples are drawn according to the same or different proportions for investigation.

3) Equidistant sampling, also called systematic sampling or mechanical sampling. That is, all the survey units are arranged in a certain order, and then they are regularly extracted according to equal intervals, and the size of the interval is obtained by dividing the number of units in the total number of units to be investigated by the number of samples.

4) Cluster sampling, also known as cluster sampling. This is a special method of sample surveying. It is to divide the survey population into several groups (groups) and select samples from them for investigation.

Simple random sampling is the most basic and simplest form of organization of sampling techniques, and simple random sampling is also the basis of other sampling techniques and methods. In the case that the dispersion of the quantitative characteristics of the respondents is not very large and the distribution is relatively uniform, simple random sampling is often used. Simple random sampling has the characteristic that each sampling unit has an equal chance of entering the sample, so simple random sampling is a typical isometric sampling.

There are four basic sampling methods.

1.Pure random sampling: Simple random sampling is a completely random sampling of a subset of observation units in a population to form a sample (i.e., each observation unit has an equal probability of being selected into the sample).

The commonly used method is to first number all the observation units in the population, and then use the lottery to draw lots, random number tables or computers to generate random numbers and other methods from which a part of the observation units is selected to form a sample.

Its advantages are simple and intuitive, and the calculation of the mean (or rate) and its standard error is simple; The disadvantage is that when the population is large, it is difficult to number the individuals in the population one by one, and the samples taken are scattered, which is not easy to organize the survey.

2.Systematic sampling: Systematic sampling, also known as equidistant sampling or mechanical sampling, first sorts all individuals in the population according to characteristics that are not related to the research phenomenon; Then, according to the size of the sample content, the sampling interval k is specified; Individuals of i(ik) are randomly selected, and one individual is selected every other k to form a sample.

The advantages of systematic sampling are: easy to understand, simple and easy to implement; It is easy to obtain a sample that is evenly distributed in the population, and its sampling error is less than that of simple random sampling. The disadvantages are:

The samples collected are scattered, and it is not easy to organize the investigation; When there is a cyclical trend or a monotonic increasing (decreasing) trend in the observation units in the population, bias is susceptible.

3.Cluster sampling: Cluster sampling is to divide the population into k "groups", each group contains several observation units, and then randomly select k groups (k k), which is composed of all the observation units of each group in the sample.

The advantages of cluster sampling are that it is convenient to organize the survey, save money, and easily control the quality of the survey; The disadvantage is that when the sample content is constant, the sampling error is greater than that of simple random sampling.

4.Stratified sampling: Stratified sampling is to divide all individuals in the population into several "layers" according to a certain characteristic that has a greater impact on the main research indicators, and then randomly select a certain number of observation units from each layer to form a sample.

The advantage of stratified random sampling is that the sample has good representativeness, the sampling error is small, and different sampling methods can be used for different layers according to the specific situation after stratification.

The sampling error of the four sampling methods is generally as follows: cluster sampling, simple random sampling, systematic sampling, and stratified sampling.

In actual survey research, two or more sampling methods are often used in combination to conduct multi-stage sampling.

Simple random sampling: A sample that is taken one by one from the population and is not put back.

There are five methods of random sampling, which are simple random sampling, equidistant random sampling, type random sampling, cluster random sampling, and multi-segment random sampling.

Simple random sampling, also known as pure random sampling, is the most common sampling method that directly draws samples from various units of the population according to the principle of randomness.

Simple random sampling.

Equidistant random sampling, also known as mechanical random sampling or systematic random sampling, is to compile a sampling frame first, and arrange and number each sampling unit according to a certain mark; Then, the sampling interval was obtained by dividing the number of units in the population by the number of sample units, and a number was randomly selected as the first sample in the first sampling interval. Finally, samples are taken equidistant at sampling intervals until the last sample is drawn.

The type random sleeve sample is called stratified random oil sample, which is to first divide the overall units into several types (or levels) according to a certain standard; Then, according to the ratio of the number of sampling units contained in each type (or level) to the number of total units, the number of sample units drawn from each type is determined. Finally, samples were taken from each type (or level) according to the simple random sampling or equidistant random sampling method.

Isometric random sampling.

Cluster random sampling, also known as cluster random sampling or collective random sampling, is to first divide the units of the population into many groups according to a certain standard, and each group is regarded as a sampling unit; Then, a number of groups were selected from these groups as samples according to the principle of randomness; Finally, each unit in the sample population was investigated on a case-by-case basis.

Multi-stage random sampling, also known as multi-level random sampling or segmented random sampling, is a method of dividing the process of sampling samples from a population into two or more stages.

Methods: Lottery, random number table Conditions: The sample number is small, the population heterogeneity is small, and the simple random sampling method is to directly extract a few units from the population n units as samples according to the principle of immediacy.

It ensures that each object in the population has the same possibility of being extracted, and requires that they are all independent of each other.

Method: Equidistant sampling conditions: suitable for large sample system random sampling method is to arrange the population units in a certain sign order to arrange the serial number, and then divide the number of population units by the number of sample units to obtain the sampling interval, and finally randomly select a unit as the first unit sample in the first sampling interval, and do equidistant sampling according to the sampling distance until the last sample unit is drawn, which is the system random sampling.

Method: Stratification, proportional sampling of samples from each layer Conditions of use: the overall composition is mixed, and the differences in each layer are largeStratified random sampling method is to first divide the population units into several types according to a certain standard, and then determine the number of sample units from each type according to the ratio of the number of type units to the number of population units, and finally take samples from each type according to the principle of randomness.

Stratified random sampling is characterized by stratification followed by random sampling in each layer.

Methods: The whole group was regarded as an individual use condition: the whole range is large, the number of clusters is large, and the random sampling of the whole group is to divide the units of the population into many groups according to a certain standard, and then twitch several groups from these groups as samples according to the random principle.

For example, in the teaching experiment, it is generally required to conduct research in the class as a unit, but the original teaching unit can not be disrupted, at this time, the cluster random sampling can be used, which is convenient for organization, and can also save manpower, material resources and time.

Random sampling, also known as mechanical sampling and pure random sampling, refers to a sampling method that arbitrarily selects n units from the population n units as a sample and uses the number of sample units to estimate the number of population units, which is an important form of statistical inference. Random sampling is to take sample units from the population according to the principle of randomness, and infer the population from this sample. There are three methods of random sampling: simple random sampling, systematic sampling, and stratified sampling.

Simple random sampling is the random sampling of one unit out of n units of the population, then one unit from that unit, and so on until n units are drawn. The advantage of simple random sampling is that it is simple and convenient, and it is suitable for situations where the overall number of units is small. However, since the sampling unit cannot be determined in advance, when the overall number of units is large and simple random sampling cannot be directly carried out, systematic sampling or stratified sampling should be used.

Random sampling is a sampling method that selects some units from the population as a sample for investigation according to the principle of randomness, and infers the relevant indicators of the population from the results.

1) Simple random sampling.

Simple random sampling is the simplest one-step sampling method, in which sampling units are selected from the population, and each possible sample drawn from the population has an equal probability of being sampled. In sampling, the sampling units in the sampled population are arranged into 1 n codes, and then the random numbers in the 1 n range are determined by using a random number table or a special computer program, and those units that match the random numbers in the population become random samples.

This sampling method is simple and easy to analyze errors, but it requires a large sample size and is suitable for situations where there is little difference between individual individuals.

2) Systematic sampling.

Systematic sampling, also known as sequential sampling, starts at random points and takes samples at regular intervals (i.e., every few times) in the population. The advantages of this method are that the sample distribution is relatively good, there is a good theory, and the population estimate is easy to calculate.

3) Stratified sampling.

Stratified sampling is a kind of unequal probability sampling method that divides the population into several layers that are homogeneous and do not overlap with each other according to some specific characteristics, and then independently draws samples from each layer. Stratified sampling uses auxiliary information stratification, and the layers should be homogeneous, and the differences between the layers should be as large as possible. Such stratified sampling can improve the representativeness of the sample, the accuracy of the population estimate and the efficiency of the sampling scheme, and the operation and management of sampling are more convenient.

However, the sampling frame is more complex, the cost is higher, and the error analysis is also more complicated. This method is suitable for situations where the maternal body is complex, the differences between individuals are large, and the number is large.

4) Cluster sampling.

Cluster sampling is to first group the population unit, which can be grouped according to the natural group or according to the need, and in the traffic survey, the group can be grouped according to the geographical characteristics, and the group is randomly selected as the sampling sample to investigate all the units in the sample group. The cluster sampling sample is relatively concentrated, which can reduce the cost of the survey. For example, in a survey of residents' travel, this method can be used to divide households into groups based on different residential areas, and then randomly select groups as the sample.

The advantage of this method is that the organization is simple, and the disadvantage is that the sample is poorly representative.

5) Multi-stage sampling.

Multi-stage sampling is a type of unequal probability sampling in which samples are drawn in two or more consecutive phases. The cells sampled for the stages are hierarchical, and the sampling units for each stage are structurally different. Multi-stage sampling saves time and money by centralizing the sample distribution.

The organization of the survey is complex, and the calculation of the population estimate is complex.

Simple sampling is a common method.

1) Lottery method: first number all individuals (a total of n) in the population (the number can be from 1 to n), and write the number on the same shape and size of the number (the number can be made of balls, cards, strips of paper, etc.), and then put these numbers in the same box, evenly stirred, draw a number of signs from it each time, and draw n times in a row, you can get a sample with a capacity of n Scope of application: when the number of individuals in the population is not much, advantages:

The lottery method is simple and easy to use, and it is appropriate to use the lottery method when the overall number of individuals is not too large.

2) Random number table method: the "three steps" of random number table sampling: the first step is to number the individuals in the population; The second step is to select the starting number; The third step is to obtain the sample number probability.

Definition of simple random sampling.

In general, if a population contains n individuals, and n individuals are taken as samples (n n) one by one, if each individual in the population has an equal chance of being sampled, this sampling method is called simple random sampling.