The concept of sampling.

Lecture 4

1. The concept of sampling. 2. Types of samples and methods of constructing samples.3. Determining the sample size.

Population – the set of all elements that have some common properties that are essential for their characteristics. Sampling is based on knowledge of the outline of the general population, which is understood as a list of all consumers of interest to the researcher. For example, a list of all homeowners in a certain region or city, or a list of all retail outlets selling products.

Depending on the size of the population and the objectives of the study, methods can be used continuous or selective examinations. When conducting continuous surveys examine all units in the population. This method can be used if the number of elements in the population is small (vip clients in consumer research, organizations in business-to-business research).

The most common way to obtain data in marketing research is selective observation. Fulfillment of certain rules for selecting units in the sample population and compliance with the representativeness of the sample allows the sample data to be extended to the general population.

When forming a sample, we use probabilistic And non-probabilistic (deterministic) methods.

Probability sampling is a sample into which each element of the research object can be included with a given degree of probability. In probability samples, each element of the population is known and has a certain probability of being included in the survey. It should be noted that it is not possible to accurately calculate probabilities due to lack of information on population size. Therefore, the term "certain probability" is more related to the rules of sampling than to knowledge of the exact size of the population.

Non-probability sampling is a sample in which items are selected based on predetermined preferences or judgments. IN non-probability samples the condition of equal probability of each object in the population being included in the sample is not met. For this type samples, sampling error (bias) cannot be calculated. But this does not mean that the study will produce inaccurate results. Non-probability sampling requires less time and money. Non-probability samples are often used for relatively small populations (thousands, tens of thousands of consumers).

The following types of deterministic samples are distinguished:

· unrepresentative;

· intentional;

· quotas;

· main array.

Non-representative (convenience sampling) samples are based on the selection of the most accessible elements (customers in stores, passers-by on the street, etc.). The researcher relies on the principle that the respondent belongs to the projected population.

Judgemental sampling is based on the manual selection of those elements that, in the opinion of the researcher, meet the objectives of the study. A variation of purposive sampling is snowball sampling. It consists of identifying initial elements, each of which points to several new ones, and so on. Such a sample is used when examining objects with specific characteristics that occupy a small proportion in the total set of similar objects and closely interact with each other. Purposeful sampling has the same main disadvantage as non-representative sampling - the inability to estimate its error and the low degree of representativeness.

Quota sampling - Quota sampling - deterministic samples formed by including elements in the sample in the same proportion according to the main characteristics in which they are present in the general population under study

one of the most popular sampling methods. When using the quota method, one or more characteristics are selected by which the sample will be controlled. The number of units in a sample that have certain characteristics must be proportional to the number of such units in the population. It is believed that when using the quota method, it is possible to take a smaller sample size than with random sampling, since quota sampling gives almost complete coincidence of the sample and general populations according to the given parameters, i.e. the property of representativeness (representativeness) of the sample is observed. However, this statement cannot be confirmed using mathematical methods. Most often, socio-demographic characteristics (gender, age, education, income level, etc.) are used as quota parameters.

Method main array assumes the inclusion in the sample of more than 50% of the objects of the general population. The advantage of a survey main array method is that the sample has a high proportion of the population. Due to this, it is possible to eliminate possible errors. In principle, it is enough to survey a large proportion of respondents in the general population, which minimizes the difference between the sample mean and the general mean.

Probabilistic methods. If the sampling units have a known chance (probability) of being included in the sample, then the sample is called a probability sample. Probabilistic methods include:

· simple random selection;

· systematic selection;

· cluster selection;

· stratified selection.

Simple random sampling (SRS) – a sample in which each element of the research object has an equal probability of being included in the sample population. The simplest method of forming a probability sample. Such a sample is formed by randomly selecting elements from their complete list with equal probability. The main disadvantage of such a sample is the need to have a complete list of elements of the population being studied. , which is provided in the practice of marketing research quite rarely. With a simple random sample, the selection is made from the entire mass of units of the general population without first dividing it into any groups, and each element has the same probability of being included in the sample (P), which can be calculated as the ratio of the sample size to the size of the general population. For example, if the population size is 10,000 thousand people, and the sample size is 600 people, then the probability of a particular person being included in the sample is 6% (400/10,000 * 100). The simplest way to organize a random sample is by drawing lots or using a table of random numbers. During a telephone interview, the computer can randomly generate telephone numbers because it has a random number generator.

Sample - This:

1) the totality of those elements of the research object that will be directly studied;

2) methods and procedures for selecting elements of the research object.

Population – a complete set of objects related to the problem being studied. In sociological research as G.S. most often they are aggregates of individuals - the population (city, country, etc.), a social group (youth, the unemployed, businessmen, etc.), the audience of mass media (MSC), etc. However, in many cases G.S. . may consist of larger elements (objects) - families (households), academic groups, enterprises, religious communities, individual localities or states, etc.

Sample population - a portion of objects from a population selected for study in order to draw conclusions about the entire population.

In order for the conclusion obtained by studying the sample to be extended to the entire population, the sample must have the property of representativeness.

Representativeness is the ability of a sample to represent the population being studied. The more accurately the composition of the sample represents the population on the issues being studied, the higher its representativeness.

EXAMPLE: Representativeness can be illustrated by the following example. Let's assume that the population is all the students of the school (600 people from 20 classes, 30 people in each class). The subject of study is attitudes towards smoking. A sample consisting of 60 high school students represents the population much worse than a sample of the same 60 people, which will include 3 students from each class. The main reason for this is the unequal age distribution in classes. Consequently, in the first case, the representativeness of the sample is low, and in the second case, the representativeness is high (all other things being equal).

Sample types

1.Random sampling.

1.1.Simple random selection.

1.2. Systematic (or mechanical) sampling method.

1.3. Serial (cluster or cluster) sampling.

1.4. Stratified sampling.

2. Non-random sampling (non-probability).

2.2. Spontaneous sampling.

2.3. Multi-stage and single-stage sampling.

1.Random sampling.

The peculiarity of a random sample is that all units in the population have an equal probability of being included in the sample population. In case of random sampling it is carried out randomness principle. The sampling basis can be lists of enterprise employees, telephone directories, registration lists of car owners, lists of voters at polling stations, house registers, as well as various lists compiled by the sociologist himself, depending on the purposes of the study (a list of streets on which respondents are then selected).

Random sampling is usually used in public opinion polls before elections, referendums and other public events.

Plus This method is to fully comply with the principle of randomness and, as a result, to avoid systematic errors.

Disadvantages of this method:

– The need to have a list of population elements.

– Difficulty of conducting a survey.

– Relatively large sample size.

It often happens that it is necessary to analyze a specific social phenomenon and obtain information about it. Such tasks often arise in...

Sampling is... Definition, types, methods and results of sampling

From Masterweb

09.04.2018 16:00

It often happens that it is necessary to analyze a specific social phenomenon and obtain information about it. Such tasks often arise in statistics and in statistical research. It is often impossible to verify a fully defined social phenomenon. For example, how to find out the opinion of the population or all residents of a certain city on any issue? Asking absolutely everyone is almost impossible and very time-consuming. In such cases, we need sampling. This is precisely the concept on which almost all studies and analyzes are based.

What is sampling

When analyzing a specific social phenomenon, it is necessary to obtain information about it. If you take any research, you will notice that not every unit of the totality of the object of study is subject to research and analysis. Only a certain part of this entire totality is taken into account. This process is sampling: when only certain units from a set are examined.

Of course, a lot depends on the type of sample. But there are also basic rules. The main one states that selection from the population must be absolutely random. The population units to be used should not be selected because of any criterion. Roughly speaking, if it is necessary to recruit a population from the population of a certain city and select only men, then there will be an error in the study, because the selection was not carried out randomly, but was selected on the basis of gender. Almost all sampling methods are based on this rule.

Sampling rules

In order for the selected set to reflect the main qualities of the entire phenomenon, it must be built according to specific laws, where the main attention must be paid to the following categories:

• sample (sample population);
• population;
• representativeness;
• representativeness error;
• aggregate unit;
• sampling methods.

Features of selective observation and sampling are as follows:

1. All results obtained are based on mathematical laws and rules, that is, if the research is carried out correctly and with correct calculations, the results will not be distorted on subjective grounds
2. It makes it possible to obtain results much faster and with less time and resources by studying not the entire array of events, but only part of them.
3. It can be used to study various objects: from specific issues, for example, age, gender of the group we are interested in, to the study of public opinion or the level of material security of the population.

Selective observation

Selective is what it is statistical observation, in which not the entire population of what is being studied is subjected to research, but only a certain part of it, selected in a certain way, and the results obtained from studying this part are distributed to the entire population. This part is called the sample population. This is the only way to study a large array of research objects.

But sample observation can only be used in cases where it is necessary to investigate only small group units. For example, in a study of the ratio of men to women in the world, sample observation will be used. For obvious reasons, it is impossible to take into account every inhabitant of our planet.

But with the same study, but not of all the inhabitants of the earth, but of a certain 2 “A” class in a specific school, a certain city, a certain country, it can do without selective observation. After all, it is quite possible to analyze the entire array of the research object. It is necessary to count the boys and girls of this class - this will be the ratio.

Sample and population

In fact, everything is not as difficult as it sounds. In any object of study there are two systems: the general population and the sample population. What is it? All units belong to the general one. And to the sample - those units of the general population that were taken for the sample. If everything is done correctly, then the selected part will constitute a reduced model of the entire (general) population.

If we talk about the general population, then we can distinguish only two types of it: a definite and indefinite general population. Depends on whether the total number of units of a given system is known or not. If it is a specific population, then sampling will be easier because you know what percentage of the total number of units will be sampled.

This point is very necessary in research. For example, if it is necessary to investigate the percentage of low-quality confectionery products at a specific plant. Let us assume that the population has already been determined. It is known for sure that this enterprise produces 1000 confectionery products per year. If you take a sample of 100 random confectionery products from this thousand and send them for examination, then the error will be minimal. Roughly speaking, 10% of all products were subject to research, and based on the results, we can, taking into account the representativeness error, talk about the poor quality of all products.

And if you sample 100 confectionery products from an uncertain population, where in reality there were, say, 1 million units, then the result of the sample and the study itself will be critically implausible and inaccurate. Do you feel the difference? Therefore, the certainty of the population in most cases is extremely important and greatly influences the result of the study.

Representativeness of the population

So now one of the most important questions is what should the sample be? This is the most important point of the study. At this stage, it is necessary to calculate the sample and select units from the total number into it. A population has been correctly selected if certain features and characteristics of the population remain in the sample. This is called representativeness.

In other words, if after selection a part retains the same tendencies and characteristics as the entire number of the sample, then such a population is called representative. But not every specific sample can be selected from a representative population. There are also research objects whose sample simply cannot be representative. This is where the concept of representativeness bias arises. But let's talk about this in more detail a little later.

How to make a sample

So, in order to maximize representativeness, there are three basic sampling rules:

1. The most unique sample number is considered to be 20%. A statistical sample of 20% will almost always give a result as close to reality as possible. At the same time, there is no need to transfer a large part of the population to the collected one. 20% of the sample is the indicator that has been developed by many studies. Let's give some more theory. The larger the sample, the smaller the representativeness error and the more accurate the research result. The closer the sample population is to the general population in terms of the number of units, the more accurate and correct the results will be. After all, if you examine the entire system, then the result will be 100%. But there is no longer a sample here. These are studies in which the entire array, all units, are examined, so this does not interest us.
2. If it is inappropriate to process 20% of the general population, it is allowed to study population units in an amount of at least 1001. This is also one of the indicators of studying the array of the research object, which has developed over time. Of course, it will not give accurate results with large research arrays, but it will bring it as close as possible to the possible sampling accuracy.
3. In statistics, there are many formulas and tabulations. Depending on the object of study and the sampling criterion, it is advisable to choose one or another formula. But this point is used in complex and multi-stage studies.

Error (error) of representativeness

The main characteristic of the quality of the selected sample is the concept of “representative error”. What is it? These are certain discrepancies between the indicators of sample and continuous observation. Based on error indicators, representativeness is divided into reliable, ordinary and approximate. In other words, deviations of up to 3%, from 3 to 10% and from 10 to 20%, respectively, are acceptable. Although in statistics it is desirable that the error does not exceed 5-6%. Otherwise, there is reason to talk about insufficient representativeness of the sample. To calculate representativeness bias and how it affects a sample or population, many factors are taken into account:

1. The probability with which an accurate result must be obtained.
2. Number of units in the sample population. As mentioned earlier, the fewer units the sample contains, the greater the representativeness error will be, and vice versa.
3. Homogeneity of the study population. The more heterogeneous a population is, the greater the representativeness bias will be. The ability of a population to be representative depends on the homogeneity of all its constituent units.
4. The method of selecting units in the sample population.

In specific studies, the percentage of error in the mean value is usually set by the researcher himself, based on the observation program and according to data from previously conducted studies. As a rule, a maximum sampling error (error of representativeness) of 3-5% is considered acceptable.

More is not always better

It is also worth remembering that the main thing when organizing sample observation is to bring its volume to an acceptable minimum. At the same time, one should not strive to excessively reduce the margins of sampling error, as this can lead to an unjustified increase in the volume of sample data and, consequently, to increased costs for conducting sample observations.

At the same time, the size of the representativeness error cannot be excessively increased. Indeed, in this case, although there will be a decrease in the size of the sample population, this will lead to a deterioration in the reliability of the results obtained.

What questions are usually posed to the researcher?

If any research is carried out, it is for some purpose and to obtain some results. When conducting a sample survey, the initial questions typically asked are:

1. Determining the required number of units in the sample population, that is, how many units will be studied. In addition, for an accurate study, the population must be representative.
2. Calculation of representativeness error with a specified level of probability. It is immediately worth noting that sample studies do not have a 100% probability level. If the authority that conducted the study of a certain segment claims that their results are accurate with 100% probability, then this is a lie. Many years of practice have already established the percentage of probability of a correctly conducted sample study. This figure is 95.4%.

Methods for selecting research units in the sample

Not every sample is representative. Sometimes the same characteristic is expressed differently in the whole and in its part. To achieve the requirements of representativeness, it is advisable to use various techniques creating a sample. Moreover, the use of one method or another depends on specific circumstances. Among these sampling techniques are:

• random selection;
• mechanical selection;
• typical selection;
• serial (cluster) selection.

Random selection is a system of measures aimed at randomly selecting units in the population, when the probability of being included in the sample is equal for all units in the population. This technique is advisable to use only in the case of homogeneity and a small number of inherent characteristics. Otherwise some character traits risk not being reflected in the sample. The characteristics of random selection underlie all other methods of sampling.

With mechanical selection of units is carried out at a certain interval. If it is necessary to form a sample of specific crimes, you can remove every 5th, 10th or 15th card from all statistical cards of registered crimes, depending on their total number and available sample sizes. The disadvantage of this method is that before selection it is necessary to have a complete record of population units, then ranking must be carried out and only after that sampling can be carried out at a certain interval. This method takes a long time, which is why it is not often used.

Typical (zoned) sampling is a type of sampling in which the general population is divided into homogeneous groups according to a certain sign. Sometimes researchers use other terms instead of “groups”: “districts” and “zones”. Then, from each group, a certain number of units are randomly selected in proportion to the group’s share in the total population. Typical selection is often carried out in several stages.

Serial sampling is a method in which the selection of units is carried out in groups (series) and all units of the selected group (series) are subject to examination. The advantage of this method is that sometimes it is more difficult to select individual units than series, for example, when studying an individual who is serving a sentence. Within selected areas and zones, a study of all units without exception is used, for example, a study of all persons serving sentences in a particular institution.

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Most sociological research is not continuous, but selective: according to strict rules, a certain number of people are selected, reflecting the socio-demographic characteristics of the structure of the object being studied. This type of research is called selective.

A sample survey is a method of systematically collecting data about the behavior and attitudes of people by interviewing a specially selected group of respondents who provide information about themselves and their opinions. It is a more economical and no less reliable method than continuous research, although it requires more sophisticated methods and techniques.

Proper sampling is the key to success and a necessary prerequisite for any survey, unless it is a national census. If the sociologist incorrectly compiled the sample population, i.e. group of people who are going to be surveyed, the results of the study will turn out to be incorrect, and therefore useless to anyone.

Why is it irrational and practically impossible to interview all the people who make up the subject of research? You can roughly calculate how much it would cost to conduct a complete survey of adult residents of at least one urban area with a population of, say, 200 thousand people. Considering that one questionnaire (interviewer) is able to interview no more than three people per hour, with a seven-hour working day its production will be 20 questionnaires. This means that to fully collect information we will need 85 thousand man-days. We want to complete the survey in 10 days and pay 20 rubles for each interview. Thus, we need to attract 8.5 thousand assistants and pay 340 thousand rubles. No matter how important the information is, it is not worth the cost, so sociologists resort to selective survey methods.

The essence of the sampling method is that, according to certain, rather strict, rules, from the total number of people, called general population(population of the entire country, all urban population, residents of one area, only young people, etc.) a limited number of people are selected, which is intended as a kind of model to reproduce the structure of the object. In the language of sociologists, this group of people, as well as the procedure for defining it, is called sampling. Correct construction sample population– the basis and guarantee of high accuracy of sociological research.

The program includes the definition of the population being surveyed, since the vast majority of studies are not continuous, but selective. It is very important to competently, according to certain rules, select the required number of people for the survey.

General population – the entire set of studied elements that have the same social characteristics, which indicate belonging to one object, i.e. This is the entire object to which the research findings apply. It is usually localized in time, geographically, etc. The size of the population in formulas and tables is usually indicated by the symbol N, and the part of its members selected from the general population is called a sample, or the sample population will be designated small n.

Sample population – it is a part, a scaled-down model of the population. The basic rule for its compilation is: Each element in the population should have an equal chance of being included in the sample. But how to achieve this? First of all, you need to find out as many properties, or parameters, of the gene as possible. population, for example, variation in age, income, nationality, and place of residence of respondents. The spread in the ages of respondents is called variation, specific age values ​​– values, and the totality of all values ​​forms variable. Thus, the “age” variable has values ​​from 0 to 70 (average life expectancy) or more years. Values ​​can be grouped into intervals: 0 – 5, 6 – 10, 11 – 15 years, etc. it all depends on the objectives of the research.

Units of analysis or selection –

Experience has shown that a correctly made sample represents or represents well (from the Latin. Represento- represent) the structure and state of the general population. She must be representative– i.e. proportionally reproduce all the main characteristics of the population, and must guarantee for each element of the population an equal probability of being included in the sample. The sampling process is based on the relationship and interdependence of the qualitative characteristics and features of a social object, and is also based on the validity of conclusions about the whole based on the study of its part, provided that in its structure this part is a micromodel of the whole. In other words, a representative sample in sociology is considered to be such a sample population, the main characteristics of which completely coincide (represented in the same proportion or with the same frequency) with the same characteristics of the general population.

A representative study is considered to be one in which the deviation in the sample population for control characteristics does not exceed 5%.

Once the sociologist has decided who he wants to interview, he has determined the sampling frame, after which the question of the type of sample, sampling method, and sampling structure is decided.

Sampling types The main types of statistical sampling are called: random – probabilistic (if the general population is homogeneous elements) and non-random - non-probabilistic ( targeted, quota).

Sampling method is a method of constructing the type of sample whose name this method bears, for example, the probability sampling method.

To ensure representativeness, a complete and accurate list of units in the sample population is required; this list forms sampling frame.Items intended for selection are called selection units, gene element. the population from which information is directly collected is called unit of observation - this is a separate person.

Units of analysis are elements of the sample or survey population (individuals, groups).

If the sampling frame includes a list of selection units, then the sampling frame implies their grouping, reflecting the percentage distribution of genes. aggregates according to some important characteristics, for example, the distribution of individuals by profession, qualifications, gender or age. Sampling structure - These are the percentage proportions of the characteristics of an object, on the basis of which the sample population is compiled. So if in gen. population, for example, 30% of youth, 50% of middle-aged people and 20% of elderly people, then the same percentage proportions of the three ages should be observed in the sample population.

SAMPLING TYPES AND METHODS

In statistical science, depending on the selection method, the following types of samples are distinguished:

1) Random sampling with backtracking, or another name accidental-repeated.

2) Random sampling without return, or random and non-repetitive.

3) Mechanical.

4) Typical.

5) Serial.

When forming a sample, probabilistic (random) and non-probabilistic (non-random) methods are used. If all sample units have a known chance (probability) of being included in the sample, then the sample is called probabilistic in other words, it is a sample for which each element in the population has a certain, predetermined probability of being selected. This allows the researcher to calculate how well the sample reflects the population from which it was selected (designed). This type of sampling is sometimes also called random.

If this probability is unknown, then the sample is called improbability.(non-random, targeted, purposeful).

Probability sampling – her model is associated with the concept of statistical probability. The probability of some expected event is the ratio of the number of expected events to the number of all possible ones. In this case, the total number of events should be quite large (statistically significant).

P =100/600=1/6 Where R - probability of an expected event.

The probability that any of the events will definitely happen is always equal to one, i.e. is reliable approval.

Probabilistic methods include:

Simple random selection

Systematic selection

Cluster selection,

Stratified selection.

Simple random selection can be carried out using blind sampling (lottery method) and using a table of random numbers. In one case, you make your choice without looking, in the other, you are aware of everything, but in order not to interfere and spoil anything, we turn to special tables. Simple random selection is divided into two varieties according to another criterion, namely, the return or non-return of the lottery ball (the respondent's last name may be instead) back into the basket. In this case, the following are distinguished:

Random repeated (with return) selection,

Random irreversible (without return) selection.

Probability (random) sampling methods ( the first two are given above)

1) Mechanical sampling method(for large general populations, the homogeneity of gene elements is implied. aggregate, selection from gene. collections at regular intervals of the required number of elements). All elements of the population are combined into a single list, and from it, at regular intervals, the corresponding number of respondents is selected.

K – Selection step calculated:

K=N/n Where N- size (or number) of gene. aggregates, and n – size of the sample population.

2) Serial sampling method(convenient and accurate), partitioning gene. aggregates into homogeneous parts with subsequent selection within the series.

If it is possible to split the gene. collection into homogeneous parts (series) according to a given characteristic, then the selection of respondents can be carried out from each series separately. Moreover, the number of respondents selected from the series is proportional to total number elements in it. Sorting respondents into homogeneous groups.

From each series, units of analysis can be selected using random or mechanical sampling. Number of respondents to be selected from each series separately.

3) Cluster sampling method(small groups) – selection as research units not of individual respondents, but of groups. Followed by a complete survey in selected groups. A cluster sample is representative if the composition of the groups is as similar as possible in terms of the main demographic characteristics of the respondents. Lists or cards are compiled only for groups (teams, sections, students, class, etc.) that represent an object from the point of view of the sociological study of the problem.

Elements that are covered by the experiment (observation, survey).

Sample characteristics:

• Qualitative characteristics of the sample - what exactly we choose and what methods of sampling we use for this.
• Quantitative characteristics of the sample - how many cases we select, in other words, sample size.

Sampling Need:

• The object of study is very extensive. For example, consumers of a global company's products are a huge number of geographically dispersed markets.
• There is a need to collect secondary information.

Sample size

Sample size - the number of cases included in the sample population.

Samples can be divided into large and small, since different approaches are used in mathematical statistics depending on the sample size. It is believed that samples larger than 30 can be classified as large.

Dependent and independent samples

When comparing two (or more) samples, an important parameter is their dependence. If a homomorphic pair can be established (that is, when one case from sample X corresponds to one and only one case from sample Y and vice versa) for each case in two samples (and this basis of relationship is important for the trait being measured in the samples), such samples are called dependent. Examples of dependent samples:

• pairs of twins,
• two measurements of any trait before and after experimental exposure,
• husbands and wives
• and so on.

If there is no such relationship between samples, then these samples are considered independent, For example:

• men and women ,
• psychologists and mathematicians.

Accordingly, dependent samples always have the same size, while the size of independent samples may differ.

Comparison of samples is made using various statistical criteria:

• Pearson test (χ 2 )
• Student's t test ( t )
• Wilcoxon test ( T )
• Mann-Whitney test ( U )
• Sign criterion ( G )
• and etc.

Representativeness

The sample may be considered representative or non-representative. The sample will be representative when examining a large group of people, if within this group there are representatives of different subgroups, this is the only way to draw correct conclusions.

Example of a non-representative sample

1. A study with experimental and control groups, which are placed in different conditions.
   • Study with experimental and control groups using a pairwise selection strategy
2. A study using only one group - an experimental group.
3. A study using a mixed (factorial) design - all groups are placed in different conditions.

Sample types

Samples are divided into two types:

• probabilistic
• non-probabilistic

Probability samples

1. Simple probability sampling:
   • Simple resampling. The use of such a sample is based on the assumption that each respondent is equally likely to be included in the sample. Based on the list of the general population, cards with respondent numbers are compiled. They are placed in a deck, shuffled and a card is taken out at random, the number is written down, and then returned back. Next, the procedure is repeated as many times as the sample size we need. Disadvantage: repetition of selection units.

The procedure for constructing a simple random sample includes the following steps:

1) it is necessary to obtain a complete list of members of the general population and number this list. Such a list, recall, is called a sampling frame;

2) determine the expected sample size, that is, the expected number of respondents;

3) extract as many numbers from the table of random numbers as we need sample units. If there should be 100 people in the sample, 100 random numbers are taken from the table. These random numbers can be generated by a computer program.

4) select from the base list those observations whose numbers correspond to the written random numbers

• Simple random sampling has obvious advantages. This method is extremely easy to understand. The results of the study can be generalized to the population being studied. Most approaches to statistical inference involve collecting information using a simple random sample. However, the simple random sampling method has at least four significant limitations:

1) It is often difficult to create a sampling frame that would allow for a simple random sample.

2) the result of using a simple random sample can be a large population, or a population distributed over a large geographical area, which significantly increases the time and cost of data collection.

3) the results of using a simple random sample are often characterized by low accuracy and a larger standard error than the results of using other probabilistic methods.

4) as a result of using SRS, a non-representative sample may be formed. Although samples obtained by simple random sampling, on average, adequately represent the population, some of them are extremely misrepresentative of the population being studied. The likelihood of this is especially high with a small sample size.

• Simple non-repetitive sampling. The sampling procedure is the same, only the cards with respondent numbers are not returned to the deck.

1. Systematic probability sampling. It is a simplified version of simple probability sampling. Based on the list of the general population, respondents are selected at a certain interval (K). The value of K is determined randomly. The most reliable result is achieved with a homogeneous population, otherwise the step size and some internal cyclic patterns of the sample may coincide (sampling mixing). Disadvantages: the same as in a simple probability sample.
2. Serial (cluster) sampling. Selection units are statistical series (family, school, team, etc.). The selected elements are subject to a complete examination. The selection of statistical units can be organized as random or systematic sampling. Disadvantage: Possibility of greater homogeneity than in the general population.
3. Regional sampling. In the case of a heterogeneous population, before using probability sampling with any selection technique, it is recommended to divide the population into homogeneous parts, such a sample is called district sampling. Zoning groups can include both natural formations (for example, city districts) and any feature that forms the basis of the study. The characteristic on the basis of which the division is carried out is called the characteristic of stratification and zoning.
4. "Convenience" sample. The “convenience” sampling procedure consists of establishing contacts with “convenient” sampling units - a group of students, a sports team, friends and neighbors. If you need to obtain information about people's reactions to new concept, such a sample is quite justified. Convenience sampling is often used to pretest questionnaires.

Group Building Strategies

Selection of groups for their participation in psychological experiment carried out through various strategies that are needed to ensure that internal and external validity are maintained to the greatest possible extent.

Randomization

Randomization, or random selection, is used to create simple random samples. The use of such a sample is based on the assumption that each member of the population is equally likely to be included in the sample. For example, to make a random sample of 100 university students, you can put pieces of paper with the names of all university students in a hat, and then take 100 pieces of paper out of it - this will be a random selection (Goodwin J., p. 147)....

Pairwise selection

Pairwise selection- a strategy for constructing sampling groups, in which groups of subjects are made up of subjects who are equivalent in terms of secondary parameters that are significant for the experiment. This strategy is effective for experiments using experimental and control groups with the best option- attraction of twin pairs (mono- and dizygotic).