A selective method of collecting information is a method in which a part of the whole is selected, and the characteristics obtained regarding this part are extended to the entire population.

The unit of observation is a direct, primary source of information, which can be an individual buyer, family, enterprise, social groups, etc. The unit of observation is determined depending on the goals and objectives of the study.

The entire set of units of observation, which has the properties of interest to the researcher, is called the general population.

The sample population, or sample, is the part of the general population that is selected for research and analysis.

The main goal of any sample study is to obtain, as a result of studying the sample population, statistical characteristics that fairly well reflect the properties of the general population. If the sample reflects the properties of the general population well enough, it is called representative (representative).

In selective observation, data recording errors may occur. These errors are not unique to the selective method. They also arise in the case of continuous accounting. However, in a selective study, errors occur that are not inherent in continuous observation - errors of representativeness.

Representativeness errors are called discrepancies between the generalizing indicators of the sample and the general population in the conditions of correctly conducted primary data registration. Representativeness errors can be systematic or random.

Representativeness biases arise from violating the requirements of sampling theory. In particular, such errors arise due to a violation of the sample structure, when the sample is formed from observation units with any one value of the studied characteristics to the detriment of observation units with other characteristic values. Sampling bias is also known as sampling bias. For example, if only large stores are selected from the entire population of trade enterprises to study the structure of sales of goods, then such a sample will not reflect the full sales structure characteristic of the entire retail network.

In the practice of research in order to eliminate sample bias, it is formed in two ways.

The first way is to build a miniature model of the general population through conscious selection. For example, if a sample is formed from the inhabitants of a locality (district), then such a part is taken that has a structure identical to the structure of the entire population (according to the main characteristics - gender, age, occupation, etc.). Such a sample is called a quota sample.

The second way is to form a sample randomly. Random selection is called not random, but in a certain way organized selection, in which each unit has an equal chance to get into the sample.

Both of these methods are most often used not independently, but in combination with each other. The scheme and specific methods of selection are determined by the characteristics of the units of observation and the objectives of the study.

In theory, random selection has significant advantages. However, it is not always possible to apply it in practice for marketing research. For random selection, you need to have a sampling frame, i.e. complete list of observation units. Obtaining such lists in some cases is difficult, or even impossible. For example, if it is necessary to identify the degree to which families are provided with technically complex goods, there should be a list of all families in the study area (city, region, etc.). Compiling such lists is a difficult task. Therefore, the sample is formed by a multi-stage selection. The sampling methods will be discussed in more detail below.

If the sampling is correctly formed, there will be no systematic errors of representativeness. However, in this case, the characteristics of the sample and the general population do not necessarily coincide.

The discrepancy between the indicators of the sample and the general population under conditions of correct selection and accurate registration are called random errors of representativeness or sampling errors. Random errors are due to the nature of the sampling method, i.e. they are a consequence of the fact that the sample size is smaller than the size of the general population.

When conducting marketing research, it becomes necessary to obtain information about both qualitative and quantitative characteristics. If qualitative signs are studied, then their shares are calculated, and when studying quantitative signs, the average values ​​are determined.

Sample surveys allow solving various problems: studying the structure of realized demand, the structure of commodity stocks, the characteristics and size of unsatisfied demand, the socio-economic composition of buyers, buyer intentions, etc.

Questionnaire forms:

• - full-time;
• - correspondence.

Requirements for completing the questionnaire:

• Questions should be simple and clear
• Questions must be unambiguous
• Questions must be neutral
• * questions should go from easy to difficult
• * questions should go from general to specific
• * questions should inspire confidence among respondents
• o Trust-building questions come first
• o in second place is a block of basic questions
• o third place is followed by control questions
• o at the end are passport questions (about the person) or the company

The survey is based on statistical methods of selective observation. A necessary condition for the organization of the survey are:

• a preliminary study of the general population;
• assessment of its homogeneity (homogeneity)
• Sorting according to the main features;
• determination of the required number of respondents (sample)

The method of mechanical selection, in which the sample size is determined by the formula:

where n is the sample, t is the confidence factor, depending on the probability with which it can be guaranteed that the marginal error does not exceed the average error (with a probability of 0.99 it is 3, more often e = 2), is the variance of the studied feature, usually determined on the basis of experiment or, by analogy, - the marginal (ordinary) sampling error, N - the number of units of the studied population.

Processing of results. When processing the results, various mathematical methods are used. The results are presented in the form of tables and graphs. During the analysis of the results, the error probability and the corresponding confidence interval are determined. Finally, a report is written that sets out the facts with scrupulous accuracy and minimal influence of the analyst's own beliefs.

Analysis of results. Each answer is carefully analyzed. If the answer is obviously incorrect, it is impossible to understand anything from it, or the answer is being determined, or the respondent answered for a tick or without knowing the subject, his answer is excluded.

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FEDERAL AGENCY FOR EDUCATION

RUSSIAN INTERNATIONAL ACADEMY OF TOURISM

VOLZHSKKO-KAMA BRANCH

Faculty of Management

COURSE PROJECT

Subject "Statistics"

Sampling method

Completed:

Khaliullina Asiya

Naberezhnye Chelny

• Introduction
• I. Theoretical part. Sampling method
• 2. Sampling error
• 3. Small sample
• Mean error of a small sample
• The average error of serial sampling is determined by the formulas
• II. Practical part
• 1. Task
• 2. Solution
• 3. Conclusion
• Conclusion.
• Application

Introduction

The concept of "statistics" comes from the Latin word "status", which in translation means - position, state, order of phenomena.

The term "statistics" was introduced into scientific circulation by Gottfried Achenwal (1719-1772), a professor at the University of Göttingen.

Statistics is an independent social science that has its own subject and method of research. It arose from the practical needs of social life. Already in the ancient world, there was a need to count the number of inhabitants of the state, to take into account people suitable for military affairs, to determine the number of livestock, the size of land and other property. Information of this kind was necessary for collecting taxes, waging wars, and so on. In the future, as social life develops, the range of phenomena taken into account gradually expands.

At the present stage, interest in statistical analysis is caused by the development of the economy in the country, the formation of market relations. This requires deep economic knowledge in the field of collection, processing and analysis of economic information.

The general methodology for studying statistical populations is to use the basic principles that guide any science. These principles, as a kind of principles, include the following:

1. objectivity of the studied phenomena and processes;

2. identifying the relationship and consistency, in which the content of the studied factors is manifested;

3. goal setting, i.e. achievement of the set goals on the part of the researcher studying the relevant statistical data.

This is expressed in obtaining information about trends, patterns and possible consequences of the development of the processes under study. Knowledge of the patterns of development of socio-economic processes that are of interest to society is of great practical importance.

Among the features of statistical data analysis are the method of mass observation, the scientific validity of the qualitative content of groupings and its results, the calculation and analysis of generalized and generalizing indicators of the objects under study.

As for the specific methods of economic, industrial or statistics of culture, population, national wealth, etc., there may be specific methods for collecting, grouping and analyzing the corresponding aggregates (sum of facts).

In economic statistics, for example, the balance method is widely used as the most common method of linking individual indicators in a single system of economic relations in social production. The methods used in economic statistics also include the compilation of groupings, the calculation of relative indicators (percentage ratio), comparisons, the calculation of various types of averages, indices, etc.

To conduct this study, I chose a method in which the generalizing indicator of the studied population is established for some part of it on the basis of random selection, that is, the sampling method.

The purpose of the course work is to study the sampling method of statistics.

As the object of statistical analysis under study, I took the entire staff of kindergarten No. 17 "Forest Fairy Tale", located at st. Sh. Usmanova, 51/08. My main task is to determine the average age of people working in this kindergarten.

I. Theoretical part. Selective method.

1. The essence of the sampling method and its practical significance

The sampling method is the main way to collect information in a developed market economy.

Sampling is a kind of discontinuous observation that allows you to determine the indicators of the entire population (general population) based on the study of its part. In this case, the selected part is formed taking into account the provisions of the theory of probability and mathematical statistics.

The sample has a long history, but its mathematical component was developed in the 2nd half of the 19th-20th century. Russian statisticians made a significant contribution to the formation of sampling theory. In the USSR, continuous statistical observation in the form of reporting dominated. The sample covered only:

§ Evaluation of product quality;

§ Observation of prices in urban collective farm markets;

§ Monitoring family budgets;

§ Study of demand.

Abroad at that time, selective surveys prevailed. Continuous observation covered only customs statistics, taxation and periodically conducted censuses of the population, and industrial qualifications.

Advantages of sampling.

With a properly organized sample survey, no more than 20-25% of the population is studied, usually 10% and then a lot. A huge savings in time and money. At the same time, thanks to the work of extras - professionals, the accuracy of observations is significantly increased (often it is higher than with continuous observation). However, the sampling parameters, due to objective reasons, may differ from the corresponding parameters of the general population, therefore, the results of the sampling study are applied to the general population with a certain probability.

Not every non-continuous observation is a science-based sample.

To obtain reliable results, the sample must be carefully prepared. Preparation includes the following steps:

1. Justification of the expediency of sampling;

2. Preparation of the sampling program;

3. Solving organizational issues of the sample;

4. Determination of the method of selection and the size of the sample, ensuring the reproducibility of its results.

5. Carrying out the selection of units of the general population.

6. Summary of the obtained results and calculation of sampling parameters.

7. Determination of sampling errors.

8. Distribution of sample parameters to the general population.

The main task of the sample:

§ Calculation of the expected sampling error, that is, the difference between the same characteristics of the sample and the general population;

§ Determination of the confidence probability that the reproducibility error will not exceed some predetermined value;

§ Calculation of the sample size that provides the required accuracy of research with a given probability.

2. Sampling error

It arises due to differences in the variation of the values ​​of the studied trait in units of the sample and the general population. Since, subject to the requirements of random selection, all units of the general population have an equal chance of being included in the sample, the composition of the sample can change significantly when the tests are repeated. The sampling parameters will change accordingly, and sampling errors will occur. Sampling errors are inevitable, they follow from the essence of the method. Sampling errors cannot be constant when sampling is repeated.

Sampling error in statistics is a certain average value or generalizing characteristic of errors obtained by repeated repetition of tests.

W-P

- sampling error;

- sample mean;

- general average;

W - the proportion of units that have the studied trait in the sample population (sample proportion);

P - the proportion of units with the studied trait in the general population.

The magnitude of the errors depends on the selection method. It has been proven in mathematical statistics that the mean sampling error (the mean sampling error) is the standard deviation of the distribution of the sample mean.

The sampling error is defined by:

In mathematical statistics, it is proved that the average error of the actual random reselection is calculated:

where

- average sampling error;

- variance of the general population;

- sample size.

If the sample share is being examined during re-selection, where is the variance of the binomial distribution.

The results of repeated selection obey the law of binomial distribution.

With non-repetitive sampling, the results of multiple sampling and the distribution of errors obey the hypergeometric distribution, and the formula for the average error is:

respectively for the sample share

When sampling a large number, when from the mass general populations (), the re-selection formula can be used to calculate sampling errors.

There is a general variance in the mean sampling error formulas. However, it is usually unknown. If we are sampling in order to study only a part of the population, we cannot know the general variance. The only exceptions are the samples carried out to control the result of continuous observation.

However, it has been proved by mathematical statistics that if the sample is taken from the normal distribution of the population, the general and sample variance are related as follows:

2 - general dispersion;

S 2 - sample variance;

n - sample size.

From the formula it can be seen that a sufficiently large sample (n-1)n, and, whence 2 S 2 . Therefore, sample variances are used in practice to calculate mean sampling errors.

If samples are repeatedly taken from the same general population, then one or another statistical probability of its occurrence will correspond to the specific size of the sampling error.

It is impossible (inappropriate) to calculate the probabilities of a specific error size, it is much more important to know that the observational error will not go beyond certain limits.

The essence of the limit theorem: Chebyshev proved that the arithmetic mean of a sufficiently large number of independent random variables whose variances are bounded by some constant becomes, in fact, independent of the game of chance.

According to the Chebyshev formula, if

This formula is for reselection conditions.

Academician Markov proved that the limit theorem is also valid for non-repetitive selection.

Academician Lyapunov proved that the probabilities of the marginal errors of multiple samples obey the normal distribution law, therefore, to determine the probabilities of finding a sample error within the given limits, you can use the Laplace integral formula.

Curve area

Hence, if the confidence factor t=1, then the probability that the marginal sampling error will not be greater than the mean error, which is 0.683.

The probable interval of change in the general average or share in statistics is usually called the confidence interval.

3. Small sample

In the process of statistical research, it is often necessary to limit the size of the sample, especially in cases where the study of population units leads to their destruction.

It has been proved in statistics that even in a sample of a very small size (20-30, and sometimes 4-5 units) it is possible to obtain results acceptable for analysis. The problem of small samples was solved in 1908. English statistician W. Gasset (pseudonym Student). He was able to determine the relationship between the value of the confidence factor t, as well as the size of a small sample n, on the one hand, and the probability of finding a sampling error within the given limits, on the other hand. This dependence is called the Student's distribution. To simplify the calculations, there are special tables of Student's criteria values ​​(p. 372 of the "Practice on the theory of statistics").

N-1 - number of degrees of freedom.

A small sample is determined by the formula

t - Student's criterion;

Small sample mean error

Small sample mean error

Small sample variance

number of degrees of freedom.

4. Determining the optimal sample size

The labor and material costs of conducting a sample directly depend on its size, so it is extremely important to keep the sample size to the optimum so as not to lose its accuracy.

It is convenient to search for the optimal sample size based on the formulas for the average and marginal errors. From the formula for the mean error of random resampling, it can be seen that the value of the mean error is inversely proportional to the square root of the sample size

To reduce the mean error by a factor of 2, the sample size needs to be quadrupled. Using the marginal sampling error formula, you can find the number

This is the optimal sample size for random resampling

The presence of the general variance in the formula of the optimal number leads at first glance to a paradox: why do we need to conduct a sample if the general variance (and, consequently, the general average) is known. However, in practice, the general variance is usually not known, the sample variance of the previous survey is used instead, since the variance as an indicator is more stable than the options themselves, on the basis of which it is calculated.

If the selection is carried out without repetition, then the sample size for such selection is calculated by the formula:

If in the conditions of the problem there is a marginal error of the sample fraction, then the formula:

For re-selection;

For non-recurring selection.

5. Extending the sampling results to the population

For these purposes, two methods are used:

§ Direct conversion method;

§ Method of correction factors.

The direct recalculation method is used to determine, from the data on the sample share, the size of the interval within which the number of units with the studied trait is located in the general population with a given probability.

The main purpose of the method of correction factors is to refine the data of continuous mass observation through random checks. Typically, such checks are carried out by supervisory trainers based on the results of the censuses.

6. Classification of selection methods

The methodology and results of calculating the main parameters of the sample directly depend on the method of selecting units from the general population.

The selection method is a specific system for organizing a sample study. The application of this or that method depends on the purpose of the study, the conditions of the sample, the specifics of the object of study, the required accuracy and efficiency of the results, and the funds allocated for the study.

All selection methods are divided into 3 types:

Individual;

Group;

Combined.

With an individual view, individual units of the population are selected.

In a group view, groups are selected, a series of population units (for example: several boxes were selected from a container and all of them were checked).

The combined method combines individual and group.

If the sample is obtained immediately, the selection is called one-stage.

If there are several successive stages of selection, the sample is considered to be multistage.

The unit of selection changes at each stage. Unlike multi-stage sampling, multi-phase sampling retains the same unit at all stages of selection. However, the monitoring program is gradually expanding.

Depending on the selection scheme used, there are:

Repeated;

Repetitive.

Each of the types of selection can be carried out in the following ways:

1 Actually random;

2 Mechanical;

3 Typical (stratified);

4 Serial (nested);

5 Combined.

7. Organization of selection in various ways and assessment of the reliability of the results

Different selection methods are distinguished by different sampling methods and different algorithms for calculating representativeness errors.

Actually random selection organized in such a way that all units in the population have an equal opportunity to be included in the sample. This is ensured by selection by lot, by tables of random numbers or by using random number generators. Regardless of how units are selected, they must be numbered. When selecting by lot, these numbers are applied to cards, balls, etc., which are then thoroughly mixed and a number of cards equal to the number of selection is randomly selected from them.

The table of random numbers is a matrix of 4 or 5 numbers, each digit of which is independent of the other digits of the given number and other numbers. Depending on the size of the sample, one, two, three or four-digit numbers are selected from the table. Numbers can be selected by columns or rows of the table (starting from any row or column) by a predefined selection algorithm.

Computers and some calculators have a random number generator that displays random numbers on the screen.

The average error of the actual random repeated or non-repeated selection is determined by the formula: see paragraph (2).

Mechanical selection it is a directional sample from a population pre-ordered according to an existing or non-existing feature.

At the first stage, the general population is ordered according to some attribute. Regardless of the trait, mechanical selection sets the selection proportion according to the formula: N/n.

If the population is grouped according to an insignificant feature, then it makes no difference from which unit to start the selection.

If the population is grouped or ordered according to an essential feature, then the selection should begin from the middle of the first group.

The average error of mechanical selection is calculated using formulas for random selection. This is true when the selection was made from a population ordered by an unimportant attribute.

If the population was sorted according to an essential attribute, then this method of calculation somewhat overestimates the average sampling error.

In this case, it was possible to use the average of the within-group variances, rather than the total variance.

Typical sample(stratified). With this sample, the general population is first divided into typical groups (strata), from which a random selection of units is made. Such a sample guarantees the representation of all typical groups of the sample population, which reduces the sampling error. There are proportional and non-proportional methods of typical selection.

With the proportional method, a number of units is selected from each group proportional to either the size of the group or the intragroup variation of the trait under study.

In a typical re-sampling proportional to the size of the group, the average sampling error is determined by the formula:

Mean sampling error for reselection;

mean sampling error for non-repetitive sampling;

Average of intragroup variances;

Intragroup dispersion;

n j - number of corresponding typical groups.

If the proportion of population units that have the trait under study is studied, then the average errors and variance

For re-selection;

For non-recurring selection.

The optimal size of a typical sample is proportional to the size of the groups and is determined by the formulas:

For re-selection;

For non-recurring selection.

The most accurate proportional method of typical selection is the selection of proportional variation of the trait values ​​in groups. This selection is appropriate in the presence of general intragroup dispersions. This is possible when sampling is carried out to control data from a complete observation or when data from a previous complete observation is available.

The number of sample groups is determined by the formula:

Sample size from the j-th typical group;

General intra-group variance;

The number of constituent typical groups in the general population.

The average sampling error of non-repetitive type selection is proportional to the variation of the trait in groups. Determined by the formula:

This method of selection gives an error smaller than the selection proportional to the size of the groups.

The most common case is disproportionate typical selection. With arbitrary proportions of the formation of typical sample groups, the average sampling error is calculated by the formula:

Mean sampling errors in each typical group;

The number of corresponding typical groups.

In this case, the errors of the average sample by groups are determined by the formulas:

Intragroup dispersion.

For re-selection;

For non-recurring selection.

Serial or nest selection- this is a random selection of groups of units with subsequent continuous observation within the selected series. This sample is used mainly for quality control of goods, when it is advisable to open and examine individual packages. This is a type of directional selection that helps to reduce sampling error. Due to the continuous study of nests, partial variances do not affect the representativeness error, which depends only on the variation of serial means, that is, on the intergroup variance, is determined by the formula:

Partial sample variance;

Overall average serial sample;

The number of selected episodes.

The average serial sampling error is determined by the formulas:

- for re-selection;

- for non-recurring selection.

Combined sampling is a combination of group and individual selection of units of observation. Most often, serial and actually random selection are combined.

The sampling error of the combined selection is the sum of the sampling errors expected for each selection method included in the combination. Usually non-repeated combined sampling is used, although re-combined sampling is theoretically possible. Combination sampling is multi-stage in nature. Despite the simplicity of the multi-stage selection methodology, the calculation of its error is rather complicated and is determined by the formula:

for equal selection at each stage.

- average sampling errors at each of the selection stages;

- the number of stages of selection.

8. Momentary selective observation

The method of momentary (instantaneous) observations was developed in 1938 by the English statistician Tiplet for a selective study of the production process. The method is used for group photographs of the costs of working time and equipment operation time, when the observer periodically bypassing the workplaces along a predetermined route registers in a special form what the worker is doing at a particular moment of time, whether he is working or resting at the moment.

The method of momentary observations is a sampling in time, where the general population is the working time fund of the object of observation, that is, a team of workers or a group of pieces of equipment. The sample set consists of time periods of registration of the state of the object of study.

Group photos provide a multiple reduction in costs compared to individual photos, as they do not require the constant presence of an observer at each workplace throughout the working day. The method is effective for assessing the work of a team of workers performing homogeneous operations.

The first step in organizing instantaneous observations is to determine the size of the sample, that is, the required number of the moment of registration.

Confidence factor;

Selective proportion of units that have the trait under study;

Marginal sampling error, expressed as a percentage.

II. Practical part

1. Task

From the entire staff of kindergarten No. 17 "Forest Fairy Tale", a 20% random non-repeated sample was taken to determine the average age of people working in this institution.

The sampling results were as follows:

My task is to determine with a probability of 0.987 confidence intervals in which the average age of people for the entire kindergarten team lies.

Solution

The average age of workers (let us denote it by a letter) lies in a certain interval (- ; +), where is the average age in the sample population, is the sampling error. Let's put it in the form of a formula

- ? ? +

To calculate the sample variance of a feature in a sample, we group the data in the form of an interval distribution series.

Age, years	Number of people, fi

Using the presented data, we calculate the average age in the sample using the formula

To calculate the marginal error, I proceed from the following considerations

Р(| - | ?) = 2Ф(/) = ,

The mean square deviation of a feature in the general population. Since = t *

Р(| - | ?) = 2Ф((t *)/) = 2Ф(t) =

But since we do not know, we can find the marginal error using the average sample error per 1 unit of this sample -

Since in our case the sample is non-repetitive, the average error is calculated in the following way, where

the sample variance of the feature, n is the size of the given sample, and N is the size of the general population.

The coefficient t is determined based on the fact that the distribution of the random variable is considered normal, and that the probability of fulfilling the inequality for should be equal to = 0.987.

According to the table of values ​​of the Laplace functions F(t) (see Appendix), the most approximate value is 0.9869. We select it:

t = 2.48, then it turns out that

2,48 * 1,312 = 3,25

So we find the confidence intervals

37,38 - 3,25 ? ? 37,38 + 3,25

3. Conclusion

Having examined, using the sampling method, the data presented to me by kindergarten No. 17 "Forest Fairy Tale", I can conclude that with a probability of 0.987 (that is, 98.7%), the average age of employees in this institution will approximately lie in the range from 34 to 41 years .

Conclusion

The transition to a market economy fills the work of businessmen, economists and managers with new content. This imposes increased requirements on the level of their statistical training. Mastering the statistical methodology is one of the indispensable conditions for understanding the market situation, studying trends and forecasting supply and demand, making optimal decisions at all levels of management, commercial activity in the market of goods and services.

Selective observation is one of the most modern types of statistical observation. This is an observation in which a portion of the units of the population under study are selected on the basis of scientifically developed principles that ensure that a sufficient amount of reliable data is obtained in order to characterize the entire population as a whole.

After studying this method and applying the knowledge gained to the study of the composition of personnel in the children's institution "Forest Fairy Tale", we can conclude: the age of the majority of workers in kindergarten is from 34 to 41 years.

Bibliography

With sampling error sampling

1. Eleseeva M.A.<<Общая теория статистики>> M:<<Статистика>> 1988

2. Kharchenko L.P.<<Статистика>> M: INFRA - M 1997

3. Boyarsky A.Ya., Gromyko G.L. General theory of statistics, Moscow: "Moscow Universities", 1985.

4. Theory of Statistics: Textbook / Ed. prof. G.L. Gromyko. - M.: INFRA-M, 2002. - 414 p. - (Series "Higher education").

5. Workshop on the theory of statistics: textbook. allowance / Ed. R.A. Shmoylova. - M.: Finance and statistics, 2003. - 416.: ill.

6. Personal data of employees of kindergarten No. 17 "Forest Fairy Tale"

7. http://www.referatw.ru.

8. http://www.referatus.ru.

9. http://www.bankreferatov.ru

Application

The meaning of the Laplace functions
Integers and tenths of t	Hundredths of t

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term paper, added 01/15/2011


System of indicators of trade turnover statistics. Method of analytical grouping. Determination of the correlation coefficient and determination. Maximum permissible error (sampling error). Index of structural shifts, variable and fixed composition.

Visit to the consumer. The first thing a market research firm should do is send members of the cross-functional team to the customer. Face-to-face communication with the customer seems to be the most natural method of obtaining data, and yet some firms delve into the most complex customer research, preferring to keep the latter at a distance. As a result, too long a chain of value judgments is stretched between the consumer and the decision maker. Trade shows provide merchants with the opportunity to promote the latest products and services to consumers and distributors. They allow you to collect significant amounts of information not only about new products, but also about how consumers and distributors are interested in them. Annual reports of competitors. Despite the fact that the annual reports are more like a covert self-promotion, growing | calculated on shareholders, valuable information can be gleaned from them regarding trends in the development of the company's mission, its goals and financial condition, comparing reports for the last four to five years. Government and industry reports. Newsletters. The US Census Bureau, the world's largest market research organization, owns a significant amount of data on the development of the American family. Nearly every public library receives reports prepared by it, and in every major city there are branches of the Bureau, to which businessmen turn for information of interest to them. Computer databases All interactive databases allow the user to search for relevant articles, newsletters and other data by keywords. Focus groups are made up of carefully selected six to twelve people for a casual one or two hour discussion of a particular issue. An experienced facilitator skillfully guides the discussion, while members of the cross-functional decision-making team watch it through a translucent mirror or on a local television network.

Systematic consumer samplingcarried out on the basis of pre-prepared questionnaires. Consumers who have recently purchased a product (service) are surveyed for satisfaction with the product; contact with them is established through a phone call or a postcard with a paid response.

V. 36 Information collection methods

Despite the huge number of various research methods and techniques, the general scheme of activities implemented in the framework of market research is quite simple and understandable. The main sources of marketing information are:

• Interviews and surveys;
• Registration (observation);
• Experiment;
• Panel;
• Expert review.

Interview (poll)- finding out the position of people or obtaining information from them on any issue.

A survey is the most common and essential form of data collection in marketing. Approximately 90% of studies use this method. The survey can be oral (personal) or written.

Personal (Face-to-face) and telephone surveys are called interviews.

Phone interviews

Face-to-face interviews: formalized and non-formalized.

In a formalized interview, there is a specific survey scheme (usually a questionnaire containing pre-prepared clear wording of questions and well-thought-out models of answers to them)

informal interviews- this is a specific method of collecting information in which there is only a topic and a goal.

In-Depth Interviews

Hall - tests- These are personal semi-formalized interviews in a special room.

Group non-formalized interview (focused interview, focus - group) - is a group discussion of issues of interest to representatives of the target audience.

Observation (registration) is a form of marketing research, with the help of which a systematic, systematic study of the behavior of an object or subject is carried out.

Experiment- this is a study of the influence of one factor on another while controlling extraneous factors.

Panel- this is a repeated collection of data from one group of respondents at regular intervals.

Expert review- this is an assessment of the processes under study by qualified specialists - experts.

Delphi method- a form of survey of experts, in which their anonymous answers are collected over several rounds and, through familiarization with the intermediate results, they receive a group assessment of the process under study.

brainstorming method consists in uncontrolled generation and spontaneous interweaving of ideas by participants in a group discussion of a problem.

Synectics considered a highly creative method. The idea of ​​the method lies in the gradual alienation of the original problem by building analogies with other areas of knowledge. After multistage analogies, a quick return to the original problem is made.

37 definition of competitors and competition structure.

Porter identified five factors that determine competition: current competitors; the danger of new competitors; the danger of the appearance of substitutes for the product; the consumer's ability to make deals; supplier's ability to make deals. This structure can be simplified to current competitors potential competitors and product substitutes. As we will see later, this is because the ability of consumers to make deals depends largely on how strong the competition between competitors for supplies to these consumers is. . A company's market share can change dramatically depending on whether the market is defined as global, export-specific, US, regional, urban, or user or consumption segment. The scale of the market is usually determined by a realistic assessment of the company's resources and its growth goals.

38 definition of the concept of "product distribution channel". Distribution management
goods.

Distribution channels

A firm operating on the international market must necessarily comprehensively consider the problems of bringing its products to end consumers 13 . On fig. 94 shows three main links between the seller and the end buyer. The first link is the headquarters ¾ of the seller's organization, which controls the operation of the distribution channels and at the same time is itself part of these channels. The second link ¾ interstate channels ¾ ensures the delivery of goods to the borders of foreign countries. The third link ¾ domestic channels ¾ ensures the delivery of goods from the border crossing points of a foreign state to end consumers. Too many American manufacturers consider their mission complete once the product is out of their hands. And they should more closely monitor what happens to this product in the process of its movement within a foreign state.

Rice. 94. The general structure of the distribution channel at

international marketing

Intrastate distribution channels of different countries differ in many ways from each other. There are large differences in the number and types of intermediaries serving each individual overseas market.

One of the tasks that a researcher faces when conducting a study is to collect the necessary empirical data about the object of study. The set of elements that make up the object of study is called the general population (GS). The simplest, at first glance, way to collect data is a complete survey of the HS. However, the use of a complete survey is not always possible. In this case, sampling is used. The essence of the sampling method lies in the fact that only a part of the elements of the HS, which is called the sampling set (FS), is subjected to the survey. The inventor of the sampling method was life itself. Indeed, even before the theoretical substantiation of the possibilities of applying the sampling method, statisticians were forced to conduct sample surveys. The main reasons for this were the lack of time and funds.

The selective method allows not only to reduce the time and material costs of the study, but also to increase the reliability of the study results. This statement may be perplexing: how can you get more reliable data by examining a smaller part of the HW? However, practice shows that the reliability of the information obtained when using the sampling method can be not only not lower than with a complete survey, but also higher due to the possibility of attracting higher-class personnel and applying various procedures for controlling the quality of the information received.

In addition, the sampling method has a wider scope. The breadth of the scope of the sampling method is explained by the fact that a small (compared to the HS) sample size allows the use of more complex survey methods, including the use of various technical means (for example, video and audio equipment, personal computers and the Internet, as well as sophisticated measuring equipment).

Sample surveys are widely used in the work of state statistics bodies. Most often, large and medium-sized enterprises are covered by a continuous; observation, and observation of the activities of small enterprises is carried out with the help of sample surveys. In a number of cases, sample observations are used in combination with complete censuses and enumerations. For example, the program of the All-Russian population census in 2002. contains both questions of continuous observation related to the entire population, and questions of sample observation of 25% of the population to characterize the main occupation, position, place of work, as well as questions of a 5% sample survey to study marriage and fertility.

Question 56. Marketing program, its main sections and stages of development

The specific purpose or program of the firm is usually clear from the outset. However, over time, as the organization grows and new products and markets emerge, the program can become less clear. Perhaps the program will remain clear, but will cease to be of interest to part of the leadership. A. may, having retained clarity, it will no longer correspond to the new environmental conditions.

It's time to ask yourself:<Что представляет собой наше предприятие? Кто наши клиенты? Что ценно для этих клиентов? Каким будет наше предприятие? Каким оно должно быть?>

Many firms respond to these questions by developing formal written policy statements. A well-crafted mission statement allows employees to feel like they are part of a common cause in the development of opportunities, gives them a goal, emphasizes their importance, aims for achievement.

The mission statement should clearly state the scope (or areas) of the firm's activities. Business boundaries can be determined by products, technologies, customer groups, their needs, or a combination of several factors. A mission statement from the position of market orientation defines an enterprise in terms of its activities to serve specific consumer groups and / or meet specific needs and requests.

In the process of strategic marketing planning, short-term (annual) and long-term marketing programs for enterprises are developed. The marketing program is a set of interrelated activities that determine the actions of the enterprise for a given period of time for all elements of marketing. Short-term marketing programs are distinguished by great detail and specificity of the programming of the enterprise's actions. Long-term - cover activities designed for a long period of time according to the adopted marketing strategy. A unified marketing program is an interconnected system of programs for individual markets and groups of homogeneous products. It serves as the basis for the development of plans for research and development, production, marketing, service, etc.

When preparing a marketing program, it must be taken into account that the entry of an enterprise into the market (in addition to resource and logistics) involves:

in-depth analysis of the market situation and forecast of its development;

a certain degree of freedom in establishing economic ties;

own position in the market, taking into account the commercial risk associated with competition;

technological and organizational policy, allowing to influence the market conditions;

collection, processing and analysis of information about competitors that determine the situation on the market;

Availability of highly qualified specialists and managers capable of implementing marketing solutions in practice.

Marketing programs are developed on the basis of a comprehensive market research, identification of customer requests, marketing strategies and tactics. They are the basis that ensures the interaction of the commercial and sales services of the enterprise with scientific, technical, design and production departments.

Methods of collecting information about the market. Sampling method and its advantages

There is no nationwide market data bank in Russia yet, so the necessary information has to be collected bit by bit, creating a more or less objective picture.

There are two methods for collecting such information - continuous observation, when all units of the general population are surveyed, and selectively, in which information is obtained from only a part of the units of this population. More common in marketing research is the selective method of collecting information, which has the following advantages:

1 - information can be obtained much faster, which ensures the timeliness of information;

2 - the data obtained by sampling is much more complete, because it is possible to characterize each unit of observation much more fully;

3 - information is more complete, because the number of collected information is less, and therefore the number of possible errors is less.

However, the advantages of the sampling method can only be realized if certain rules are strictly observed in the organization and conduct of sampling. These primarily include ensuring the quantitative and qualitative representativeness (representativeness) of the sample.

Quantitative representativeness is understood as the provision in the sample of such a number of units, in which it is possible to fairly reasonably judge the magnitude of the studied characteristics.

If nothing is known about the general population, then the calculation of the required sample size is carried out according to the formula:

where n is the required sample size;

D p is the sampling error we allow for the proportion (given accuracy);

t is a coefficient depending on the probability with which the given sampling accuracy is guaranteed;

p, q are the shares of opposite events (p + q = 1).

If nothing is known about the general population, then take

p = 0.5 and q = 0.5, and the sample size calculated for these values ​​will be sufficient for any other "p" and "q" ratios.

In marketing research, the probability of an event equal to 0.954 is usually considered quite acceptable, at which t = 2 (from the table at p = 0.997, t = 3, etc.).

Example . Traders who sell agricultural machinery need to know how many farms use hay mowers. It is hardly possible to survey all households, so it is better to conduct a selective survey. But how many farms to interview?

For the example in question:

p – share of farms using hay mowers;

q – share of farms not using hay-mowers;

If we can allow a sampling error of ± 5%, then D p = 0.05 and thus have a sample size

farms.

If something is known about the population (for example, it is known from past studies that in the area 800 farms of which 80% used hay mowers), then the sample size is calculated by the formula:

,

where N - the volume of the general population.

For the example in question

farms.

If it is necessary to determine the average value of the general population (for example, the average service life of an electric mower), then the sample size is calculated by the formula:

,

where s2 – variance characterizing the variations of the trait under study;

D x – marginal sampling error for the mean.

For example, past research has shown thats2 is±2.25 of the year. Then, with acceptable accuracy, we have±0.3 of the year.

farms.

The sample must be representative (Figure 1.4), i.e. should be represented by the maximum possible number of groups in the population.

Figure 1.4 - Qualitatively representative sample

Figure 1.5 - Qualitatively unrepresentative sample

To avoid unrepresentativeness (Figure 1.5 in market research, random mechanical selection is used. Its essence is that random objects are examined at a certain interval.