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 sampling.
When constructing a sociological sample, many special terms are used, including two most important ones: general And sample population.
The population from which options are selected for joint study is called general and the part of its members selected from the general population is called samples,or sample population. The size of the population is indicated by the symbol N, and the sample size is n.
General population consider the entire population or that part of it that the sociologist intends to study, a set of people who have one or more properties to be studied. Often the population (also called a population) is so large that interviewing every member is extremely cumbersome and costly. These are those to whom the theoretical interest of a sociologist is directed (in the sense that a scientist can only learn about each representative of the general population indirectly - on the basis of information about the sample population).
Sampling is a set of elements of an object of sociological research that is subject to direct study. The concept of sampling in statistics and sociology is considered in two meanings:
– sample (as a result of an action) – a representative part of the general population in which the law of distribution of a characteristic corresponds to the law of distribution of this characteristic in the general population;
– sampling (as a method or process of action) – a method of selecting objects from a general population into a sample.
The sample should best represent the object of study (the general population).
Sample population– reduced model of the general population. In other words, this is a set of people whom the sociologist interviews. To the sample, or sample population, includes only those whom the sociologist intends to interview directly. Let’s imagine that the subject of his research, i.e., the topic, is the economic activity of pensioners. All pensioners - older people over the age of 55 (women) and 60 (men) years - will form the general population. Using special formulas, the sociologist calculated that it was enough for him to survey 2.5 thousand pensioners. This will become his sample population.
The basic rule for its compilation is: Every 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 general population as possible, for example, the spread in age, income, nationality, and places 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. The values are grouped into intervals: 0–5, 6–10, 11–15 years, etc. They can be grouped differently, it all depends on the objectives of the study. The intervals of values for the “age” variable in the case of pensioners begin at 55 and 60 years.
An entire population, an entire nation, or a very large social group rarely constitutes a general population. In most empirical studies, the sociologist is interested in a particular problem, for example, the increase in the number of divorces among young families in major cities or interest in investment activities among the middle class of the capital city. Divorce and investment activities are topics that interest a particular researcher in a given period of time. Accordingly, all people involved in this process or participating in this event will be called interest group.There may be thousands or tens of thousands of people. They constitute the source population, or population, from which the sociologist constructs a sample and interviews it.
The essence of the sampling method is to judge by the properties of a part (sample) the numerical characteristics of the whole (general population), and by individual groups of elements - about their totality, which is sometimes thought of as a collection of infinitely large volume. The basis of the sampling method is that intercom, which exists in populations between the individual and the general, the part and the whole.
Representative sample in sociology, a sample population is considered whose main characteristics completely coincide (represented in the same proportion or with the same frequency) with the same characteristics of the general population. Only for this type of sample can the results of a survey of some units (objects) be extended to the entire population. Prerequisite to build a representative sample – the availability of information about the general population, i.e. either full list units (subjects) of the general population, or information about the structure according to characteristics that significantly influence the attitude towards the subject of research.
Under representativeness In sociology, we understand the properties of a sample that allow it to act as a model, a representative of the general population, at the time of the survey. In other words, a representative sample is an accurate model of the general population that it should reflect (according to parameters that are significant for the study). To the extent that a sample is representative, conclusions based on the study of that sample can be applied to the entire population.
Representative A study is considered to be one in which the deviation in the sample population for control characteristics does not exceed 5%. When conducting a pilot survey of a small population (for example, within a faculty of up to 100–250 people), a continuous survey will be representative. On a university scale, it will be enough to survey 25% of the total number of students.
Once the sociologist has decided who he wants to interview, he determines sampling frame Then the question of the type of sampling is decided.
Sampling types The main types of statistical sampling are called: random (probability) and non-random (non-probability). Sample type tells how people are included in the sample population. sample size reports how many of them got there.
Let's move on to the characteristics of the most common samples.
In fact, we start with not one, but three questions: What is sampling? when is it representative? what is she?
A set is any group of people, organizations, events that interest us, about which we want to draw conclusions, and a case or object is any element of such a set1. Sample – any subgroup of a population of cases (objects) selected for analysis. If we wanted to study the decision-making activity of state legislators, we could examine such activity in the state legislatures of Virginia, North Carolina, and South Carolina, rather than in all fifty states, and from there generalize the findings to the population from which these three states were chosen. If we wanted to examine Pennsylvania's voter preference system, we could do so by surveying 50 U.S. workers. S. Steele” in Pittsburgh, and extend the survey results to all voters in the state. Likewise, if we wanted to measure the intelligence of college students, we could test all the defensive players enrolled in Ohio State in a given football season and then generalize the results to the population of which they are a part. In each example, we proceed as follows: we identify a subgroup within the population, study this subgroup, or sample, in some detail, and generalize our results to the entire population. These are the main stages of sampling.
However, it seems quite clear that each of these samples has significant shortcomings. For example, although the legislatures of Virginia, North Carolina, and South Carolina are part of the legislative bodies states, they, due to historical, geographical and political reasons, are likely to act in very similar and very different ways from the legislatures of such disparate states as New York, Nebraska and Alaska. While fifty steel workers in Pittsburgh may indeed be voters in the state of Pennsylvania, they, by virtue of socioeconomic status, education, and life experience, may well have views that differ from those of many other people who are also voters. Likewise, although Ohio State football players are college students, they are, by virtue of their various reasons may well be different from other students. That is, although each of these subgroups is indeed a sample, the members of each are systematically different from most of the other members of the population from which they are selected. As a separate group, none of them is typical in terms of the distribution of attributes of opinions, motives of behavior and characteristics in the population with which it is associated. Accordingly, political scientists would say that none of these samples are representative.
A representative sample is a sample in which all the main features of the population from which the sample is drawn are represented in approximately the same proportion or with the same frequency with which a given feature appears in this population. Thus, if 50% of all state legislatures meet only once every two years, approximately half the composition of a representative sample of state legislatures should be of this type. If 30% of Pennsylvania voters are blue collar, about 30% of the representative sample for those voters (not 100% as in the example above) should be blue collar. And if 2% of all college students are athletes, approximately the same proportion of a representative sample of college students should be athletes. In other words, a representative sample is a microcosm, a smaller but accurate model of the population it is intended to reflect. To the extent that the sample is representative, conclusions based on the study of that sample can be safely assumed to apply to the original population. This spread of results is what we call generalizability.
Perhaps a graphic illustration will help explain this. Suppose we want to study patterns of political group membership among US adults.
Rice. 5.1. Formation of a sample from the general population
Figure 5.1 shows three circles divided into six equal sectors. Figure 5.1a represents the entire population under consideration. Population members are classified according to the political groups (such as parties and interest groups) to which they belong. In this example, each adult belongs to at least one and no more than six political groups; and these six levels of membership are equally distributed in the aggregate (hence the equal sectors). Suppose we want to study people's motives for joining a group, group choice, and patterns of participation, but due to resource limitations we are only able to study one out of every six members of the population. Who should be selected for analysis?
One of the possible samples of a given volume is illustrated by the shaded area in Fig. 5.1b, but it clearly does not reflect the structure of the population. If we were to make generalizations from this sample, we would conclude: (1) that all American adults belong to five political groups and (2) that all group behavior of Americans matches the behavior of those who belong specifically to the five groups. However, we know that the first conclusion is not true, and this may give us doubt about the validity of the second. Thus, the sample depicted in Figure 5.1b is unrepresentative because it does not reflect the distribution of a given population property (often called a parameter) according to its actual distribution. Such a sample is said to be biased toward members of the five groups or biased away from all other patterns of group membership. Based on such a biased sample, we usually come to erroneous conclusions about the population.
This can be most clearly demonstrated by the disaster that befell the Literary Digest magazine, which organized the survey, in the 1930s. public opinion regarding the election results. Literary Digest was a periodical that reprinted newspaper editorials and other materials reflecting public opinion; this magazine was very popular at the beginning of the century. Beginning in 1920, the magazine conducted a large-scale national poll in which ballots were sent by mail to more than a million people asking them to mark whose candidacy in the upcoming elections. presidential elections preferable for them. For a number of years, the magazine's polling results were so accurate that a September poll seemed to make the November election irrelevant. And how could an error occur with such a large sample? However, in 1936, this is exactly what happened: with a large majority of votes (60:40), victory was predicted for the candidate from Republican Party Alpha Landon. In the elections, Landon lost to a disabled man - Franklin D. Roosevelt - with almost the same result with which he should have won. The Literary Digest's credibility was so badly damaged that the magazine went out of print shortly thereafter. What happened? It's very simple: the Digest poll used a biased sample. Postcards were sent to people whose names were extracted from two sources: telephone directories and car registration lists. And although previously this method of selection was not very different from other methods, the situation was completely different now, during Great Depression 1936, when less affluent voters, Roosevelt's most likely base, could not afford a telephone, let alone a car. Thus, in fact, the sample used in the Digest poll was skewed toward those most likely to be Republican, yet it is still surprising that Roosevelt had such good result.
How to solve this problem? Returning to our example, let's compare the sample in Fig. 5.1b with the sample in Fig. 5.1c. In the latter case, a sixth of the population is also selected for analysis, but each of the main types of population is represented in the sample in the proportion in which it is represented in the entire population. Such a sample shows that one out of every six American adults belongs to one political group, one out of six belongs to two, and so on. Such a sample would also identify other differences among members that might be correlated with participation in different numbers groups. Thus, the sample presented in Fig. 5.1c is a representative sample for the population under consideration.
Certainly, this example is simplified with at least two extremes important points vision. First, most populations of interest to political scientists are more diverse than the one illustrated. People, documents, governments, organizations, decisions, etc. differ from each other not by one, but by a much larger number of characteristics. Thus, a representative sample should be such that each major, distinct area is represented in proportion to its share of the population. Secondly, the situation where the actual distribution of the variables or attributes we want to measure is not known in advance is much more common than the opposite - it may not have been measured in a previous census. Thus, a representative sample must be designed so that it can accurately reflect the existing distribution even when we are unable to directly assess its validity. The sampling procedure must have an internal logic that can convince us that, if we were able to compare the sample with the census, it would indeed be representative.
To provide the ability to accurately reflect the complex organization of a given population and some degree of confidence that proposed procedures can do so, researchers turn to statistical methods. At the same time, they act in two directions. Firstly, using certain rules (internal logic), researchers decide which specific objects to study and what exactly to include in a specific sample. Second, using very different rules, they decide how many objects to select. We will not study these numerous rules in detail; we will only consider their role in political science research. Let's begin our consideration with strategies for selecting objects that form a representative sample.
The ultimate goal of studying a sample population is always to obtain information about the population. To do this, the sample study must satisfy certain conditions. One of the main conditions is representativeness of the sample. As discussed earlier, there are qualitative and quantitative representativeness.
Randomness, which guarantees the qualitative (structural) representativeness of statistical studies, is achieved by fulfilling a number of conditions for the formation of sample groups (populations):
1. Each member of the population must have an equal probability of being included in the sample.
2. The selection of observation units from the general population must be carried out regardless of the characteristic being studied. If the selection is carried out purposefully, then it is also necessary to comply with the conditions of independence of the distribution of the characteristic being studied.
3. Selection should be carried out from homogeneous groups.
Compliance with the conditions that guarantee maximum closeness of the sample and general populations is ensured by special selection methods. Depending on the method of formation, the following samples are distinguished:
1. Samples that do not require dividing the general population into parts (actually, random repeated or non-repetitive sampling).
2. Samples that require dividing the general population into parts (mechanical, typical or typological samples, cohort, paired samples).
Actually, a random sample is formed by random selection - at random. Random selection is based on mixing. For example: choosing a ball in sports lotto after mixing all the balls, choosing winning lottery numbers, randomly selecting sick cards for research, etc. Sometimes random numbers obtained from tables are used random numbers or using random number generators. According to these numbers, units of observation with numbers corresponding to the random numbers drawn are selected from a pre-numbered array of the general population.
When compiling a random sample, after an object has been selected and all the necessary data about it has been recorded, there are two options: the object can be returned or not returned to the population. According to this the sample is called resampling(the object is returned to the population) or repeatable(the object is not returned to the population). Since in most statistical studies there is practically no difference between repeated and non-repetitive samples, the condition that the sample is repeated is accepted a priori.
In order for the sample population to be quantitatively representative of the general population, it is necessary to first estimate the amount of data that needs to be included in the sample population.
With an unknown population size the amount of resampling that guarantees representative results if the result is reflected by an indicator in the form relative value (share), determined by the formula:
where p is the value of the indicator of the characteristic being studied, in %; q = (100- p) ;
t is a confidence coefficient showing the probability that the size of the indicator will not exceed the limits of the maximum error (usually t = 2 is taken, which provides a 95% probability of an error-free forecast);
is the maximum error of the indicator.
For example: One of the indicators characterizing the health of workers at industrial enterprises is the percentage of workers who did not get sick during the year. Let us assume that for the industrial sector to which the surveyed enterprise belongs, this figure is 25%. The maximum error that can be allowed so that the spread of indicator values does not exceed reasonable limits is 5%. In this case, the indicator can take values of 25% ±5%, i.e. from 20% to 30%. Assuming t = 2, we get
In that case, if the indicator is an average value, then the number of observations can be determined using the formula:
where σ is the standard deviation, which can be obtained from previous studies or based on pilot studies.
With non-repetitive selection And subject to a known population to determine the required random sample size if used relative values (shares) the formula is applied:
for average values The formula used is:
where N is the size of the general population.
Based on the conditions of the above example and taking the size of the general population N=500 workers, we get:
It is easy to see that the required sample size for non-repetitive sampling is less than for repeated sampling (188 and 300 workers, respectively).
In general, the number of observations required to obtain representative data varies inversely with the square of the acceptable error.
Mechanical sampling- sampling, when units of observation are selected mechanically from the population being surveyed. For example: selection of every fifth or every tenth worker using the cards of the enterprise’s HR department or the outpatient cards of the medical unit clinic.
Typical, typological or zoned sampling involves dividing the population into a number of qualitatively homogeneous groups. For example: when studying the morbidity of university students, student groups that are typical in composition are selected for in-depth examination in each course. Often this selection method is combined with other methods. For example: the territory of a city is divided depending on the degree of pollution into typical areas, and observation groups are formed in these areas by random selection.
Cohort selection refers to targeted selections. With this method, individuals are selected from the general population (the distribution into subgroups is non-random), united by the moment of the appearance of any sign or the studied effect, which plays a significant role in the study (year of birth, onset of the disease, taking a drug, etc.).
Case-control study(RS) is a type of epidemiological study in which the distribution of a risk factor is compared in a group of patients with a disease and a control group. The study (SC) is retrospective, since the researcher, having divided patients into groups according to whether they have the disease or not, obtains information from them from the past.
Special attention should be paid to the use of the sampling method in sanitary statistics when studying the general morbidity of the population. The theoretical premises of the sampling method were tested during special studies. So, V.S. Bykhovsky et al. in 1928, they made parallel processing of 132.8 thousand cards with data on diseases using the continuous method and the method of mechanical selection of every fifth card. Analysis of the results of this processing showed high representativeness of the data from a sample study of morbidity. However, up to today, there are no uniform methodological approaches to conducting selective sanitary and statistical studies in widespread practice.
Section 8 of ISO 9001:2000 covers "measurement, analysis and improvement". Although sampling is not covered by this standard, clause 8.1, which is a general introduction to the entire measurement section, states that measurement, analysis and improvement activities (should include the identification of applicable methods, including statistical methods) and the extent of their application). Accurate measurement of customer satisfaction can only be achieved when it is based on a good sample of customers. This chapter provides an overview of the sampling methods used to achieve this goal.
The sampling principle is simple. Most organizations have a large number of customers, but in order to obtain accurate IEP results, it is not necessary to conduct research with everyone, it is enough to do it with a small sample, provided that this sample represents a large group of people. There are several various types samples that are shown in Figure 4.1.
Rice. 4.1 Possible samples
The fundamental difference between samples is whether they are probability or non-probability samples. Probability sampling is also often called random sampling, and only with random, or probability, samples can you be sure that they are free from bias. By definition, all members of the population of a random sample have an equal chance of being represented in it, and the most obvious example of a random sample is the ordinary lottery. All balls or numbers remaining in the draw retain an equal chance of being drawn the next time. It is clear that no trend influences the choice of numbers in the lottery.
4.2.2.1 Non-representative samples
The simplest form of sampling is non-representative sampling. Imagine that you are conducting a public opinion poll. You could go out on the street and ask the first 50 people you meet how satisfied they are with the government's actions. It will be fast, simple and cheap, but it will not be very representative. This may sound trivial, but for clearly more complex cases, as we will see later, it is very easy to slip into an unrepresentative sample.
4.2.2.2 Purposeful sampling
Another form of non-probability sampling is purposive sampling. This is the same form that we have proposed for exploratory research, and although purposive sampling is good for qualitative research that is not aimed at achieving good statistics, it is not suitable for conducting basic research, or any other research that aims to obtain a statistically reliable result. .
4.2.2.3 Sampling based on quotas
The third type of non-probability sampling is quota sampling and is often used to study large populations. Imagine that a municipal council wants to measure the level of satisfaction of the population with the services and facilities that the council provides to them. Suppose you decide to interview members of a quota sample of 500 people living in the city on the street. You could assign five interviewers, each tasked with interviewing 100 people in a main shopping area. However, interviewers are not allowed to use unrepresentative sampling, i.e. interview the first 100 people they meet. Quota sampling requires that each interviewer adhere to many carefully defined norms to ensure that the sample is representative of the local population. The standards may be based on statistics available to the municipal council showing the groups into which the population is divided. So, for example, these data may indicate that 15% of the population is aged from 21 to 30 years, 18% is from 31 to 40 years old, etc. The division can also be based on other characteristics, for example, by gender, income level , ethnic origin. If the council wants the sample to be representative, it must include all of these groups in the same proportion as they are represented in the entire population. To achieve this, interviewers must define groups and quotas for them. In the example given, 15 out of every 100 people interviewed should be between 21 and 30 years of age, 18 should be between 31 and 40 years of age, and this should be combined with quotas for other groups imposed by gender, income, etc.
Let's assume that the interviewers worked all week, from Monday to Friday, from 9 a.m. to 5 p.m. every day, interviewing in a shopping arcade, so that by the end of the week each of them had completed 100 interviews while meeting all the standards. The resulting sample size is 500, which will be fully representative of the city's population, but it will not be selected at random, so it will not be free from trend. According to the definition of random sampling, all residents of a city should have an equal chance of being represented in the sample. In the example given, only those people had such a chance who visited the shopping arcade on these days of the week from 9 am to 5 pm. Thus, the sample will inevitably be biased, perhaps towards older people, the unemployed, and people working nearby. In reality, of course, researchers try to minimize the tendencies inherent in quota sampling by interviewing in different places and at different times, but they can never completely get rid of it, since the sample can only represent those people who at a given time time ended up in a given place, so theoretically such a sample will never be random, completely free from trend.
This does not mean that quota sampling should never be used. If you don't know the people who are your customers, you can't draw a random sample because there is no way to list the entire population from which to draw it. For example, many retailers do not know who their customers are. In such situations, organizations resort to quota sampling.
If you have a database of your customers, you can and should draw a random sample, and the first step is to determine the basis of the sample. The core is the list of consumers from which you intend to sample, and defining this list is a strategic decision. Organizations typically measure customer satisfaction once a year, and the sampling frame consists of those customers who have dealt with the organization in the last twelve months. However, this may not be acceptable for everyone. For example, it is not very effective when studying consumer satisfaction with the help system of any information technology ask questions about your experience using this system over the past 11 months. In this case, it is better to use a shorter time frame, for example, counting all consumers who used the help system in the last month. This may require ongoing monitoring, in which a consumer survey is conducted every month and the results are accumulated to produce a periodic report, such as quarterly or even annually if the number of consumers during the quarter is small.
Thus, you can see that the "consumers" under study may be different for various organizations, and their definition is a strategic decision and you must clearly define them, for these will be the consumers who will form the basis of the study, i.e. the sample population.
4.2.3.1 Simple random sampling
A probability or random sample is trendless because all members of the population will have an equal chance of being included in the sample. As stated earlier, the lottery gives good example simple random sampling - each time a new number is selected, it is selected at random from all those remaining in the “general population”. However, this is a fairly lengthy process if you need a large sample from a large population, so in the days before computers were used to obtain complex samples, market researchers invented a less labor-intensive way of obtaining a simple random sample, known as systematic random sampling.
4.2.3.2 Systematic random sampling
To obtain a systematic random sample for conducting an IEP, you first print out a list of your consumers. Let's say there are 1000 consumers and you want to sample 100, which is 1 in 10 people from the population. First you need to use a random number generator to get a number from 1 to 10. If you get 7, then you include in your list the 7th name from the list, the 17th, 27th, etc., which will result in a systematic random sample of 100 consumers. Before receiving a random number, all consumers have an equal chance of being included in the list. Thus, it will be a random sample, but it may not be representative, especially in the business market. In this case, it is good to use stratified random sampling.
Rice. 4.2 Example of stratified random sampling
We will show with an example how sampling could be done for a typical case of a business-to-business market. The first step for this business market is to build a customer database and sort it by customer value, starting with the highest and working down to the lowest. You then typically divide the resulting list into three parts—high, medium, and low customer value segments, respectively. Finally, determine the sample size in each segment. The results of this process are summarized in Fig. 4.2.
4.2.3.3 Stratified random sampling
Often in business markets, some customers are much more valuable than others. Sometimes a very large portion of a company's activities, such as 40 or 50%, is associated with the first five or six customers. If simple or systematic random sampling is used, it is likely that none of these five or six consumers will be included in the sample. It is clear that there is no point in conducting a survey measuring customer satisfaction if 40 or 50% of the company's overall activities are completely ignored. In a business market where most companies have a small number of high-value customers and larger number low-value consumers, a simple or systematic random sample will inevitably be dominated by low-value consumers. Stratified random sampling is used to obtain a sample that is both representative and free from trend. Obtaining a stratified random sample involves first dividing consumers into segments, or types, and then selecting a random sample within each segment. The sample shown in Figure 4.2 will be representative of the consumer base according to the business contribution each consumer segment makes. In consumer markets, the segmentation may be different, such as by age or gender.
In the example shown, the company derives 40% of its turnover from high-value customers. The fundamental principle of sampling in a business market is that if a high-value customer segment makes up 40% of the turnover (or profit), they should make up 40% of the sample. If a company decides to study a sample of 200 respondents, 40% of the sample, i.e. 80 respondents, should be from high value customers. Since there are 40 high-value consumers, the sampling ratio will be 2:1, which means that 2 respondents in the high-value segment are selected from each consumer. In business markets, it is common practice to select more than one respondent from large consumers when conducting research.
Average value customers also account for 40% of turnover, so they should also make up 40% of the sample. This means that the company must select 80 respondents from its average value customers. Since there are 160 such consumers, the selected proportion will be 1:2, i.e., one respondent for every two consumers of average value. This necessitates a random sample of one representative from every two consumers. This can be easily done using the systematic random sampling procedure described earlier. First, one of two random numbers is generated: 1 or 2. Let it be 2. In this case, you select the 2nd, 4th, 6th, etc. average value consumer.
Finally, 20% of the company's turnover comes from low value customers, so they should make up 20% of the sample, i.e. 40 respondents in the example given. There are a total of 400 low-value consumers there, which corresponds to a selected share of 1:10. This can be done using the same systematic random sampling procedure. At the end of the process, the company will receive a typed random sample of consumers that will be representative of their business activity and, due to random selection, will be free from trend.
Although the above procedure produces a random and representative sample of consumers, after all, the research is not conducted on companies, but on individuals, so if you work in the business-to-business market, you must, in addition to sampling consumers, sample among personal contacts. In practice, organizations often select individuals based on convenience - people with whom they have more contacts, whose names they have on hand. If individuals are selected according to this principle, then no matter how carefully the typified sample of companies is carried out, as a result it will be reduced to an unrepresentative sample of people whom someone knows. To avoid this tendency, you should select individuals at random. The way to implement this selection is to create a list of people associated with your product or service for each customer, and then randomly select people from that list. If you want to carry out a more complex and more precise procedure, you should divide the list of all persons into sectors, which will avoid including too many minor persons. Let, for example, you conduct an analysis of the activities of the administration and decide that in order to more accurately reflect the decision-making process, your sample should contain 40% of procurement contacts, 40% technical contacts and 20% of all other contacts. In this case, you must draw a random sample of individuals in this proportion.
Another issue to decide is the number of consumers you need to have in your sample. Some companies, primarily in business-to-business markets, have a very small number of valuable customers. Other companies have more than a million consumers. In business markets, the size of the population corresponds exactly to the number of individuals in each customer who influence that customer's satisfaction judgment, and it is not necessarily equal to the number of individuals with whom you have regular contact. Typically, the higher the customer value, the more individuals should be included. For the supplier software One consumer may have several hundred computer users. Even so, some organizations will have a much larger population than others, but this will not affect the number of consumers surveyed that is needed to provide a reliable sample.
The statistical precision of a sample is related to its absolute size, regardless of how many people there are in the entire population. The question of what proportion of consumers should be surveyed is a misleading question. A larger sample is always more reliable than a smaller sample, no matter the size of the population. This is best illustrated by the bell curve (see Figure 4.3), from which we can conclude that when we examine a set of data, it tends to follow a normal distribution. This doesn't just apply to research data.
Extreme data Normal data Extreme data
Rice. 4.3 Bell curve
For example, if you record June rainfall in Manchester over a period of five years where three years had normal June rainfall, but two years June was extremely wet, then the estimated average rainfall will be heavily biased by these two unseasonably wet months. If the data were collected over 100 years, then two exceptionally wet or dry months would have little effect on the average June rainfall in Manchester. The same applies to research. If you only study 10 people and two of them have extreme views, they will greatly skew the end result. They will have much less impact with a sample size of 50 and virtually no impact with a sample size of 500, so what larger size samples, the lower the risk of obtaining incorrect results. Figure 4.4 shows that as sample size increases, so does sample reliability. At first, at very small sizes, reliability increases very quickly, but as sample size increases, the effect of sample size on sample reliability decreases. You can see that the curve starts to flatten out between 30 and 50 respondents, which is generally considered the threshold between qualitative and quantitative research. When the sample size reaches 200, the increase in reliability with increasing number of respondents is extremely small. Accordingly, a sample size of 200 respondents is considered the minimum sample size to ensure a reliable IEP. Companies with a very small consumer base (around or less than 200 contacts) should simply research all contacted consumers.
Some years there may have been no rain in June (even in Manchester), some years the intensity of the rain has been incredibly high, but in most years the rainfall falls somewhere between these two limits, in the "normal" zone. Whether we look at research data or rainfall in Manchester, key question is: “What is the risk of obtaining abnormal data that distorts the result?” The smaller the sample, the higher the risk.
As noted earlier, in business research it is generally assumed that a sample size of 200 members provides the necessary reliability for an overall measure of customer satisfaction, regardless of whether the population is 500,000 or 600,000. There is one important exception to this, however, and that comes when you have different segments and want to do an in-depth analysis of the results by comparing satisfaction across the different segments. If you split a sample of 200 items into many segments, you will be faced with the problem of a small and therefore unreliable sample size in each segment. Therefore, it is generally accepted that the minimum total sample size is 200 and the segment minimum is 50.
Because of all this, the size of the total sample is often determined by how many segments you want to analyze. If you want to split your result into six segments, you will need a sample size of at least 300 members so that there are at least 50 members in each segment. This may have great importance for companies with many divisions or markets. Based on a figure of 50 respondents per segment, a retailer with 100 stores would need a sample of at least 5,000 members if customer satisfaction was to be measured at the store level. In our opinion, however, if comparisons are to be made between stores and based on the results of the study, it will be accepted management decision, then the absolute minimum should be 100 consumers per store, or better yet 200. For a retailer with 100 stores, this would result in a sample size of 20,000 consumers needed to obtain very reliable results at the store level.
One more factor needs to be noted. The recommended sample size of 200 respondents to ensure adequate reliability refers to the responses, not the number of consumers selected and invited. Moreover, to ensure statistical reliability, this means 200 consumers selected and the same 200 participants who answered the interview questions or returned the questionnaires. If the response rate is low, then it is statistically unreliable to compensate for it with a simple mailing. more questionnaires until you receive 200 responses. The problem of underresponse tendency can be very significant in IEP studies and will be discussed in more detail in the next chapter.
(a) ISO 9000:2000 states that recognized statistical methods must be used to obtain a reliable sample for consumer-related measurements.
(b) Non-probability sampling increases the risk of a trend influencing the result and should only be used by organizations that do not have a customer database.
(c) For most organizations the best way To obtain a representative and trend-free sample is random sampling based on quotas.
(d) The sampling frame should be significant individuals. In business markets, it may be necessary to include many respondents (sometimes many) from large consumers.
(e) 200 respondents constitute the minimum number of respondents required to reliably measure customer satisfaction across an organization. This number is independent of the number of consumers you have.
(f) Organizations with fewer than 200 customers or contacts must conduct research on all customers enumerated.
(g) If results are to be obtained by segment, the minimum sample size per segment is 50 respondents. In these cases, the required minimum size of the entire sample will be equal to the number segments multiplied by 50.
Representative sample
Representative sample
A representative sample is a sample that has the same distribution of relative characteristics as the population.
In English: Representative sample
See also: Sample Populations
Finam Financial Dictionary.
Representative sample- A group of participants that more or less accurately represents the composition of the population being studied. The sample can reflect the distribution by age and gender, as well as any other characteristics that influence the result of the experiment in terms of... ...
representative sample- - [English-Russian glossary of basic terms on vaccinology and immunization. World Health Organization, 2009] Topics vaccinology, immunization EN representative sampling ... Technical Translator's Guide
REPRESENTATIVE SAMPLE- (representative sample) a sample that is (or is considered to be) a true reflection of the parent population, that is, has the same profile of characteristics, for example, age structure, class structure, level of education. Representative... ... Large explanatory sociological dictionary
REPRESENTATIVE SAMPLE- See sample, representative... Dictionary in psychology
REPRESENTATIVE SAMPLE- such a sample in which all the main characteristics of the general population from which this sample is extracted are presented in approximately the same proportion or with the same frequency with which a given characteristic appears in this general population... encyclopedic Dictionary in psychology and pedagogy
Representative sample- this is a sample in which all the main characteristics of the general population from which this sample is extracted are presented in approximately the same proportion or with the same frequency with which this characteristic appears in this general population... ... Sociological Dictionary Socium
Representative sample- (representative sample). A sample that accurately reflects the condition and properties of the entire population... Developmental psychology. Dictionary by book
representative sample- (representative sample) a sample made according to the rules, that is, in such a way that it reflects the specifics of the general population both in composition and in individual characteristics included subjects. Dictionary of a practical psychologist. M.: AST,... ... Great psychological encyclopedia
English sampling, representative; German Stichprobe, reprasentative. A sample that has essentially the same distribution of relative characteristics as the population. Antinazi. Encyclopedia of Sociology, 2009 ... Encyclopedia of Sociology
Representative sample A sample that has the same distribution of relative characteristics as the population Dictionary of business terms. Akademik.ru. 2001 ... Dictionary of business terms
