Researchers often use sample survey methodology to obtain information about a large aggregate or population by selecting and measuring a sample from that population. In order to make statistically valid inferences for the population, they must incorporate the sample design in the data analysis. In this project we have described the sample design on one specific technique of determining it which is quota sampling. This project gives the brief idea of quota sampling and how it is applicable in different fields of research.
Sampling is the process of selecting units (e.g., people, organizations) from a population of interest so that by studying the sample we may fairly generalize our results back to the population from which they were chosen. It is the act, process, or technique of selecting a suitable sample, or a representative part of a population for the purpose of determining parameters or characteristics of the whole population. Basically means selecting people/objects from a “population” in order to test the population for something. For example, we might want to find out how people are going to vote at the next election. Obviously we can’t ask everyone in the country, so we ask a sample.
A quota sample is an attempt to make a sample representative by having the same proportions of different groups of people in the sample and in the population. It is designed by dividing the population into groups, and interviewing a fixed number in each group. Quota sampling is frequently used in survey designs, and especially in market research projects
1: In the past Simple quota survey in Indonesia had been designated in 2001. In which Audience Dialogue have had good results with quota sampling. Planned total sample size was 200 people. Principle was: interview approximately equal numbers at home, in public places, and 80 at their workplaces. The area to be surveyed was divided (roughly) into “rich places” and “poor places”. The wealth of the areas was determined informally, using local knowledge – not from official data – which was unobtainable anyway.
2: A Nationwide Survey of Migraine in France: Prevalence and Clinical Features in Adults.
In November 1990 a nationwide survey of migraine was conducted in France on a representative sample of residents aged 15 years and older. The diagnosis of migraine was based on the overall prevalence of migraine patients with the IHS criteria in the present study was 8.1%; another 4% were classified as “borderline” migraine, which we in fact considered as definite migraine. Age, gender and occupation were found to be risk factors for migraine. Neither frequency nor duration of attacks nor length of time of disease differed with gender. Expressed intensity of attacks, however, was greater in females.
3: Journal of the Royal Statistical Society Series A (general) part IV, 1953.
This paper was based on research on quota sampling carried out with in the division of research techniques of the London school of economics the division, which is finished by the Nuffield foundation, is charged with the duty of investigating those techniques of research which are of interest to the various departments of the school and one of its principal tasks is the investigation of methods of sampling human populations.
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DELIBERATE (QUOTA) SAMPLING:
It is sampling method of gathering representative data from a group. As opposed to random sampling, quota sampling requires that representative individuals are chosen out of a specific sub-group. For example, a researcher might ask for a sample of 100 females, or 100 individuals between the ages of 20-30. It is the non probability equivalent of stratified sampling. Like stratified sampling, the researcher first identifies the stratums and their proportions as they are represented in the population. Then convenience or judgment sampling is used to select the required number of subjects from each stratum. This differs from stratified sampling, where the stratums are filled by random sampling.
Quota sampling is a non-probability sampling technique wherein the assembled sample has the same proportions of individuals as the entire population with respect to known characteristics, traits or focused phenomenon.
STEP-BY-STEP QUOTA SAMPLING
The first step in quota sampling is to divide the population into exclusive subgroups.
Then, the researcher must identify the proportions of these subgroups in the population; this same proportion will be applied in the sampling process.
Finally, the researcher selects subjects from the various subgroups while taking into consideration the proportions noted in the previous step.
The final step ensures that the sample is representative of the entire population. It also allows the researcher to study traits and characteristics that are noted for each subgroup.
TYPES OF QUOTA SAMPLING:
In quota sampling you select people nonrandom according to some fixed quota. There are two types of quota sampling which are as follows:
In proportional quota sampling we want to represent the major characteristics of the population by sampling a proportional amount of each. For instance if we know the population has 40% women and 60% men, and that we want a total sample size of 100, we will continue to sample men but even if legitimate women respondents come along. We will not sample them because we have already met our quota. .” The problem here (as in much purposive sampling) is that why we have to decide the specific characteristics on which we will base the quota. Will it be by gender, age, education race, religion, etc.?
NON-PROPORTIONAL QUOTA SAMPLING:
Non-proportional quota sampling is a bit less restrictive. In this method we specify the minimum numbers of sampled units. We want in each category. Here we are not concerned with having numbers that match the proportions in the population. Instead we simply want to have enough to assure that we will be able to talk about even small groups in the population. This method is the non-probabilistic analogue of stratified random sampling in that its typically used to assure that smaller groups are adequately represented in our sample.
EXAMPLE OF QUOTA SAMPLES:
In a study wherein the researcher likes to compare the academic performance of the different high school class levels, its relationship with gender and socioeconomic status, the researcher first identifies the subgroups.
Usually, the subgroups are the characteristics or variables of the study. The researcher divides the entire population into class levels, intersected with gender and socioeconomic status. Then, he takes note of the proportions of these subgroups in the entire population and then samples each subgroup accordingly.
WHEN TO USE QUOTA SAMPLES:
The main reason why researchers choose quota samples is that it allows the researchers to sample a subgroup that is of great interest to the study. If a study aims to investigate a trait or a characteristic of a certain subgroup, this type of sampling is the ideal technique.
Quota sampling also allows the researchers to observe relationships between subgroups. In some studies, traits of a certain subgroup interact with other traits of another subgroup. In such cases, it is also necessary for the researcher to use this type of sampling technique.
ADVANTAGES OF QUOTA SAMPLING:
1 Quota sampling is less costly. A quota interview on average costs only half or a third as much as a random interview, but we must remember that precision is lost.
2 It is easy administratively. The labor of random selection is avoided, and so are the headaches of non-contact and callbacks.
3 If fieldwork has to be done quickly, perhaps to reduce memory errors, quota sampling may be the only possibility, e.g. to obtain immediate public reaction to some event.
4. Quota sampling is independent of the existence of sampling frames.
EXAMPLE OF QUOTA SAMPLING AT WORK SCENARIOS:
Quota Sampling at Work Scenario A: Thursday afternoon, nothing to do, wandering around the city centre, when you see a market researcher. You amble by, looking like you didn’t have a care in the world. They look through you. You stroll past again, and are ignored. You try and do someone a favor, and they don’t appreciate it. Scenario B: Tuesday morning, job interview. Not sure of the exact location and a little anxious to make sure you’re not late. See market researcher ahead – walk briskly ahead, looking concerned and busy, but they head towards you. “Excuse me, could you spare a couple of minutesâ€¦” Do you look like you could spare a couple of minutes? The market researcher is driven by a quota sample; they must get the correct number of each kind of person. If you are not the person they are looking for, they will ignore you. If you are, they can go home, if only they can get to interview you.
DISADVANTAGES OF QUOTA SAMPLING:
It may appear that this type of sampling technique is totally representative of the population. In some cases it is not. Keep in mind that only the selected traits of the population were taken into account in forming the subgroups.
In the process of sampling these subgroups, other traits in the sample may be overrepresented. In a study that considers gender, socioeconomic status and religion as the basis of the subgroups, the final sample may have skewed representation of age, race, educational attainment, marital status and a lot more.
1. It is not possible to estimate sampling errors with quota sampling because of the absence of randomness. Some people argue that sampling errors are so small compared with all the other errors and biases that enter into a survey that not being able to estimate is no great disadvantage. One does not have the security, though, of being able to measure and control these errors.
2 .The interviewers may fail to secure a representative sample of respondents in quota sampling. For example, are those in the over 65 age group spread over all the age range or clustered around 65 and 66?
3. Social class controls leave a lot to the interviewer’s judgment.
4. Strict control of fieldwork is more difficult, i.e. did interviewers place respondents in groups where cases are needed rather than in those to which they belong.
QUOTA VS RANDOM SAMPLING:
The advantages and disadvantages of quota versus probability samples has been a subject of controversy for many years. Some practitioners hold the quota sample method to be so unreliable and prone to bias as to be almost worthless. Others think that although it is clearly less sound theoretically than probability sampling, it can be used safely in certain circumstances. Still others believe that with adequate safeguards quota sampling can be made highly reliable and that the extra cost of probability sampling is not worthwhile.
Generally, statisticians criticize the method for its theoretical weakness while market researchers defend it for its cheapness and administrative convenience.
Quota sampling suffers from a number of methodological flaws and has several inherent problems, the most basic of which is that the sample is not a random sample and therefore the sampling distributions of any statistics are unknown.
Second biases may exist in the selection of sample elements within a given cell- even though its proportion of the population is accurately estimated. A interviewer instructed to interview five persons meeting a given, complex set of characteristics may still avoid people living at the top of seven story walkups, occupying particularly run-down homes, or owing vicious dogs. Researchers using quota sampling should be aware of potential problems like this and work to prevent them. For example, they should do all they can to obtain an accurate count of the number and characteristics of individuals who make up a particular cell. They should make sure that interviewers are properly trained and supervised to minimize the chances that the interviewers will violate the sampling protocol in order to skip certain undesirable interviews. But there is no guarantee that all potential problems like these will be anticipated or prevented. Therefore, you would be advised to treat quota sampling warily if your purpose is statistical description.
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