Sampling, Market Research, population, sample, sampling strategy, Probability sampling, Non-probability sampling In statistical language, sampling is choosing the portion or subset of a population. The subset of a population that is chosen for the study is known as a sample. In the context of market research, sampling means collecting opinions from a chosen segment of a large mass, to know the characteristics about the whole group.
Proportional allocation is advisable when all we know of the strata is their sizes.
In situations where the standard deviations of the strata are known it may be advantageous to make a disproportionate allocation. Suppose that, once again, we had stratum A and stratum B, but we know that the individuals assigned to stratum A were more varied with respect to their opinions than those assigned to stratum B.
Optimum allocation minimises the standard error of the estimated mean by ensuring that more respondents are assigned to the stratum within which there is greatest variation. Quota sampling Quota sampling is a method of stratified sampling in which the selection within strata is non-random.
Selection is normally left to the discretion of the interviewer and it is this characteristic which destroys any pretensions towards randomness.
Quota v 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 criticise the method for its theoretical weakness while market researchers defend it for its cheapness and administrative convenience.
Quota sampling 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. For example, are those in the over 65 age group spread over all the age range or clustered around 65 and 66?
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. The labour of random selection is avoided, and so are the headaches of non-contact and callbacks. Quota sampling is independent of the existence of sampling frames.
Cluster and multistage sampling Cluster sampling: The process of sampling complete groups or units is called cluster sampling, situations where there is any sub-sampling within the clusters chosen at the first stage are covered by the term multistage sampling.
For example, suppose that a survey is to be done in a large town and that the unit of inquiry i. Suppose further that the town contains 20, households, all of them listed on convenient records, and that a sample of households is to be selected.
One approach would be to pick the by some random method. However, this would spread the sample over the whole town, with consequent high fieldwork costs and much inconvenience.
All the more so if the survey were to be conducted in rural areas, especially in developing countries where rural areas are sparsely populated and access difficult. One might decide therefore to concentrate the sample in a few parts of the town and it may be assumed for simplicity that the town is divided into areas with 50 households in each.
A simple course would be to select say 4 areas at random i. The overall probability of selection is unchanged, but by selecting clusters of households, one has materially simplified and made cheaper the fieldwork. A large number of small clusters is better, all other things being equal, than a small number of large clusters.
Whether single stage cluster sampling proves to be as statistically efficient as a simple random sampling depends upon the degree of homogeneity within clusters.
If respondents within clusters are homogeneous with respect to such things as income, socio-economic class etc. On the other hand, the lower cost of cluster sampling often outweighs the disadvantages of statistical inefficiency. In short, cluster sampling tends to offer greater reliability for a given cost rather than greater reliability for a given sample size.
The necessity of multistage sampling is easily established. Within the selected PSU one may go direct to the final sampling units, such as individuals, households or addresses, in which case we have a two-stage sample. It would be more usual to introduce intermediate sampling stages, i.
Area sampling Area sampling is basically multistage sampling in which maps, rather than lists or registers, serve as the sampling frame. This is the main method of sampling in developing countries where adequate population lists are rare.
The area to be covered is divided into a number of smaller sub-areas from which a sample is selected at random within these areas; either a complete enumeration is taken or a further sub-sample.
Sampling points are selected on the basis of numbers drawn at random that equate to the numbered columns and rows of the grid. If the area is large, it can be subdivided into sub-areas and a grid overlayed on these.
As in figure 7.The development of a sampling plan follows the selection of the research approach and the instruments that will be used to gather torosgazete.com processes that are involved in identifying and obtaining a sample are known collectively as the sampling plan.A sample unit is the group of potential research participants or respondents from which the .
Entry-level researchers looking for a solid introduction to sampling for market research. Mid-level staff seeking to expand their skillset. Experienced researchers looking to catch up with the latest developments. Sampling and Samples written by Joanne Birchall from Rainbow Research Unless you are in the luxurious position of having access to everyone who forms your population, you will need to take some form of sample from which to .
Quota sampling is a method of stratified sampling in which the selection within strata is non-random. Selection is normally left to the discretion of the interviewer and it is this characteristic which destroys any pretensions towards randomness.
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Sampling therefore is a very important part of the Market Research process. If you have surveyed using an appropriate sampling technique, you can be confident that your results will be generalised to the population in question.