Sampling has long been a common activity in everyday life. When we wish to acquire a large quantity of an items, we decide on the complete lot by studying only a little portion. It has been demonstrated that a well-planned sample survey may provide highly precise information. Because surveys only assess a subset of the population and make conclusions about the entire population, the results are likely to diverge from population values.
A "sample" is a miniature of and selection from a broader group or aggregate. In other words, the sample serves as a microcosm of a broader whole. The "population" or "universe" refers to this greater totality. This phrase is used in a larger sense in research; it is a well-defined group that may consist of humans, items, human qualities, or even the behavior of inanimate things, such as the flipping of a coin. In order to get a meaningful result, research can only cover some units of a population.
Furthermore, population numbers are frequently so high that studying all units would be costly but also inconvenient and time-consuming. A representative few, i.e. a sample from the population, will be chosen by the researcher for the survey, which is referred to as sampling.
However, the advantage of a sample survey is that this error can be quantified and controlled, and it may be reduced to a large extent by using well-trained surveyors. Another advantage of sample surveys is that they are less time-consuming and expensive. In most cases, the population is too huge for the researcher to poll all its members. A small but well-chosen sample might be employed to reflect the population, and the population features from which the sample was chosen are reflected.
There are broadly two types of sampling: i) Probability sampling and ii) Non-probability sampling.
Every population element has a known likelihood of being picked in probability sampling methods. Please remember that "known chance" does not imply "equal chance." Equal chance probability sampling is a type of probability sampling that is also known as simple random sampling. Because there is no possibility of arbitrary or biased selection in probability sampling procedures, the rules of probability apply. As a result, we can calculate the sampling error, which is the difference between the population and sample values.
Simple random sampling is a technique in which each element in the population has an equal probability of being included in the sample. The components are chosen from a list of random numbers in most research and statistics textbooks. Before employing the random number table, it is required first to identify all of the population's items to be investigated. The table is then marked at some position, and the cases whose numbers come up as one from this point along the column of numbers are sampled until the appropriate number of elements is attained. The selection of any given element has no bearing on any other element, making the selection of any of the many conceivable combinations of elements equally feasible.
A systematic Random Sample, like a Simple Random Sample, employs a list of all population members in its sampling frame because designing one may be difficult and time intensive. Instead of utilizing random numbers to choose the sample components, the researcher uses a skip interval on the list to get the appropriate sample size.
Skip interval = number of elements in the population/the required sample size
In certain circumstances, the sampled population is not homogeneous. As a result, rather than randomly picking from the total population, the primary population is separated into several strata, each of which is homogenous in terms of one or more characteristics. The sample items are then selected at random from each stratum. As a result, the sample includes representatives from all strata.
This sampling method is known as stratified random sampling because the population is divided into sub-populations, and the condition of random selection is included in the stratum selection. There needs to be more than just making a list of items for stratified random sampling (and estimating the number of elements on the list). It also entails categorizing the list into subgroups (or strata) and then sampling randomly or systematically within those subgroups.
The proportion of Probability to Size (PPS) Sample ensures a better chance of selection to bigger sampling units. Because productivity is directly proportional to field size, this approach was first used to estimate crop output, fruit production, and so on. The size of the population influences the features of the village population in social science surveys.
Cluster sampling is a strategy used when natural groups in a statistical population are obvious. It is frequently used in market research. The complete population is split into these recognized groups (or clusters) in this approach, and a sample of the groupings is chosen. The necessary information is then gathered from the elements inside each selected category. This can be done for every element in these groupings, or a subset of items from each group can be chosen. When the majority of the diversity in the population is inside the groups rather than between them, the approach works well.
A non-probability sample is one in which a case in a sample is picked to provide information for the sample and allows you to generalize the findings to the population with a given degree of precision. A sample of this type is also known as a purposive sample. This type of sampling is usually used to gather information on market surveys to learn about people's attitudes, opinions, behavior, and responses. Non-probability samples include snowball sampling, convenience sampling, purposive/judgment sampling, quota sampling, and others.
The convenience sample is so named because it is generally easy to collect and contact. In this strategy, investigators are typically requested to pick persons for interviews based on the researcher's instructions. The advantage of a convenience sample is that the interviewer can typically complete interviews quickly and cheaply. Convenience sampling is ideal for exploratory research.
A convenience sample is comparable to a judgment sample. In a judgment sample, the researcher chooses samples that are thought to be representative of the population. The sample is chosen based on knowledge about the population and the qualities that the sample is to reflect. It is less expensive and quite beneficial for predicting.
Quota sampling is analogous to stratified sampling. In quota sampling, the population is divided into strata of the expected size, and the samples are considered significant for the population they represent. The benefits of quota sampling include a shorter time frame, lower costs, and reasonable representation of a heterogeneous population.
This is a significant form of non-probability sampling. In snowball sampling, the investigator encourages responders to provide the names of other contacts, and the sample grows in size and quantity until the study goal is met. As a result, it is also known as a networking, chain, or referenced sampling technique. It is less expensive and highly beneficial in the study of networking.
The sampling technique chosen is determined by various factors unique to each project. These include population definition, the availability of information regarding population structure, the parameters to be estimated, the aims of the study, including the degree of precision necessary, and the availability of financial and other resources. This necessitates the proper selection of a sample for any research project.
A good sample should have the following properties −
Representativeness and
Adequacy.
If the information from the sample is to be generalized for that population, it must be representative of that population. The phrase "representative sample" refers to a perfect "miniature" or "copy" of the population from which it was gathered. In other words, the average of the sample elements' properties is the same as or extremely close to the population average.
A good sample should also be 'adequate' or large enough to provide confidence in the stability of its features. A sufficient sample has enough examples to achieve trustworthy results. A good sample should also be 'adequate' or large enough to provide confidence in the stability of its features. A sufficient sample has enough examples to achieve trustworthy results. As a result, it is critical to plan the sample size ahead of time. It changes depending on the type of traits being studied and their distribution. It should be noted that representativeness and adequacy do not guarantee the correctness of results. Sampling and data-gathering strategies must be properly selected and applied to acquire higher precision in results and generalizations about the population.
The following are some examples of sampling procedure errors−
Accuracy − Compared to a census, observations from a sample may have more flaws, making it less accurate than a census approach.
Unit Changeability − In the survey field, units might change, and if they are not in harmony, the sampling approach can be incorrect. Extending results from one set to another will no longer be scientific.
False conclusions− : If we do not care enough when picking samples, the survey findings may be incorrect if applied to all units. If we return to our previous example of food expenditure and choose just well-off students, the result for food expense will be deceptive if applied to the entire institution.
Need for Specific Knowledge or Expertise − The sampling approach's effectiveness depends on the researcher's skills; any researcher with a substandard skill level might jeopardize the entire selection process.
Impossible Sampling − Several scenarios make using the sampling approach impossible. This strategy cannot be used if we require 100% accuracy or a limited time to decide. When the material is diversified in nature, it may not be employed.
A population is a well-defined set of units such as persons, things, properties, qualities, human characteristics, and so on. A sample is a subset of a larger population and a small representation of the complete group from which it was chosen. In order to generate a representative sample, the unit must be chosen in a certain manner. This is known as sampling. It generally consists of the four phases listed below: (i) Define the population; (ii) List the population; (iii) Choose a representative sample, and (iv) Obtain a sufficient sample. Sampling techniques are divided into two categories: There are two types of sampling: probability sampling and non-probability sampling.
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