Research Aptitude Free Study Material (UGC NET Updated Notes 2020)

Type of Sampling

There are basically two types of sampling methods:

  1. Probability Sampling
  2. Non-Probability

1. Probability Sampling

It is also known as ‘random sampling’ or ‘chance sampling’. In probability Sampling the chance of every unit in the population being included in the sample is known. Selection of the specific units in the sample depends entirely on chance.

Types of Probability Sampling

i. Simple Random Sampling

ii. Systematic Sampling

iii. Stratified Sampling

iv. Cluster Sampling

v. Multistage  Sampling

i. Simple Random Sampling

Simple random sampling is the simplest type of sampling, in which we draw a sample of size (n) in such a way that each of the ‘N’ members of the population has the same chance of being included in the sample. A sample selected in this way is called a simple random sample.

Principles for selection of Simple Random Sampling

a) The universe should consist of large number of small units.

b) There should be a ready list of universe.

c) Methods of selection should be independent.

d) The sample units should be accessible for investigation.

e) Once selected, the units should be discarded.

f) All the units must be clearly defined.

g) The units should be equal in size.

h) All the units should be independent of each other.

Simple random samples are drawn using the following methods:

i) Lottery method

Under this method numbers or names of various units of universe are written on chits and put in a bowl and mixed thoroughly. Then the needed chits are drawn in random manner.

ii) Tippett’s numbers

These are the tables of random numbers that have been constructed by Tippetts with 41600 digits. They are combined into  10400 sets of  four figure random numbers. Suppose 50 persons are to be drawn from a list of 6000 persons, we have to number each unit from 0 to 6000 and select any fifty numbers.

iii. Selection from sequential list

Under this system names are arranged in same order serial, alphabetical or geographical. Out of this every 5th or 10th or any other number may be drawn.

iv. Grid system

According to this method a group of entire area is prepared and screen with squares is placed upon the map. Some squares are selected at random. The screen is placed on the map and the areas falling in the selected squares are taken as samples.


  1. If applied appropriately, simple random sampling is associated with the minimum amount of sampling bias compared to other sampling methods.
  2. Given the large sample frame is available, the ease of forming the sample group i.e. selecting samples is one of the main advantages of simple random sampling.
  3. Research findings resulting from the application of simple random sampling can be generalized due to representativeness of this sampling technique and a little relevance of bias.


  1. It is important to note that application of random sampling method requires a list of all potential respondents (sampling frame) to be available beforehand and this can be costly and time-consuming for large studies.
  2. The necessity to have a large sample size can be a major disadvantage in practical levels
  3. This sampling method is not suitable for studies that involve face-to-face interviews covering a large geographical area due to cost and time considerations

ii. Systematic Random Sampling

With systematic random sampling, we create a list of every member of the population. From the list, we randomly select the first sample element from the first k elements on the population list. Thereafter, we select every kth element on the list.

This method is different from simple random sampling since every possible sample of n elements is not equally likely.


  1. It can be taken as an improvement over a simple random sample in as much as the systematic sample is spread more evenly over the entire population.
  2. It is an easier and less costlier method of sampling and can be conveniently used even in case of large populations.


  1. If there is a hidden periodicity in the population, systematic sampling will prove to be an inefficient method of sampling. For instance, every 25th item produced by a certain production process is defective. If we are to select a 4% sample of the items of this process in a systematic manner, we would either get all defective items or all good items in our sample depending upon the random starting position.
  2. A population needs to exhibit a natural degree of randomness along the chosen metric. If the population has a type of standardized pattern, the risk of accidentally choosing very common cases is more apparent.

iii. Stratified Sampling

With stratified sampling, the population is divided into groups, based on some characteristic. Then, within each group, a probability sample (often a simple random sample) is selected. In stratified sampling, the groups are called strata.

As an example, suppose we conduct a national survey. We might divide the population into groups or strata, based on geography – north, east, south, and west. Then, within each stratum, we might randomly select survey respondents.


  1. A stratified sample can provide greater precision than a simple random sample of the same size.
  2. Because it provides greater precision, a stratified sample often requires a smaller sample, which saves money.
  3. A stratified sample can guard against an “unrepresentative” sample (e.g., an all-male sample from a mixed-gender population).
  4. We can ensure that we obtain sufficient sample points to support a separate analysis of any subgroup.


  1. It may require more administrative effort than a simple random sample.
  2. The analysis is computationally more complex.

iv. Cluster Sampling

If the total area of interest happens to be a big one, a convenient way in which a sample can be taken is to divide the area into a number of smaller non-overlapping areas and then to randomly select a number of these smaller areas (usually called clusters), with the ultimate sample consisting of all (or sample consisting of all (or samples of) units in these small areas or clusters. In cluster sampling the total population is divided into a number of relatively small subdivisions which are themselves clusters of still smaller units and then some of these clusters are randomly selected for inclusion in the overall sample. Suppose we want to estimate the proportion of machine- parts in an inventory which are defective. Also assume that there are 20000 machine parts in the inventory at a given point of time, stored in 400 cases of 50 each. Now using a cluster sampling, we would consider the 400 cases as clusters and randomly select ‘n‘ cases and examine all the machine- parts in each randomly selected case.

Area Sampling

If clusters happen to be some geographic subdivisions, in that case cluster sampling is better known as area sampling. In other words, cluster designs, where the primary sampling unit represents a cluster of units based on geographic area, are distinguished as area sampling. The plus minus points of cluster sampling are also applicable to area sampling.


  1. Cluster sampling is less expensive and more quick. It is more economical to observe clusters of units in a population than randomly selected units scattered over throughout the state.
  2. Cluster Sample permits each accumulation of large samples.
  3. The loss of precision per individual case is more than compensated for by the possibility of studying larger samples for the same cost.
  4. Cluster sample may combine the advantages of both random sampling as well as stratified sampling.
  5. Cluster sampling procedure enables to obtain information from one or more areas.


  1. In a cluster sample, each cluster may be composed of units that is like one another. This may produce large sampling error and reduce the representativeness of the sample.
  2. In Cluster sampling, when unequal size of some of the subsets is selected, an element of sample bias will arise.
  3. This type of sampling may not be possible to apply its findings to another area.
  4. Sometimes, adequate number of cases from the stand point of increasing the precision of sample is not selected, an overlapping effect may take place.

v. Multistage sampling

In multistage sampling, we select a sample by using combinations of different sampling methods.

For example, in Stage 1, we might use cluster sampling to choose clusters from a population. Then, in Stage 2, we might use simple random sampling to select a subset of elements from each chosen cluster for the final sample.


  1. It is very flexible as compared to other methods of sampling.
  2. In this method, the subsequent stages of samples are needed only for a limited number of units i.e., for those only which were selected in the preceding stages. As such it saves a lot of time, energy and cost.
  3. It leads to administrative efficiency by permitting the field work to be concentrated and yet covering a large area.
  4. 4. It is of great utility in surveys of undeveloped areas where no up to-date and accurate frame is available for subdivision of the materials into reasonably small sample units.


  1. It is likely to cause a large number of errors as it involves a process of divisions and sub-divisions of the various strata or clusters in different stages.
  2. It leads to greater variability of the estimates than any other method of sampling,
  3. In general, it is less efficient than a suitable single stage random sampling.
  1. Non-Probability Sampling

In this type of sampling, items for the sample are selected deliberately by the researcher; his choice concerning the items remains supreme. In other words, under non-probability sampling the organizers of the inquiry purposively choose the particular units of the universe for constituting a sample on the basis that the small mass that they so select out of a huge one will be typical or representative of the whole. For instance, if economic conditions of people living in a state are to be studied, a few towns and villages may be purposively selected for intensive study on the principle that they can be representative of the entire state.

i. Convenience Sampling/Availability Sampling

As the name implies, the selection of the sample is left to the researcher who is to select the sample. The researcher normally interviews persons in groups at some retail outlet, supermarket or may stand at a prominent point and interview the persons who happen to be there. This type of sampling is also called ‘accidental sampling’ as the respondents in the sample are included merely because of their presence on the spot. The data collection and sample cost is minimum in this case. However, the method suffers greatly from the quality, i.e. accuracy point of view which can in no way be determined. However, this type of sampling is more suitable in ‘exploratory research’ where focus is on getting new ideas/insights into a given problem.


1. Simplicity of sampling and the ease of research.

2. Helpful for pilot studies and for hypothesis generation.

3. Data collection can be facilitated in short duration of time.

4.  Cheapest to implement than alternative sampling methods.


1.  Highly vulnerable to selection bias and influences beyond the control of the researcher.

2. High level of sampling error.

3. Studies that use convenience sampling have little credibility.

ii. Judgment Sampling/Purposive Sampling

In judgment sampling, the judgment or opinion of some experts forms the basis of the sampling method. It is expected that these samples would be better as the experts are supposed to know the population. However, as the use of randomness is not there and moreover there is no way to find the accuracy of the samples, hence the method has its limitations and dis used mainly for situations requiring extremely small size of samples.


  1. Judgement sampling reduces cost and time in preparing the sample
  2. Judgement sampling method enables the researcher to include the positive aspects of stratification in the sample.


  1. There is uncontrolled variability and bias in the estimates in judgement sampling.
  2. The success of judgement sampling method is solely dependent on a thorough knowledge of the population and elimination of the use of inferential parametric statistical tools for the purpose of generalization.
  3. Complete reliance on intuition and hunch is risky in judgement sampling.

iii. Quota Sampling

Quota sampling is a non-probability sampling technique in which researchers look for a specific characteristic in their respondents, and then take a tailored sample that is in proportion to a population of interest. For example when a researcher seeks to conduct a comparative market analysis of how a product is dealt with, by different age groups, socio-economic backgrounds and also gender. Quotas are created within the target population according to these three variables.


  1. Quick and simple methods.
  2. The budget required for executing this sampling method is minimalistic.
  3. 3. Quota sampling is not dependent on the presence of the sampling frames. In occasions where suitable sampling frame is absent, quota sampling may be the only appropriate choice available.


  1. In quota sampling it is not possible to calculate the sampling error and the projection of the research findings to the total population is risky.
  2. While this sampling technique might be very representative of the quota-defining characteristics, other important characteristics may be disproportionately represented in the final sample group.
  3. There is a great potential for researcher bias and the quality of work may suffer due to researcher incompetency and/or lack of experience.

iv. Snowball Sampling

Snowball sampling is particularly appropriate when the population you are interested in is hidden and/or hard-to-reach. These include populations such as drug addicts, homeless people, and individuals with AIDS/HIV, prostitutes, and so forth. Snowball sampling is also known as cold-calling, chain sampling, chain-referral sampling, and referral sampling.


  1. Quick access to your sample can help you to complete your research quicker, giving a more efficient timeline.
  2. It is a novel way in getting access to hidden populations of your identified sample.


  1. It us usually impossible to determine the sampling error or make inferences about populations based on the obtained sample.

v. Consecutive sampling

Consecutive sampling, also known as total enumerative sampling, is a sampling technique in which every subject meeting the criteria of inclusion is selected until the required sample size is achieved. Along with convenience sampling and snowball sampling, consecutive sampling is one of the most commonly used kinds of nonprobability sampling. Consecutive sampling is typically better than convenience sampling in controlling sampling bias.


  1. In consecutive sampling technique, the researcher has many options when it comes to sample size and sampling schedule.
  2. Very little effort is needed from the researcher’s end to carry out the research. This technique is not time-consuming and doesn’t require extensive workforce.


  1. This sampling method cannot be considered as a representative of the entire population. The only way this sampling technique can get any closer to representativeness is by using a large sample size that represents a population.
  2. Since there is a disadvantage of a sample obtained cannot be randomized, results or conclusions drawn through this sampling technique cannot be used to represent an entire population.
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