Types of Sample Designs

Basically, there are two different types of sample designs, namely, non-probability sampling and probability sampling. Each of the two is described below.

(1) Non-probability sampling: This type of sampling is also known as deliberate sampling, purposive sampling, or judgement sampling. In this sampling procedure, the organisers of the inquiry deliberately choose the particular units of the universe to compose a sample on the basis that the small mass selected out of a large one would represent the whole. For example, if economic conditions of the population living in a state are to be studied, a few cities and towns can be deliberately selected for intensive study on the principle that they can represent the entire state. Besides, the investigator may select a sample yielding results favorable to his point of view. In case that happens, the entire inquiry may get vitiated. Thus, there exists the danger of bias entering into this type of sampling technique. However, if the investigators are impartial, work without bias and have the necessary experience so as to take sound judgement, the obtained results of an analysis of deliberately selected sample may be tolerably reliable.

Quota sampling is also an example of non-probability sampling. In this type of sampling the interviewers are simply given quotas to be filled from the different strata, with some instructions regarding filling up the quotas. Moreover, this type of sampling is relatively inexpensive and quite convenient.

(2) Probability sampling: This type of sampling is also known as random sampling or chance sampling. This sampling procedure gives each element in the population an equal chance of getting selected for the sample; besides, all choices are independent of one another. The obtained results of probability sampling can be assured in terms of probability. In other words, we can measure the errors of estimation or the significance of obtained results from a random sample. In fact, due to this very reason probability sampling design is superior to the deliberate sampling design. Probability sampling ensures the law of Statistical Regularity, which states that if the sample chosen is a random one, the sample will have the same composition and characteristics as the universe. Hence, probability sampling is more or less the best technique to select a representative sample.



Among all the previously discussed sampling techniques, the most widely and effectively used techniques include Deliberate Sampling and Simple Random Sampling.

1. Deliberate Sampling

It is a kind of non-probability sampling that involves the selection of components based on factors excluding random chance. This type of sampling involves the chance of unequal selection of members of the population. Hence, it is not reliable to assume that the sample represents the target population completely, as it might be possible that the researcher intentionally chose the individuals to participate in the study.

Deliberate sampling method is useful for case studies, pilot studies, qualitative research, and hypothesis development. This sampling technique is generally applied in studies, which are not interested in the parameters of the total population. For example, if you are interested to find out the particular reaction of some students on the devaluation of the rupee, then instead of asking the opinions of all students in various college/universities of Delhi, you may deliberately ask only the student leaders of a particular college/university.

Deliberate sampling method is more preferred as it is easy, quick, and cost-effective. However, the findings of the sample survey cannot be universal to the entire population as the sample is not representative. Since there is no set criterion for sample selection, there is a scope for research being persuaded by the preference of the researcher.

2.  Simple Random Sampling

It is a kind of probability sampling, which provides each member of the population with a calculable and non-zero probability of selection in the sample. Since every member is given an equal chance of being selected, this type of sampling is thus considered as a reliable way of selecting a sample from a given population.

The benefits of simple random sampling can be obtained when the target population size is small, homogeneous, and not much information is available regarding the population. For example, if we have a list of 70 heads of households, each having a unique number. We want to select 30 random households from this list. By the help of a random number table, we select consecutive 2-digit numbers from the table. If a random number matches a household’s number, then that household will be added to the list of selected households. Similarly, if a random number does not match a household’s number (e.g., if it is greater than 70), then it is not added to the list of selected households. Each random number that is used is crossed out to avoid repetition. In this way, we continue to select households until we have 30.

Simple random sampling is quite advantageous as it is free of classification error and needs minimum innovative knowledge of the population. However, this sampling method is usually not preferred as it becomes crucial to list every item in the population prior to the sampling and requires a huge sampling frame, which can result in massive sampling calculations and extreme costs.