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.


Types of Sampling

Sampling can be basically categorized into probability and non-probability sampling. In probability sampling, each and every element of the population has a probability of being selected in the sample, i.e., the probability can be accurately measured. Whereas, in non-probability sampling, not all elements have a chance of being selected in the sample, i.e., their probability cannot be accurately measured.

The commonly used sampling methods are given below:

»  Deliberate sampling: It is a non-probability sample design in which the researcher purposively or deliberately selects certain units of the universe to form a sample that would represent the universe. In other words, it is a sampling with a purpose. It is also known as purposive sampling.


»  Simple random sampling: It is a probability sample design where each and every element has an equal probability of being selected in the sample. It is also known as chance sampling.


»  Systematic sampling: In this method, elements from a large population are selected at periodic intervals according to a random starting point, i.e., every nth element is selected for the sample, where n can be any random position of an element.


»  Stratified sampling: In this method, the researcher divides the entire population into different subgroups or strata, and then randomly selects elements proportionally from the strata to include in the sample.


»  Quota sampling: It is a non-probability sample in which the researcher selects random units for a sample according to certain given criteria or quota. In other words, elements are selected according to pre-specified criteria in such a way that the sample represents the same characteristics of the population under study.



A sample is a small portion or unit of something that is measured to make a general statement about the whole thing. Technically speaking, items that researchers collect for performing their research work are known as samples. These units or samples represent the larger group from which they are selected. The basic process of selecting samples is called sampling. Sampling makes studying different characteristics of a large, heterogeneous population possible. It reduces the study population to a reasonable size, thus, greatly reducing the research expenses and speeding up the research process. It helps to finish any research within a reasonable period of time. Sampling provides true, valid, accurate and reasonable data. It also saves the consumption of all data resources. Sampling targets a specific small population and provides precise and typically accurate results.


Preparing the Research Design

Once the research problem has been identified, the next task for the researcher is preparing the research design. According to Russell Ackoff, “research design is the process of making decisions before a situation arises in which the decision has to be carried out.” It is the conceptual framework within which the research would be carried out. It is a key aspect as it binds the research project together. Its aim is to provide for the collection of relevant information with minimal expenditure of effort, time and money.

But, whether this can be achieved depends upon a large extent on the research purpose, which is classified into four categories: (i) Exploratory; (ii) Description; (iii) Diagnosis; and (iv) Experimentation. For an exploratory research study, a flexible research design is more appropriate as it provides ample scope for researching various aspects of a problem (E.g.Types of vehicles suitable for the Indian market. This topic provides extensive scope for writing). For a research paper, which requires an accurate description, the research design should be formulated in such a way that, it is unbiased and vouches for the reliability of the collected data and analyzed (E.g.: Percentage of small car segment in Indian market. This topic needs accurate facts and figures).

There are various kinds of research designs, such as, experimental (independent variable is manipulated) and non-experimental (independent variable is not manipulated) hypothesis-testing. Experimental designs can be further grouped into informal and formal. Informal experimental design normally uses a less sophisticated form of analysis. It includes: before and after without control design; after only with control design; before and after with control design. Formal experimental design offers relatively more control and uses precise statistical procedures for analysis. It includes: completely randomized design; randomized block design; Latin square design; and factorial designs.

Important factors to remember while preparing the research design:

  • Objectives of the research study;
  • Means of obtaining the information;
  • Tools for data collection;
  • Data analysis (qualitative and quantitative);
  • Time available for each stage of the research; and
  • Cost involved for the research.

A well-planned research design serves as a blueprint for the researcher even before he actually starts working on his research. This helps him to decide his course of action during various stages of the research, thus saving his time and resources.