Sample Survey Designs: Basic Concept (Section 2)

1. Advantages and Disadvantage of Census and Sample Surveys
   1. The total count of all units of the population for a certain characteristic is known as complete enumeration, also termed census survey. The money, manpower and time required for carrying out complete enumeration will generally be large and there are many situations with limited means where complete enumeration is not possible. There are also instances where it is not practicable to enumerate all units due to their perishable nature where recourse to selection of a few units will be helpful. When only a part, called a sample, is selected from the population and it is further examined, it is known as sample enumeration, or sample survey.
   2. A sample survey will usually be less expensive than a census survey and the desired information will be obtained in much less time. This does not imply that economy is the only consideration in conducting a sample survey. It is most important that a degree of accuracy of results is also maintained. Occasionally, the technique of sample survey is applied to verify the results obtained from a census survey. It has been a well-established fact that in many situations a well-conducted sample survey can provide much more precise results than a census survey. The relative merits and demerits of sample surveys vis-à-vis census surveys have been discussed by Mahalanobis (1950), Yates (1953), Zarkovich (1961) and Lahiri (1963). Cochran (1977) has very lucidly shown the advantages of sample surveys over census surveys. In brief, these are:
      1.                                                               i.      Reduced cost of survey,
      2.                                                             ii.      Greater speed of obtaining results,
      3.                                                           iii.      Greater accuracy of results,
      4.                                                           iv.      Greater scope, and
      5.                                                             v.      Adaptability.
   3. Fisher (1950) sums up the merits of sample surveys over census surveys as follows:
      1.                                                               i.      “I have made four claims for the sampling procedure. About the first three, which are adaptability, speed and economy, I need say nothing further. Too many examples are already available to show how much the method has to give in these ways. But, why do I say that it is more scientific than the only procedure with which it may sometimes be in competition, complete enumeration? The answer, in my view, lies in the primary process of designing and planning an enquiry with the help of sampling. Rooted as it is in the mathematical theory of the errors of randomised sampling, the idea of precision is from the first in the forefront. The director of the survey plans from the first for a predetermined and known level of precision; it is a consideration of which he never loses sight, and precision actually attained, subject to well-understood precautions, is manifest from the results of the enquiry.”
   4. Despite the above advantages, sample surveys are not always preferred to census surveys. Sampling theory has its own limitations and the advantages of sampling over complete enumeration can be derived only if
      1.                                                               i.      The units are drawn in a scientific manner,
      2.                                                             ii.      An appropriate sampling technique is used, and
      3.                                                           iii.      The size of units selected in the sample is sufficient. If information is required for each unit, census is the only answer.

1. Principles of Sampling Theory
   1. The main aim of sampling theory is to make sampling more effective so that the answer to a particular question is given in a valid, efficient and economical way. The theory of sampling is based in thee important basic principles:
      1.                                                               i.      Principle of Validity,
      2.                                                             ii.      Principle of Statistical Regularity, and
      3.                                                           iii.      Principle of Optimisation.

1.        I.            Principle of Validity
   1.                                 i.            This principle states that the sampling design provides valid estimates about the population parameters. By valid we mean that the sample should be so selected that the estimates could be interpreted objectively and in terms of probability. Therefore, the principle ensures that there is some definite and pre-assigned probability for each individual in the sampling design.
2.     II.            Principle of Statistical Regularity
   1.                                 i.            The principle of statistical regularity, which has its origin in the theory of probability, can be explained in these words:

“A moderately large number of items chosen at random from a large group are almost sure on the average to possess the characteristics of the large group.”

1.                               ii.            This principle stresses upon the desirability and importance of selecting sample designs where inclusion of sampling units in the sample is based in probability theory.
2.  III.            Principle of Optimisation
   1.                                 i.            This principle takes into account the desirability of obtaining a sampling design which gives optimum results. In other words, optimisation is meant to develop methods of sample selection and of estimation those provide:

a)A given level of efficiency with the minimum possible resources, or

b)                        A given value of cost with the minimum possible efficiency.

1.                               ii.            Thus, the principle of optimisation minimises the risk of loss of sampling design, i.e., the principle stresses upon obtaining optimum results with minimisation of the total loss in terms of cost and mean square error

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