Chapter 4

Chapter 4

1. Sampling Design

   • Census and Sample Survey
     • All items in any field of inquiry constitute a ‘Universe’ or ‘Population.’ A complete enumeration of all items in the ‘population’ is known as a census inquiry. It can be presumed that in such an inquiry, when all items are covered, no element of chance is left and highest accuracy is obtained.
   • Implications of a Sample Design
     • A sample design is a definite plan for obtaining a sample from a given population. It refers to the technique or the procedure the researcher would adopt in selecting items for the sample.
       • Steps in Sample Design
         • While developing a sampling design, the researcher must pay attention to the following points:
           • (i) Type of universe: The first step in developing any sample design is to clearly define the set of objects, technically called the Universe, to be studied.
             • (ii) Sampling unit: A decision has to be taken concerning a sampling unit before selecting sample.
               • (iv) Size of sample: This refers to the number of items to be selected from the universe to constitute a sample.
                 • (v) Parameters of interest: In determining the sample design, one must consider the question of the specific population parameters which are of interest. For instance, we may be interested in estimating the proportion of persons with some characteristic in the population, or we may be interested in knowing some average or the other measure concerning the population.
                   • (vi) Budgetary constraint: Cost considerations, from practical point of view, have a major impact upon decisions relating to not only the size of the sample but also to the type of sample.
                     • (vii) Sampling procedure: Finally, the researcher must decide the type of sample he will use i.e., he must decide about the technique to be used in selecting the items for the sample.
   • Criteria of Selecting a Sampling Procedure
     • In this context one must remember that two costs are involved in a sampling analysis viz., the cost of collecting the data and the cost of an incorrect inference resulting from the data.
       • At best the causes responsible for these errors can be detected and corrected. Usually a systematic bias is the result of one or more of the following factors:
         • 1. Inappropriate sampling frame: If the sampling frame is inappropriate i.e., a biased representation of the universe, it will result in a systematic bias.
           • 2. Defective measuring device: If the measuring device is constantly in error, it will result in systematic bias. In survey work, systematic bias can result if the questionnaire or the interviewer is biased.
             • 3. Non-respondents: If we are unable to sample all the individuals initially included in the sample, there may arise a systematic bias.
               • 4. Indeterminancy principle: Sometimes we find that individuals act differently when kept under observation than what they do when kept in non-observed situations.
                 • 5. Natural bias in the reporting of data: Natural bias of respondents in the reporting of data is often the cause of a systematic bias in many inquiries.
   • Characteristics of Good Sample Design
     • From what has been stated above, we can list down the characteristics of a good sample design as under:
       • (a) Sample design must result in a truly representative sample.
         • (b) Sample design must be such which results in a small sampling error.
           • (c) Sample design must be viable in the context of funds available for the research study.
             • (d) Sample design must be such so that systematic bias can be controlled in a better way.
               • (e) Sample should be such that the results of the sample study can be applied, in general, for the universe with a reasonable level of confidence.
   • Different Types of Sample Design
     • There are different types of sample designs based on two factors viz., the representation basis and the element selection technique. On the representation basis, the sample may be probability sampling or it may be non-probability sampling. Probability sampling is based on the concept of random selection, whereas non-probability sampling is ‘non-random’ sampling
       • Non-probability sampling: Non-probability sampling is that sampling procedure which does not afford any basis for estimating the probability that each item in the population has of being included in the sample.
       • Probability sampling: Probability sampling is also known as ‘random sampling’ or ‘chance sampling’. Under this sampling design, every item of the universe has an equal chance of inclusion in the sample.