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. Sample design also leads to a procedure to tell the number of items to be included in the sample i.e., the size of the sample. Hence, sample design is determined before the collection of data. Among various types of sample design technique, the researcher should choose that samples which are reliable and appropriate for his research study.
Steps in Sample Design
There are various steps which the researcher should follow. Those are;
- Type of universe: In the first step the researcher should clarify and should be expert in the study of universe. The universe may be finite (no of items are know) or Infinite (numbers of items are not know).
- Sampling unit: A decision has to be taken concerning a sampling unit before selecting a sample. Sampling unit may be a geographical one such as state, district, village etc., or construction unit such as house, flat, etc., or it may be a social unit such as family, club, school etc., or it may be an individual.
- Source list: Source list is known as ‘sampling frame’ from which sample is to be drawn. It consists the names of all items of a universe. Such a list would be comprehensive, correct, reliable and appropriate and the source list should be a representative of the population.
- Size of sample: Size of sample refers to the number of items to be selected from the universe to constitute a sample. Selection of sample size is a headache to the researcher. The size should not be too large or too small rather it should be optimum. An optimum sample is one which fulfills the requirements of efficiency, representativeness, reliability and flexibility. The parameters of interest in a research study must be kept in view, while deciding the size of the sample. Cost factor i.e., budgetary conditions should also be taken into consideration.
- Sampling procedure: In the final step of the sample design, a researcher must decide the type of the sample s/he will use i.e., s/he must decide about the techniques to be used in selecting the items for the sample.
Criteria for Sample Design Selection
While selecting samples a researcher must remember that the procedure of sampling analysis involves two costs viz., (i) the cost of collecting the data and (ii) the cost of an incorrect inferences resulting from the data. So, far as the cost of collecting data is concerned, it completely depends on the researcher to reduce it and to some extent it is within the control of the researcher. But the real problem arises while taking into account about the cost of incorrect inferences which is again of two types,
- Systematic bias and
- Sampling error.
Systematic bias results from errors in the sampling procedures, and it cannot be reduced or eliminated by increasing the sample size. It can be eliminated by eliminating and correcting the causes which are responsible for its occurrence. Following are some causes of the occurrence of systematic bias which requires concern to the researcher.
- Inappropriate sampling frame: If the sampling frame is inappropriate i.e., a biased representation of the universe, then it will result in a systematic bias.
- Defective measuring device: The second cause of occurrence of systematic bias is the selection of defective measuring devices. The measuring devices may be the interviewers; the questionnaire or other instrument used to collect data or may be physical measuring devices. If the questionnaire or the interviewer is biased and/or if the physical measuring device is defective this will lead to the occurrence of systematic bias.
- Non-respondents: If the researcher is unable to sample all the individuals initially included in the sample, there may arise a systematic bias. The reason is that in such a situation the likelihood of establishing correct or receiving a response from an individual is often corrected with the measure of what is to be estimated.
- Natural bias in the reporting of data: There is usually a downward bias in the individual income data collected by the income tax department where as an upward bias is found in the income data collected by some social organizations. People give less income data when asked for income tax but they overstate when asked for social status.
- Indeterminacy principle: Same times a researcher finds that individuals act differently when kept under observations than what they do when kept in non-observed situation.
Sampling errors on the other hand, is the random variations in the sample estimated around the true population parameters. Since they occur randomly and are equally likely to be in either direction, their nature happens to be of compensatory type and the expected value of such errors happens to be equal to zero. Sampling error decreases with the increase in sample size and it happens to be a smaller magnitude in case where the population is characterized as homogeneous. Sampling error can be measured for a given sampling design and size which is called as ‘a precision of the sampling plan’. If the sample size is increased, the precision can be improved but increase in sample size causes limitations like cost of collecting data, and also increases the systematic bias. Thus the effective way to increase the precision is usually to select a better sampling design which has a smaller sampling error for a given sample size at a given cost. Therefore, it shows that while selecting a sampling procedure the researcher must ensure that the procedure causes a relatively small sampling error and helps to control the systematic bias in a better way.
Characteristics of a Good Sample Design
The characteristics of a good sample as follows;
- Sample design must result in a truly representative sample,
- Sample design must be such which results in a small sampling error,
- Sampling design must be viable in the context of funds available for the research study,
- Sample design must be such that systematic bias can be controlled in a better way, and
- 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.