What is Sample Design? Characteristics, Elements, Determination

What is Sample Design?

A sample design is a definite plan for obtaining a sample from a given population Sample constitutes a certain portion of the population or universe. Sampling design refers to the technique or the procedure the researcher adopts for selecting items for the sample from the population or universe.

A sample design helps to decide the number of items to be included in the sample, i.e., the size of the sample. The sample design should be determined prior to data collection. There are different kinds of sample designs which a researcher can choose. Some of them are relatively more precise and easier to adopt than the others. A researcher should prepare or select a sample design, which must be reliable and suitable for the research study proposed to be undertaken.

Every research study requires the selection of some kind of sample. It is the life blood of research. Any research study aims to obtain information about the characteristics or parameters of a population.

A population is the aggregate of all the elements that share some common set of characteristics and that comprise the universe for the purpose of the research problem. In other words, population is defined as the totality of all cases that conform to some designated specifications. The specification helps the researcher to define the elements that ought to be included and to be excluded.

Sometimes, groups that are of, interest to the researcher may be significantly smaller allowing the researcher to collect data from all the elements of population. Collection of data from the entire population is referred to as census study. A census involves a complete enumeration of the elements of a population.

Collecting data from the aggregate of all the elements in case of, the number of elements being larger, would sometimes render the researcher incur huge costs and time. Hence, Sampling is the process of selecting units (e.g., people, organizations) from a population of interest so that by studying the sample we may fairly generalize our results back to the population from which they were chosen.

While deciding on the sampling, the researcher should clearly define the target population without allowing any kind of ambiguity and inconsistency on the boundary of the aggregate set of respondents.

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Characteristics of a Good Sample Design

The following are the characteristic features of a good sample design:

• The sample design should yield a truly representative sample.
• The sample design should be such that it results in small sampling error.
• The sample design should be viable in the context of budgetary constraints of the research study.
• The sample design should be such that the systematic bias can be controlled.
• The sample must be such that the results of the sample study would be applicable, in general,
• to the universe at a reasonable level of confidence.

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Elements of Sampling Design

A researcher should take into consideration the following aspects while developing a sample design:

Type of Universe

The first step involved in developing sample design is to clearly define the number of cases, technically known as the Universe, to be studied. A universe may be finite or infinite. In a finite universe the number of items is certain, whereas in the case of an infinite universe the number of items is infinite (i.e., there is no idea about the total number of items).

For example, while the population of a city or the number of workers in a factory comprise finite universes, the number of stars in the sky or throwing of a dice represent infinite universe.

Sampling Unit

Prior to selecting a sample a decision has to be made about the sampling unit. A sampling unit may be a geographical area like a state, district, village etc. or a social unit like a family, religious community, school, etc. or it may also be an individual. At times, the researcher would have to choose one or more of such units for his/her study.

Source List

Source list is also known as the ‘sampling frame’, from which the sample is to be selected. The source list consists of names of all the items of a universe. The researcher has to prepare a source list when it is not available. The source list must be reliable, comprehensive, correct and appropriate. It is important that the source list should be as representative of the population as possible.

Size of the Sample

Size of the sample refers to the number of items to be chosen from the universe to form a sample. The size of sample must be optimum. An optimum sample may be defined as the one that satisfies the requirements of representativeness, flexibility, efficiency, and reliability. While deciding the size of sample a researcher should determine the desired precision and the acceptable confidence level for the estimate.

The size of the population variance should be considered, because in the case of a larger variance generally a larger sample is required. The size of the population should be considered, as it also limits the sample size. The parameters of interest in a research study should also be considered, while deciding the sample size.

Besides, costs or budgetary constraint also plays a crucial role in deciding the sample size.

• Parameters of Interest: The specific population parameters of interest should also be considered while determining the sample design. For example, the researcher may want to make an estimate of the proportion of persons with certain characteristics in the population, or may be interested in knowing some average regarding the population. The population may also consist of important sub-groups about whom the researcher would like to make estimates. All such factors have strong impact on the sample design the researcher selects.
  
  
• Budgetary Constraint: From the practical point of view, cost considerations exercise a major influence on the decisions related to not only the sample size, but also on the type of sample selected. Thus, budgetary constraint could also lead to the adoption of a non-probability sample design.
  
  
• Sampling Procedure: Finally, the researcher should decide the type of sample or the technique to be adopted for selecting the items for a sample. This technique or procedure itself may represent the sample design. There are different sample designs from which a researcher should select one for his/her study. It is clear that the researcher should select that design which, for a given sample size and budget constraint, involves a smaller error.

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Determination of Appropriate Sampling Design

Determining an appropriate sampling design is a challenging issue and has greater implications on the application of the research findings. Apart from considering the theoretical components, sampling issues, advantages and drawbacks of different sampling techniques, the decision should take into consideration the following factors:

Research Objectives

A clear understanding of the statement of the problem and the objectives will provide the initial guidelines for determining the appropriate sampling design. If the research objectives include the need to generalize the findings of the research study, then a probability sampling method should be opted rather than a non-probability sampling method. In addition the type of research viz., exploratory or descriptive will also influence the type of the sampling design.

Scope of the Research

The scope of the research project is local, regional, national or international has an implication on the choice of the sampling method. The geographical proximity of the defined target population elements will influence not only the researcher’s ability to compile needed list of sampling units, but also the selection design.

When the target population is equally distributed geographically a cluster sampling method may become more attractive than other available methods. If the geographical area to be covered is more extensive then complex sampling method should be adopted to ensure proper representation of the target population.

Availability of Resources

The researchers command over the financial and human resources should be considered in deciding the sampling method. If the financial and human resource availability are limited, some of the more time- consuming, complex probability sampling methods cannot be selected for the study.

Time Frame

The researcher who has to meet a short deadline will be more likely to select a simple, less time consuming sampling method rather than a more complex and accurate method.

Advanced Knowledge of the Target Population

If the complete lists of the entire population elements are not available to the researcher, the possibility of the probability sampling method is ruled out. It may dictate that a preliminary study be conducted to generate information to build a sampling frame for the study. The researcher must gain a strong understanding of the key descriptor factors that make up the true members of any target population.

Degree of Accuracy

The degree of accuracy required or the level of tolerance for error may vary from one study to another. If the researcher wants to make predictions or inferences about the ‘true’ position of all members of the defined target population, then some type of probability sampling method should be selected. If the researcher aims to solely identify and obtain preliminary insights into the defined target population, non-probability methods might prove to be more appropriate.

Perceived Statistical Analysis needs

The need for statistical projections or estimates based on the sample results is to be considered. Only probability sampling techniques allow the researcher to adequately use statistical analysis for estimates beyond the sample respondents.

Though the statistical method can be applied on the non-probability samples of people and objects, the researcher’s ability to accurately generalize the results and findings to the larger defined target population is technically inappropriate and questionable. The researcher should also decide on the appropriateness of sample size as it has a direct impact on the data quality, statistical precision and generalization of findings.