Sampling Process and Characteristics of Good Sample Design

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Sampling Process and Characteristics of Good Sample Design

The sampling design process consists of five steps that are intertwined and critical to every aspect of a research project as mentioned:

Selecting the Population

The target population is the group of people that the researcher believes will provide the knowledge needed to complete the research study. The researcher, while developing a sample design, must choose the population according to his/her research study. Population can be finite or infinite. Population is finite if the number of elements in it are certain and countable. In the case of infinite population, no figure can be given about the number of elements in the population.

First and foremost, the researcher selects the target demographic from the entire population. The target population is the population from whom the researcher wishes to deduce the study’s conclusions. The accessible population is the segment of the target population that the researcher can contact in order to do research. After determining the available population, a sampling frame consisting of all items or elements of the target population is created to extract a sample from it.

The list of all the uniquely identified elements/units in a population from which a sample will be taken is known as sampling frame. The frame aids in the identification of all items in the population, ensuring that everyone has an equal chance of being chosen for the study.

The sample is the unit(s) in which the researcher conducts his investigation. The term “target population” refers to the group of people or items to which researchers want to apply their results. The target population is the group of people or things from which a sample could be taken. A well-defined group decreases the chances of including items that are unsuitable for the research project’s goal.

Selecting the Sampling Frame

In research, sampling frame refers to a list or database of all the items or elements or respondents in the population from which a sample can be chosen. Items or respondents can be selected from sampling frame to be included in the given research project. It is sometimes preferable to choose a list of the population from which the researcher selects units when selecting sample units from the population.

The sampling frame is a collection of people or items (for example, a list of all playgroups in the researcher’s city) from which the researcher will select his or her sample. The sample is drawn from a list of all units in a study population. For example, in order to perform his study, the researcher may include all playgroups in his sampling frame located in his city.

Specifying the Sampling Unit

According to Organisation for Economic Cooperation and Development (OECD), a sampling unit is one of the units into which an aggregate is divided for the purpose of sampling where each unit is regarded as individual and indivisible when selection is made. For example, when a survey of a group of trees in a class is conducted, a single tree is a sampling unit. Each item or unit in the sampling frame is called as sampling unit.

Choosing the Sampling Method

Choosing a sampling technique might take some time and entail a number of options, such as whether to use a Bayesian or classical sampling approach, whether to sample with or without replacement, and whether to employ non-probability or probability sampling. Whether a researcher uses probability sampling technique or nonprobability sampling technique usually depends on the purpose of research.

If the sampling frame is almost identical to the target population, random selection can be employed to select the sample. If, on the other hand, the sampling frame does not accurately reflect the target population, the researcher may opt for a non-random selection method that will give him a rough notion of the population in his immediate vicinity.

Deciding the Sample Size

The number of units to be included in the sample is the sample size. Many factors influence the determination of sample size including time, cost, and facility. Larger samples are better in general, but they need more resources.

Characteristics of a Good Sample Design

Some of the important characteristics of a good sample design are:

  • Sample design should produce a representative sample
  • Sample design should produce a small sampling error
  • Sample design should be feasible within the research study’s budgetary limits
  • Sample design should allow for the control of systematic bias

Sample size must be large enough for the conclusions of the sample study to be generalisable to the entire universe with a fair degree of confidence Provided the researcher wishes to generalise the results Apart from the above-mentioned characteristics, a good sample design must also have the following characteristics:

Goal Orientation

A sample design should be orientated to the research aims, adapted to the survey design, and fitted to the survey conditions. If this is done, it should have an impact on the population selection, measurement, and sample selection procedure.


A sample design should allow meaningful estimates of sampling variability to be computed. In surveys, this variability is typically reported as standard error. However, this is only achievable with probability sampling. It is impossible to know the degree of precision of survey results in non-probability samples, such as a quota sample.


This means that the sample design can be correctly followed in the survey, as planned. Complete, correct, practical, and unambiguous instructions must be provided to the interviewer so that no errors occur in sampling unit selection and the final selection in the field is consistent with the initial sample design. Practicality also relates to the design’s simplicity, or its ability to be understood and followed in actual fieldwork operations.


Finally, economy means that the survey’s goals should be met with the least amount of money and effort possible. Generally, survey objectives are stated in terms of precision, which isdefined as the inverse of the variation of survey estimates. The sample design should provide the lowest cost for a given degree of precision. Alternatively, the sample design should yield maximum precision for a given per unit cost (minimum variance).

Sample Size Decisions

Following our examination of key sample designs, we now shift our attention to another critical component of sampling, namely, sample size decisions. When doing a survey and not being able to reach the complete population, the marketing researcher must first determine how large the sample should be.

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