Sampling Design
All items in any field of inquiry constitute a ‘Universe’ or ‘Population’.
- Census inquiry is a complete enumeration of all items in the ‘population’. In such an inquiry, it is presumed that when all items are covered, no element of chance is left, and the highest accuracy is obtained.
When the field of inquiry is large, the census method becomes difficult to adopt due primarily to the extensive resources (time, money, and energy) involved.
Many times, sufficiently accurate results can often be obtained by studying only a portion of the total population. These selected portions are referred to as ‘respondents’.
The selected respondents technically form a ‘sample’, and the process of their selection is known as the ‘sampling technique’.
- A survey conducted on the basis of a sample is termed a ‘sample survey’.
The researcher must prepare a sample design for their study, planning how a sample should be selected and its appropriate size. The sample should truly represent the population characteristics without bias to ensure valid and reliable conclusions.
Implications of a Sample Design
A sample design is a definite plan determined before any data are actually collected, outlining the technique or procedure the researcher would adopt in selecting items for the sample from a given population.
It also specifies the number of items to be included in the sample, which is the ‘size of the sample’.
Steps in Sample Design
When developing a sampling design, the researcher must consider several crucial points:
1. Type of Universe:
The universe, which is the set of objects to be studied, must be clearly defined. A universe can be either finite (a certain number of items, e.g., the population of a city) or infinite (an infinite number of items, e.g., stars in the sky).
2. Sampling Unit:
The researcher must decide on the unit to be selected for the study. This unit could be geographical (e.g., state, district, village), a construction unit (e.g., house, flat), a social unit (e.g., family, club, school), or an individual.
3. Source List (Sampling Frame):
This is a list containing the names of all items in a finite universe from which the sample is to be drawn. If a source list is unavailable, the researcher must create one. It should be comprehensive, correct, reliable, appropriate, and as representative of the population as possible.
4. Size of Sample:
Size of sample is the number of items to be selected from the universe to constitute a sample.
Determining the sample size is a major challenge. The sample should be optimum, meaning it is neither excessively large nor too small.
Key factors influencing sample size include:
- Nature of the universe: A small sample suffices for a homogeneous universe, while a large sample is needed for a heterogeneous one (dispersion factor).
- Number of classes proposed
- Nature of study: Intensive studies may use small samples, while general surveys require larger ones.
- Type of sampling: A small random sample can be superior to a larger, poorly selected one.
- Standard of accuracy and acceptable confidence level: Higher accuracy or precision demands a larger sample.
- Budgetary constraint: Practical cost considerations significantly impact both the size and type of sample, sometimes leading to the use of non-probability samples.
5. Sampling Procedure:
Finally, the researcher must decide on the specific technique for selecting sample items. This technique or procedure effectively is the sample design itself.
The chosen design should result in a relatively small sampling error for a given sample size and cost, and help control systematic bias.
Criteria of Selecting a Sampling Procedure
When selecting a sampling procedure, a researcher must ensure that the chosen method results in a relatively small sampling error and effectively controls for systematic bias.
Sampling Errors
Sampling errors are the random variations that occur in sample estimates when compared to the true population parameters. The magnitude of sampling error is influenced by the nature of the universe; it is smaller in a homogeneous population and increases with heterogeneity.
Systematic Bias
Systematic bias is a consistent, repeatable error that skews data or results in a particular direction due to a flaw in the system, process, or measurement method.
Unlike random errors, which vary unpredictably, systematic bias affects all measurements or observations in the same way, leading to distorted conclusions.
Systematic bias results from errors in the sampling procedures themselves and cannot be reduced or eliminated by merely increasing the sample size. Instead, the causes of these errors must be detected and corrected.
Factors that can lead to systematic bias include:
Inappropriate sampling frame: If the sampling frame is biased and does not accurately represent the universe.
Defective measuring device: A measuring device that is consistently in error.
Non-respondents: If a researcher is unable to collect data from all individuals initially included in the sample.
Indeterminacy principle: Individuals may behave differently when they are aware of being observed compared to when they are not.
Natural bias in the reporting of data: Respondents may have a natural tendency to report data in a biased manner.
A good sample design must strive to achieve a representative sample, minimize sampling error, and control systematic bias effectively, while also being economically viable. This often involves a trade-off between desired accuracy and available resources.
Characteristics of a Good Sample Design
The sample design must result in a truly representative sample.
The sample design must be such which results in a small sampling error.
The sample design must be viable in the context of funds available for the research study.
The sample design must be such that systematic bias can be controlled in a better way.
The 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.