RELEVANCE OF SAMPLING IN SOCIAL RESEARCH

 

Practice Question  – Why is random sampling said to have more validity and reliability in social research? (UPSC 2015)

Approach – Introduction, Define Sampling, List major sampling methods, Establish the relevance and advantages of random sampling over other methods, Give examples, Conclusion. 

 

INTRODUCTION

The sample method involves taking a representative selection of the population and using the  data collected as research information. A sample is a “subgroup of a population. It has also been described as a representative “taste” of a group. The sample should be representative in the sense that each sampled unit will represent the characteristics of a known number of units in the population. All disciplines conduct research using sampling of the population as a method, and the definition is standard across these disciplines. Only the creative description of sampling changes for purposes of creating understanding. The standard definition always includes the ability of the research to select a portion of the population that is truly representative of said population. Sampling theory is important to understand in regards to selecting a sampling method because it seeks to “make sampling more efficient. Cochran posits that using correct sampling methods allows researchers the ability to reduce research costs, conduct research more efficiently (speed), have greater flexibility, and provides for greater accuracy. According to Mildred Parton, “Sampling method is the process or the method of drawing a definite number of the individuals, cases or the observations from a particular universe, selecting part of a total group for investigation.”

 

BASIC TERMS

Sample: the segment of the population that is selected for investigation. It is a subset of the population. The method of selection may be based on a probability or a non-probability approach.
• Sampling frame: the listing of all units in the population from which the sample will be selected.
• Representative sample: a sample that reflects the population accurately so that it is a microcosm of the population.
Sampling bias: a distortion in the representatives of the sample that arises when some members of the population (or more precisely the sampling frame) stand little or no chance of being selected for inclusion in the sample.
• Sampling error: error in the findings deriving from research due to the difference between a sample and the population from which it is selected. This may occur even though a probability sample has been employed.
Non-probability error: error in the findings deriving from research due to the differences between the population and the sample that arise either from deficiencies in the sampling frame or non-response , or from such problems as poor question wording, poor interviewing or flawed processing data.

 

PRINCIPLES OF SAMPLING

• Sample units must be chosen in a systematic and objective manner.
• Sample units must be clearly defined and easily identifiable.
• Sample units must be independent of each other.
• Same sample units should be used throughout the study.
• The selection process should be based on sound criteria and should avoid errors, bias and distortions.

 

TYES OF SAMPLING

Probability Sampling

In probability sampling methods the universe from which the sample is drawn should be known to the researcher. Under this sampling design every item of the universe has an equal chance of inclusion in the sample. Lottery methods or selecting a student from the complete students names from a box with blind or folded eyes is the best example of random sampling, it is the best technique and unbiased method. It is the best process of selecting representative sample. But the major disadvantage is that for this technique we need the complete sampling frame i.e. the list of the complete items or population which is not always available. Probability sampling methods are of three types

i) Simple random sampling: in this method each element has the equal probability to be selected as a sample. It is bias free. Here an element cannot come twice as sample.

ii) Stratified random sampling: In stratified random sampling the population is first divided into different homogeneous group or strata which may be based upon a single
criterion such as male or female. Or upon combination of more criteria like sex, caste, level of education and so on .this method is generally applied when different category
of individuals constitutes the population viz general. O.B.C, S.C, S.T or upper caste, middle caste, backward caste or small farmers, big farmers, marginal farmers landless farmers etc .To have an actual picture of a particular population about the standard of living, in case of India it is advisable to categorized the population on the basis of caste, religion or land holding otherwise some section may be under-represented or not represented at all.

iii) Cluster sampling: This is another type of probability sampling method, in which the sampling units are not individual elements of the population, but group of elements or group of individuals are selected as sample. In cluster sampling the total population is divided into a number of relatively small sub-divisions or groups which are themselves clusters and then some of these cluster are randomly selected for inclusion in the sample. Suppose an investigator wants to study the functioning of mid day meal service in a district in that case he can use some schools clustering in a block or two without selecting the schools scattering all over the district. Cluster sampling reduces the cost and labour of collecting the data of the investigator but less precise than random sampling.

 

Non Probability Sampling
In this type of sampling, items for the sample are selected deliberately by the researcher instead of using the techniques of random sampling. It is also known as purposive or judgment sampling. For instance an investigator wants to verify the profit making and self dependency of the self help groups in their chosen enterprises assisted by the central Govt. fund in a state; then the investigator may select one or two districts having more number of S.H.G, getting comparatively more fund, and researcher having long term experience in that locality. This is a biased type of sampling bears large sampling errors. This type of sampling is rarely adopted in large and important purposes. However for research purpose this may be taken by the research scholar.
Some important techniques of non probability sampling methods are –
a) Quota sampling
b) Purposive sampling
c) Systematic sampling
d) Snow ball sampling and
e) Double sampling

Quota sampling is defined as a non-probability sampling method in which researchers create a sample involving individuals that represent a population. Researchers choose these individuals according to specific traits or qualities. They decide and create quotas so that the market research samples can be useful in collecting data. 

Purposive sampling (also known as judgment, selective or subjective sampling) is a sampling technique in which researcher relies on his or her own judgment when choosing members of population to participate in the study.

Systematic sampling is a statistical method involving the selection of elements from an ordered sampling frame. The most common form of systematic sampling is an equiprobability method.

Snowball sampling (or chain samplingchain-referral samplingreferral sampling) is a nonprobability sampling technique where existing study subjects recruit future subjects from among their acquaintances. Thus the sample group is said to grow like a rolling snowball. As the sample builds up, enough data are gathered to be useful for research. This sampling technique is often used in hidden populations, such as drug users or sex workers, which are difficult for researchers to access.

Double sampling is a two-phase method of sampling for an experiment, research project, or inspection. An initial sampling run is followed by preliminary analysis, after which another sample is taken and more analysis is run. It is used in three main ways: acceptance/rejection double sampling, ratio double sampling, and stratification double sampling.

 

ADVANTAGES OF SAMPLING
1. Very accurate.
2. Economical in nature.
3. Very reliable.
4. High suitability ratio towards the different surveys.
5. Takes less time.
6. In cases, when the universe is very large, then the sampling method is the only practical
method for collecting the data.

 

DISADVANTAGES OF SAMPLING
1. Inadequacy of the samples.
2. Chances for bias.
3. Problems of accuracy.
4. Difficulty of getting the representative sample.
5. Untrained manpower.
6. Absence of the informants.
7. Chances of committing the errors in sampling.

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