Sampling: Important Definitions and Comprehensive

Introduction to Sampling: Important Definitions and Comprehensive

In this article we will go through the topic Sampling: Important Definitions and Comprehensive. Sampling in research refers to the process of selecting a subset of individuals or items from a larger population to represent and generalize findings about the entire group.

Introduction

Once the researcher has formulated the research problem and developed a research design including the questionnaire he has to decide whether the information is to be collected from all the objects of interest or from a part of the population. When the data are collected from each member of interest, it is known as census survey. If on the other hand, data are collected only from some members of the population, it is known as sample survey. Thus, the researcher has to decide whether he will conduct a census or a sample survey to collect data needed for his study. Important definitions are crucial before conducting any survey.

Some Important Definitions

Population/Universe

 A collection or an aggregate (finite or infinite) of all the units within the scope of investigation is called ‘population’. For example, if survey is to be carried out about financial status of Life Insurance Corporation, then the whole group of persons who are working in Life Insurance Corporation will be the population.

Census

 When detailed information about each and every individual unit within the scope of investigation is obtained, it is called complete enumeration or census survey.

Sample

 A part of population, or a subset of the set of all the units within the scope of investigation selected by one process or other (usually by deliberate selection/with the objective of investigating the characteristics of the parent population.

Sampling

 Selection of part of an aggregate on the basis of which a judgement or inference about the aggregate is made.

Sample Survey

 A survey which is carried out by using a sampling method i.e. in which a portion only, and not the whole population, is surveyed.

Sampling Unit

One of the units in to which an aggregate or population is divided or regarded as divided for the purpose of sampling.

Sampling Frame

 A list of items from which the sample is to be drawn is known as sampling frame.

Perfect Sampling Frame

 It identifies each element once and only once.

Incomplete Sampling Frame

 When certain valid members of the population are left out during the sampling process.

Inaccurate Sampling Frame

 When some of the sampling units of the population are listed inaccurately or some units which do not actually exist are included.

Inadequate Frame

 A frame which does not include all the units of population.

Out of date Frame

 A frame is out of date when it has not been updated.

Sampling Error

 The part of the difference between a population value (statistical property of the population under study called population parameter) and an estimate thereof, derived from a random sample (statistical property of sample called sample statistic) which is due to the fact that sample survey method is used; as distinct from errors due to imperfect selection, bias in response or estimation errors in observation and recording etc.

Sampling Distribution

 If we select all possible samples of same size from a given population and measure a particular statistical property for all the samples and arrange these values in the form of a frequency distribution, then this distribution is called sampling distribution of that sample characteristic.

Standard Error

 The standard error is a measure of the variability or dispersion of sample statistics such as the mean or proportion in relation to the population parameter. It indicates the accuracy of an estimate and reflects how much the sample statistic is expected to differ from the true population parameter on average. A smaller standard error suggests a more precise estimate, while a larger standard error indicates greater uncertainty in the estimate.

Bias

 Bias refers to the systematic error or deviation of an estimate or measurement from the true value in a consistent direction. It can result from various factors such as flawed study design, measurement errors, or sampling issues. Bias can lead to inaccurate conclusions or predictions and can affect the reliability and validity of research findings.

Biased Sample

 A biased sample is a subset of a population that is not representative of the entire population due to systematic errors or flaws in the sampling method. This can occur when certain groups or individuals are disproportionately included or excluded from the sample, leading to an overestimation or underestimation of population characteristics. Biased samples can result in misleading conclusions and invalidate statistical analyses.

Sampling Error= Frame Error + Chance Error + Response Error

Total Error = Non-Sampling Error +Sampling Error.

Estimation

 Estimation refers to the process of using sample data to make inferences or predictions about population parameters. It involves calculating point estimates, such as means or proportions, and constructing confidence intervals to quantify the uncertainty associated with the estimates. Estimation techniques help researchers draw conclusions about populations based on limited sample information while considering potential sources of error or bias.

Testing of Hypothesis

 Testing of hypothesis, also known as hypothesis testing, is a statistical method used to evaluate the validity of a claim or hypothesis about a population parameter. It involves formulating a null hypothesis (H0) and an alternative hypothesis (H1), collecting sample data, calculating a test statistic, and determining whether the evidence supports rejecting or failing to reject the null hypothesis. Hypothesis testing helps researchers assess the significance of relationships or differences in data and draw conclusions based on statistical evidence.

Sampling: Important Definitions and Comprehensive

Advantages of Sampling

The sampling has a number of advantages as compared to complete enumeration due to variety of reasons.

1. Less expensive

Sampling is less expensive than complete enumeration. For example it is obviously economical to cover a sample of households than all the households of a town.

2. Less time consuming

 Sample studies allow the researcher to conserve his effort and time. The smaller size of the sample enables the researcher to collect the data quickly with less effort. For example, a marketing manager willing to know the reactions of the customers to his new product, can choose few representative customers and go on his study, rather than surveying his entire area of marketing.

3. More detailed and specific information

 A sample enables the researcher to collect more detailed information that would otherwise be possible in a census survey. Specialized information can be gathered through targeted surveys that wouldn’t be feasible in a census due to the limited number of experts available. This specialized data collection involves reaching out to specific groups or individuals with unique knowledge or skills related to the research topic.

By focusing on these specialists, researchers can obtain in-depth insights and expertise that may not be accessible through a broader census approach. This targeted approach allows for a more detailed and nuanced understanding of complex issues or specialized areas of study, enhancing the quality and depth of research findings.

4. Greater accuracy

 It is possible to achieve greater accuracy by using appropriate sampling technique than by a complete enumeration of all the units of the population. The smaller number allows the quality of the field staff to be at higher level. Enhanced scrutiny and validation can be conducted at every phase of the process. As focus intensifies, there is a natural elevation in the precision of data and the thoroughness of analysis.

5. Best studies at times

Sampling may be the only way to conduct a study if the universe is infinite or extremely large. Similarly, sampling is indispensable if enumeration is destructive. For example, if we are interested in computing the average life of electric bulbs supplied in a batch, the life of the entire batch cannot be examined to compute the average as it means that entire supply will be wasted. Thus, in such cases sampling is the only method.

Sampling: Important Definitions and Comprehensive

 Limitations of Sampling

1.When the information is needed on every unit in the population such as individuals (e.g. in decennial census every individual is enumerated) or business establishments, a sample survey cannot be of much help for it fails to provide information on individual count.

2. Sampling introduces specific sampling errors. When these errors exceed acceptable limits, the outcomes of the sample survey become significantly less useful.

3. While in a census survey it may be easy to check the omissions of certain units in view of complete coverage, it is not so in the case of sample survey. From the above discussion it is clear that sampling has the advantages like less cost, saving in time, effort.

In fact, it is not only because of these reasons that the technique of sampling is advocated, but also because there is a greater scope to maintain adequate degree of accuracy of results. In view of statistician’s use of census survey every time is the failure of statistics.

In these days the sampling theory has advanced much incorporating modern methods of sampling which can eliminate bias and chance error to make sample studies more meaningful and acceptable.

Sampling: Important Definitions and Comprehensive

Suitability of Sampling

Use of sample method proves to be successful only in the following circumstances

1. Where the scope of investigation is indefinite and unlimited for example, for determining the average weight of newly born baby.

2. Where the units are likely to be destroyed or consumed during the process of investigation, e.g., testing ghee, milk, coal etc.

3. Where the units under investigation have not much diversity.

4. Where the statistician or the investigator has full knowledge of the rules of sampling technique.

5. Where economy of money, time and other human resources is desired or the budget is limited.

Sampling: Important Definitions and Comprehensive

Read Also : Sample Survey

 Essentials of Sampling

In order to get accurate results of the investigation using sample method, it is essential that the sample selected from the universe has the following characteristics

1. Independence

 Every unit in the universe should have equal chances of being included in the sample, i.e., selection of any one unit should not depend on any other unit.

2. Representative

 For accuracy of sampling, it is necessary that the sample is a representative of the universe and it has all those attributes which are possessed by the universe. If the universe has varieties, units from all the various groups should be included in the sample. Moreover, sample should have been chosen at random.

3. Stability

 Various units selected in the sample should have uniformity and stability, so that the results obtained are reliable.

4. Adequacy

 Size of the sample should be adequate in proportion to the size of universe. Generally, larger the sample, more accurate it will be.

5. Free from bias

 Most important factor for the success of sampling method is that the sample should be free from bias. There should be no bias or partiality at any level in the selection of the method of sampling, in the working of investigators (enumerators), giving information by the informants, or in drawing inferences and interpretation of the result etc. Even then there is every possibility of some bias, but it should be within negligible limits.

Sampling: Important Definitions and Comprehensive

Principles of Sampling

There are two important principles of sampling which determine the possibility of arriving at a valid statistical inference about the features of a population process

(i) Principle of Statistical Regularity

(ii) Principle of inertia of large numbers.

(i) Principle of Statistical Regularity

This principle implies that a sample drawn at random from a population of interest is likely to possess all the features of the parent population. When the sample is selected by using simple random sampling method it can reduce the number of efforts required in arriving at a conclusion about the characteristics of a large population.

(ii) Principle of Inertia of Large Numbers

This principle plays a significant role in the sampling theory. This principle states that, under similar conditions, as the sample size increases enough, inference is likely to be more accurate and stable. For example, if an unbiased coin is tossed a large number of times, then relative frequency of occurrence of head and tail is expected to be equal.

Conclusion

Sampling is a pivotal concept in research, involving the selection of a subset from a larger population to study and make conclusions about the entire group. This process is essential for practicality and efficiency in data collection, allowing researchers to gather insights without studying every single element of the population.

Key terms such as population (the entire group of interest), sample (the subset chosen for study), sampling error (discrepancy between sample and population characteristics), bias (systematic errors), and representative sample (accurately reflecting population characteristics) are fundamental in understanding sampling’s importance. By employing representative samples and minimizing bias and sampling errors, researchers can enhance the validity and generalizability of their findings, ensuring the reliability and applicability of their research outcomes.