BSc Nursing 3rd Year Nursing Research And Statistics Chapter 10 Sampling Question And Answers

Sampling

Question 1. Advantages And Disadvantages Of Sampling.
Answer:

The advantages of sampling are as follows:

The economy in expenditure:

If the data are collected for the entire population, the cost will be very high. It is economical of cost when the data are collected from a sample that is only a fraction of the population i.e., sampling helps to reduce the cost of the research.

The economy in time:

• The use of sampling is economical for a time. Sampling is less time-consuming than the census technique.
• Tabulation, analysis, etc. also takes much less time in the case of a sample than in the case of a population. That means sampling helps greater speed in the project.

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Greater scope:

• In many fields of inquiry (For example, quality control tests) where the complete destruction of the product is involved, a cent percent test production is impossible, and often, impracticable, but they require highly trained personnel and sophisticated equipment.
• Sample simplifies things and personnel with little training can collect and handle data. There is a greater scope and flexibility of studies when a sample is used.

Greater accuracy:

• Sampling ensures completeness and a high degree of accuracy due to a limited area of operation.
• In dealing with a sample, the volume of work is reduced, therefore careful execution of fieldwork is possible.
• This processing of the data is also done more accurately, which in turn produces better results.

Organization of convenience:

• Sampling involves very few organizational problems. Due to small numbers, it does not require vast facilities. It is economical with respect to resources.
• The space and equipment required for this study are very small.

Intensive and exhaustive data:

• As the number is limited, it is possible to collect intensive and exhaustive data.

Suitable in limited resources:

• In every society, there are more problems and fewer resources, particularly when the people are poor and problems are uncountable. This is the method which enables the researcher to work even with limited resources.

Better rapport:

• It is very difficult to develop a rapport with a large number of people, but it is possible to develop a better rapport with the respondents/subjects.

Disadvantages of Sampling Chances of bias:

• Sampling may involve biased selection and thereby lead to drawing erroneous conclusions, which may be due to various reasons.

Difficulty in getting representative supply:

• Selection of a truly representative sample is very difficult particularly when the phenomena under study are of a complex nature.

Need for specialized knowledge:

• In the absence of specialized knowledge, the investigator may commit serious mistakes.
• So it requires specialized knowledge of sampling techniques, statistical analysis, and calculation of probable error.

The changeability of units:

• The cases of the sample may be widely dispersed since many refuse to cooperate some may be inaccessible and sometimes the selected cases may have to be replaced by others. All these introduce a change in the stipulated subjects to be studied.

Impossibility of sampling:

• Sometimes the universe is too small, or too heterogeneous, it is not possible to drive a representative sample. In such cases, supply is not required.

Question 2. Characteristics of a Good Sample
Answer:

1. A good sample is one that, within restrictions imposed by its size, will reproduce the characteristics of the population with the greatest possible accuracy.
2. It should be free from error due to bias or due to deliberate selection of the unit of the sample.
3. It should be free from random sampling errors.
4. There should not be any substitution of the originally selected unit for some other more convenient in any way.
5. It should not suffer from incomplete coverage of the units selected for the study i.e., it should not ignore the failure in the sample in responding to the study.
6. Relatively small samples properly selected may be much more reliable than large samples poorly selected.
7. But at the same time, it is essential that the sample is adequate in size so that it can become more reliable.
8. In the sample, only such units should be included, which as far as possible, are independent
9. While constructing a sample, it is important that measurable or known probability sample techniques are used. This will substantially reduce the likely discrepancies.

Question 3. Types of Sampling
Answer:

• There are two main sampling methods for quantitative research: probability and nonprobability sampling.

Probability sampling:

• A theory of probability is used to filter individuals from a population and create samples in probability sampling.
• Participants of a sample are chosen by random selection processes. Each member of the target audience has an equal opportunity to be selected in the sample.

There are four main types of probability sampling

1. Simple random sampling:

• As the name indicates, simple random sampling is nothing but a random selection of elements for a sample.
• This sampling technique is implemented where the target population is considerably large.

2. Stratified random sampling:

• In the stratified random sampling method, a large population is divided into groups (strata) and members of a sample are chosen randomly from these strata. The various segregated strata should ideally not overlap one another.

3. Cluster sampling:

• Cluster sampling is a probability sampling method using which the main segment is divided into clusters, usually using geographic and demographic segmentation parameters.

4. Systematic sampling:

• Systematic sampling is a technique where the starting point of the sample is chosen randomly and all the other elements are chosen using a fixed interval. This interval is calculated by dividing the population size by the target sample size.

Non-probability sampling:

• Non-probability sampling is where the researcher’s knowledge and experience are used to create samples.
• Because of the involvement of the researcher, not all the members of a target population have an equal probability of being selected to be a part of a sample.

There are five non-probability sampling models:

1. Convenience Sampling:
   • In convenience sampling, elements of a sample are chosen only due to one prime reason: their proximity to the researcher. These samples are quick and easy to implement as there is no other parameter of selection involved.
2. Consecutive Sampling:
   • Consecutive sampling is quite similar to convenience sampling, except for the fact that researchers can choose a single element or a group of samples and conduct research consecutively over a significant time period and then perform the same process with other samples.
3. Quota Sampling:
   • Using quata sampling researchers can select elements using their knowledge of target traits and personalities to form strata. Members of various strata can then be chosen to be a part of the sample as per the researcher’s understanding. Snowball Sampling
4. Snowball Sampling:
   • Snowball sampling is conducted with target audiences who are difficult to contact and get information. It is popular in cases where the target audience for research is rare to put together.
5. Judgmental Sampling:
   • Judgmental sampling is a non-probability sampling method where samples are created only on the basis of the researcher’s experience and skill.

Question 4. Explain the Simple random sampling
Answer:

• It is a probability sampling procedure in which the required number of sampling units are selected at random from the population in such a manner that each population element has an equal chance (probability) of being selected for the sample.
• Each choice of a sampling unit must be independent of all other choices. One of the most acceptable methods for selecting a simple random sample is to use a table of random numbers, which can be either computer-generated or found in a statistics textbook.
• The numbers in a random-number table have been generated in such a way that there is no pattern.
• The same probability exists that any digit will follow any other digit, and each selection is an independent choice.
• To obtain a simple random sample, first list each of the population elements, then assign consecutive numbers to each of these elements.
• Then, referring to the table of random numbers, arbitrarily start at any point in the table and proceed in any direction to identify enough tabled numbers to associate with the population elements until the desired sample has been selected.

Simple random sampling Merits:

1. This method requires minimum knowledge about the population in advance which is needed in the case of purposive sampling
2. The method is free from classification errors
3. Sampling errors can be easily computed and the accuracy of the estimate easily be assessed.

Simple random sampling Demerits:

1. This method does not make use of the knowledge about the population which the researcher may have.
2. The size of the sample required to ensure statistical reliability is usually large under stratified sampling.
3. From the point of view of field surveys, it has been claimed that cases selected by random sampling tend to be too widely dispersed geographically and that the time and cost of collecting data becomes too large.
4. The use of simple random sampling necessitates a completely cataloged universe from which to draw the sample.

However, it is often difficult for the investigator to have up-to-date lists of all the elements of the population to be sampled.

Question 5. Explain the Stratified Random Sampling
Answer:

• This is an improved type of random or probability sampling. In this method, the population is subdivided into homogeneous groups or strata, and from each stratum, a random sample is drawn.
• For example, university students may be divided on the basis of discipline, and each discipline group may again be divided into juniors and seniors; the employees of a business undertaking may be divided into managers and nonmanagers and each of those two groups may be sub-divided into salary-grade-wise strata.

Stratification is necessary for

1. Increasing a sample’s statistical efficiency,
2. Providing adequate data for analyzing the various sub-populations, and
3. Applying different methods to different strata.

• Stratification ensures the representation of all relevant sub-groups of the population. It is thus more efficient statistically than simple random sampling.
• Stratification is essential when the researcher wants to study the characteristics of population sub-groups, e.g., male and female employees of an organization.
• Stratification is also useful when different methods of data collection, etc. are used for different parts of the population e.g., interviewing for workers and self-administered questionnaires for executives.

Question 6. Explain Snowball Sampling
Answer:

• It is also known as nominated sampling, which is a non-probability sampling procedure Study subjects are asked to provide referrals to other study subjects.
• In this method of sampling, investigators identify individual respondents whom they believe to have pertinent information related to their study.
• They then ask these individuals to name (nominate) others who might be able to provide further information; these respondents, in turn, are then asked to name other potential respondents.
• This sampling technique is also termed network sampling or link-tracing sampling.
• This is the colorful name for a technique of building up a list or a sample of a special population by using an initial set of its members as informants.
• For example, if a researcher wants to study the problem faced by Indians through some source like the Indian Embassy.
• Then he can ask each one of them to supply names of other Indians known to them and continue this procedure until he gets an exhaustive list from which he can draw a sample or make a census survey.
• This sampling technique may also be used in socio-metric studies. For example, the members of a social group may be asked to name the persons with whom they have social contacts, each one of the persons so named may also be asked to do so, and so on.
• The researcher may thus get a constellation of associates and analyze it.

Snowball Sampling Advantages:

• It is very useful in studying social groups, informal groups in a formal organization, and the diffusion of information among professionals of various kinds
• It is useful for smaller populations for which no frames are readily available.

Snowball Sampling Disadvantages:

• It does not allow the use of probability statistical methods. The elements included are dependent on the subjective choice of the originally selected respondents.
• It is difficult to apply this method when the population is large.
• It does not ensure the inclusion of all elements in the list.

Question 7. Define Sampling and Explain probability sampling ratified random sampling techniques.
Answer: 

Sampling Definition:

• Sampling is a process of selecting representative units from an entire population.

Probability Sampling Introduction:

• It is one technique of sampling, it is based on the theory of probability.
• It involves random selection of the elements from the population.

Techniques of probability sampling:

1. Simple random sampling
2. Stratified random sampling
3. Systemic random sampling
4. Cluster/multistage sampling
5. Sequential sampling

1. Simple Random Sampling:

In this, every member of the population has an equal chance of getting selected. In this, the sampling used a random sampling technique. Due to the random selection, there is an absence of systemic bias. Random selection either by a lottery, random table, or computer.

Simple Random Sampling Advantages:

• The most reliable and unbiased method
• Requires minimum knowledge of the study population
• Free from sampling errors

Simple Random Sampling Disadvantages:

• Need to date complete list of all members of the population
• Expensive and time-consuming

2. Stratified Random Sampling:

Dividing heterogeneous populations into strata based on selected traits such as age, gender, and habitat, and then random selection of samples from each stratum.

Stratified Random Sampling Advantages:

• Ensures a representative sample in a heterogeneous population.
• Comparison is possible in two groups.

Stratified Random Sampling Disadvantages:

• Requires complete information on the population
• A large population is required
• Chances of faulty classification of strata

Systematic Random Sampling:

Selecting every Kth case from the group, such as every 10th person on a patient list or 100th person

Systematic Random Sampling Advantages:

• Convenient and simple to carry out
• Distribution sample over the entire population

Systematic Random Sampling Disadvantages:

• Less representative sample if subjects are non-randomly distributed
• Sometimes may result in a biased sample

4. Cluster Or Multistage Sampling:

In very large populations random selection of geographic cluster and then random selection of subjects from the cluster. When the population is very large such as “In Asia” random selection of geographic cluster

Cluster Or Multistage Sampling Advantages:

• Cheap, quick, and easy for a large population
• Population parameters of population can be estimated for sample size

Cluster Or Multistage Sampling Disadvantages:

• Possibility of high sampling error
• Chances of least presentative sample due to over-represented or under-represented cluster

5. Sequential Sampling:

• The investigator initially selects a small sample and tries to make inferences;if not able to draw results, he/she then adds subjects until clear-cut inferences can be drawn. Sample size is not fixed continue till inferences are drawn

Sequential Sampling Advantages:

Study on the best possible smallest sample Facilitates inferences of study

Sequential Sampling Disadvantages:

Not possible to study a phenomenon that needs to be studied to one point in time Requires is repeated entry into the field to collect the sample