Sample MCQ Quiz - Objective Question with Answer for Sample - Download Free PDF

The correct answer is - (D), (A), (B), (C)

Key Points

• Define the universe to be studied
  • The first step is to clearly identify and define the population or universe that is the focus of the study.
  • It involves specifying the characteristics of the group being studied.
• Preparing sampling frame
  • A sampling frame is a list of elements from which the sample will be drawn.
  • This step ensures that every element of the population has a chance of being included in the sample.
• Applying the sampling technique
  • In this step, the researcher selects a sample from the sampling frame using a specific sampling technique.
  • Common techniques include random sampling, stratified sampling, and cluster sampling.
• Administer the tool
  • Finally, the researcher uses the selected sample to administer the data collection tool, such as surveys or interviews.
  • This step involves gathering the necessary data from the chosen sample.

Additional Information

• Importance of Sampling
  • Sampling allows researchers to draw conclusions about a population without having to survey everyone.
  • It is cost-effective and less time-consuming than studying the entire population.
• Types of Sampling Techniques
  • Probability Sampling
    • Each member of the population has a known, non-zero chance of being selected.
    • Examples include simple random sampling, stratified sampling, and cluster sampling.
  • Non-Probability Sampling
    • Members are selected based on non-random criteria, and not all members have a chance of being included.
    • Examples include convenience sampling, judgmental sampling, and quota sampling.
• Sampling Frame Errors
  • Occurs when the sampling frame does not perfectly match the population.
  • This can result in some elements of the population being excluded or overrepresented.

The correct answer is 'Less'

Key Points

• Understanding Sampling Error in Homogeneous Populations:
  • Sampling error refers to the difference between the sample results and the actual population characteristics.
  • In a homogeneous population, the individuals are very similar or identical in terms of the characteristics being measured.
  • When the population is homogeneous, the variability among the sample units is minimal.
  • This low variability leads to smaller differences between the sample and the population, resulting in a reduced sampling error.

Additional Information

• Explanation of Incorrect Options:
  • High: If the population is homogeneous, the sampling error is not high because the sample units are similar, reducing variability.
  • Moderate: In a homogeneous population, the sampling error is typically less, not moderate, due to the low variability among the sample units.
  • Cannot say definitely: This option is incorrect because we can definitively say that the sampling error will be less in a homogeneous population based on the principles of statistical sampling.

The correct answer is 'Obtaining representative sample'

Key Points

• Obtaining representative sample:
  • A representative sample accurately reflects the characteristics of the population from which it is drawn.
  • This reduces sampling error because the sample provides a true picture of the population, thereby improving the reliability and validity of the results.
  • By ensuring that all subgroups within a population are proportionally represented, biases are minimized, and the generalizability of the findings is enhanced.

Additional Information

• Decreasing the sample size:
  • Decreasing the sample size actually increases sampling error because smaller samples are less likely to capture the diversity of the population.
  • This leads to less reliable and less accurate results.
• Homogeneous grouping of individuals:
  • While homogeneous grouping can reduce variability within the groups, it does not necessarily reduce sampling error for the entire population.
  • This approach may lead to over-generalization and may not reflect the true diversity of the population.
• Possibility of reduction of the sample size:
  • The potential to reduce sample size does not directly impact sampling error, unless it is tied to an actual decrease in sample size, which would increase error.
  • The emphasis should be on obtaining a sufficiently large and representative sample to minimize error.

The correct answer is 'Stratified Sampling'

Key Points

• Stratified Sampling:
  • Stratified sampling is a method of sampling that involves dividing a population into subgroups or strata that share similar characteristics.
  • The purpose is to ensure that each subgroup is adequately represented within the overall sample, thereby making the sample more representative of the entire population.
  • This method improves the accuracy and reliability of the results by reducing sampling bias and ensuring all sections of the population are proportionately represented.
  • For example, if a population consists of 60% women and 40% men, stratified sampling would ensure that the sample also reflects this proportion.

Additional Information

• Quota Sampling:
  • Quota sampling is a non-probability sampling technique wherein researchers divide the population into exclusive subgroups and then arbitrarily choose participants from each subgroup.
  • Unlike stratified sampling, quota sampling does not use random selection, which may introduce bias and limit the representativeness of the sample.
• Systematic Sampling:
  • Systematic sampling involves selecting elements from an ordered population using a fixed interval, such as every 10th person on a list.
  • While it is a probability sampling method, it does not ensure proportional representation of all sections of a population.
• Snow-ball Sampling:
  • Snow-ball sampling is a non-probability sampling technique often used in studies with hard-to-reach populations.
  • Participants are recruited through referrals from initial subjects, which can lead to sampling bias and does not guarantee proportional representation of the population.

The correct answer is 'Disproportional stratified sampling'

Key Points

• Disproportional stratified sampling:
  • In disproportional stratified sampling, the sample proportions are intentionally made different from the population proportions.
  • This type of sampling is used when researchers want to ensure that smaller subgroups are adequately represented in the sample, especially when those subgroups are too small to be represented proportionally.
  • By adjusting the sample size for each stratum, researchers can ensure more reliable and valid results for subgroups of interest.

Additional Information

• Random sampling:
  • In random sampling, each member of the population has an equal chance of being selected.
  • It does not account for different proportions of subgroups within the population.
• Stratified sampling:
  • This method involves dividing the population into distinct subgroups (strata) and then randomly sampling from each stratum.
  • Stratified sampling aims to ensure that each subgroup is adequately represented, typically in proportion to its presence in the population.
• Proportional stratified sampling:
  • In proportional stratified sampling, the sample size from each stratum is proportional to the size of the stratum in the population.
  • This method ensures that the sample accurately reflects the population proportions.

Sampling: The concept of sampling involves selecting a portion (sample) from a bigger group (the sampling population). There are three methods of sampling in research:

1. Random/Probability Sampling
2. Non-random/Non-probability Sampling
3. ‘Mixed’ Sampling

Random/Probability Sampling: In this type, each element in the population has an equal and independent chance of selection in the sample.

1. Simple Random Sampling: It is the most popular of the probability sampling methods. The idea of randomization implies that sample selection is independent of human judgment.

2. Stratified Random Sampling: It combines randomization with stratification. Here, the population is divided into strata, the population within each stratum is homogeneous with respect to the characteristic based on which it is being stratified and such characteristics must be identifiable in the study population (e.g. age, income, sex, etc.).
3. Cluster Sampling: It is based on the ability of the researcher to divide the sampling population into groups, called clusters and then to select elements within each cluster, using the simple random sampling technique. It is appropriate when the population is large.
4. Systematic sampling - It is also called sequence random sampling. In which the first sample will be random and based on the first selection of sample rest are selected. In this method order of all the units in the sampling frame are based on some variable and every unit on the list is selected. Gaps between elements or units are equal and constant which means there is periodicity order.

​

Types of Non-Random/Non-Probability Sampling Designs: These designs do not operate on the principle of randomization rather these are used when the number of elements in the population is either unknown or cannot be individually identified.

1. Accidental Sampling: It is also based upon convenience in accessing the sampling population. People who are unwilling to provide data are simply ignored and the researcher moves to the next person until he/she meets somebody who is willing to be a participant.  You stop collecting data when you reach the required number of respondents you decided to have in your sample.
2. Judgment/Purposive Sampling: The primary consideration in purposive sampling is the researcher’s judgment as to who can provide the best information to achieve the objectives of your study. The researcher will only go to those people who in his opinion is likely to have the required information and will be willing to share it.
3. Snowball Sampling: It is also called network or chain referral sampling. To start with, the researcher identifies a small number of respondents having a set of characteristics of interest to the researcher. After collecting the required data from those respondents, the same respondents are asked to identify others having the same characteristics set. E.g., collecting data from drug addicts, rape victims, etc.
4. Quota Sampling: The researcher is guided by some visible characteristic, such as gender or race, of the study population that is of interest to him. The sample is selected from a location that is convenient and easily accessible to the researcher and whenever a person with this visible relevant characteristic is seen that person is asked to participate in the study.
5. Dimensional Sampling: It is an extension of quota sampling where the researcher takes into account several characteristics such as gender, residence, education, etc. and ensures that there is at least one individual in the study representing each of the chosen characteristics.

Sampling in research is the process of selection of units (e.g. people, organization) from a population of interest so that by studying the sample may fairly generate results back to the population from which they were chosen. The objective of sampling is to derive the desired information about the population at the minimum cost or with the maximum reliability.

Blalock (1960) indicated that most sampling methods could be classified into two categories: Non-probability sampling methods and Probability sampling methods.

Simple Random sampling

• The random sample entails that each and every individual in a population has an equal chance of being included in the sample and that the selection of one individual is in no way dependent upon the selection of another person.
• The two popularly used methods in random sampling are
  1. Draw of lottery
  2. Using a random number table

Quota Sample

• In quota sampling, the population is categorized into several strata which consist of the expected size, and the samples are considered to be important for the population they represent.
• The advantages of quota samples are that it involves a short time duration, is less costly, and gives moderate representation to a heterogeneous population

Purposive Sampling

• In this method, samples are expressly chosen because in the light of available information they resemble some larger group with respect to one or more characteristics.
• The controls criteria for categorization in such samples are usually identified as representative areas such as a state, a district, a city, etc or representative characteristics of individuals such as age, sex, socio-economic status, etc. or representative types of groups such as elementary school teachers, secondary school teachers, college teachers, university teachers, etc.
• These controls criteria may be further subdivided e.g. the group of college teachers can be divided into male and female teachers or teachers in science/arts/commerce colleges etc.
• For example, An investigator wants to conduct a study on politically active student-leaders in educational institutions.

Snowball Sample

• This is one of the important types of non-probability sampling.
• In snowball sampling, the investigator encourages the respondents to give the names of other acquaintances and it continues growing in size and chains until the research purpose is achieved.
• It is also, therefore, known as networking, chain, or referred sampling method. It is very useful in the study of networking and is less costly.

Research is a process of systematic inquiry. The process includes a collection of data; documentation of critical information; and analysis and interpretation of that data/information, in accordance with suitable methodologies set by specific professional fields and academic disciplines.​​

Important Points

Sampling in research is the process of selection of units (e.g. people, organization) from a population of interest so that studying the sample may fairly generate results back to the population from which they were chosen. The objective of sampling is to derive the desired information about the population at the minimum cost or with the maximum reliability.

Blalock (1960) indicated that most sampling methods could be classified into two categories: Non-probability sampling methods and Probability sampling methods.

Simple random sampling

•  A simple random sample is a subset of individuals chosen from a larger set.
• Each individual is chosen randomly and entirely by chance, such that each individual has the same probability 

​Stratified random sampling

• Stratified sampling is a method of sampling from a population that can be partitioned into subpopulations.
• When subpopulations within an overall population vary, it could be advantageous to sample each subpopulation independently.

​Systematic sampling

• Systematic sampling is the selection of Participants from an ordered sampling frame.
• The most common form of systematic sampling is an equiprobability method.

Key Points

Cluster sampling

• Cluster sampling is a sampling used when in a statistical population there are mutually homogeneous yet internally heterogeneous groups.
• In this sampling plan, the total population is divided into these groups and a simple random sample of the groups is selected.
• The researcher divides the population into separate groups, called clusters. 
• So, in the question student is a homogenous group, and a slum dweller student forms an internal heterogeneous group.

​Therefore, study the adjustment problems of students who slum dwellers cluster sampling is most appropriate.

A “sample” is a miniature representation of and selected from a larger group or aggregate. The sample provides a specimen picture of a larger whole. This larger whole is termed as the “population” or “universe”. In research, this term is used in a broader sense; it is a well-defined group that may consist of individuals, objects, characteristics of human beings, or even the behavior of inanimate objects, such as, the throw of a dice or the tossing of a coin.

Sampling is the selection of representative of a small group (sample) from the population. The method used for drawing a sample is significant
to arrive at dependable results or conclusions. The various sampling methods can be broadly classified into two categories: Probability Sampling and Non-probability Sampling.

Simple or unrestricted random sampling:

• Simple random sampling is a method of selecting a sample from a finite population in such a way that every unit of the population is given an equal chance of being selected.
• In practice, you can draw a simple random sample unit by unit through the following steps:
  1. Define the population
  2. Decide the size of the sample or the number of units to be included in the sample.
  3. Make a list of all the units in the population and number them from 1 to n.
  4. Use either the ‘lottery method’ or ‘random number tables’ to pick the units to be included in the sample.
• For example,
  1. ​​you may use the lottery method to draw a random sample by using a set of ‘n’ tickets, with numbers ‘1 to n’ if there are ‘n’ units in the population.
  2. After shuffling the tickets thoroughly, the sample of required size, say x, is selected by picking the required x number of tickets.
  3. The units which have the serial numbers occurring on these tickets will be considered selected.
  4. The assumption underlying this method is that the tickets are shuffled so that the population can be regarded as arranged randomly. 

Hence, Define the target population, decide sample size, list all the units of the target population, and drawing the sample by randomization is the process of drawing samples in a simple random sampling.

Sampling:

• Sampling is the selection of a subset (a statistical sample) of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt for the samples to represent the population in question.

There are two types of sampling

Quantitative Research:

• It is used to quantify the problem by way of generating numerical data or data that can be transformed into usable statistics.
• It is used to quantify attitudes, opinions, behaviours, and other defined variables – and generalize results from a larger sample population.
• Quantitative Research uses measurable data to formulate facts and uncover patterns in research.
• Quantitative data collection methods are much more structured than Qualitative data collection methods.
• Quantitative data collection methods include various forms of surveys – online surveys, paper surveys, mobile surveys and kiosk surveys, face-to-face interviews, telephone interviews, longitudinal studies, website interceptors, online polls, and systematic observations.

• The probability method is the best representative in quantitative research. So, the correct answer is A, B, and E only.

Sampling	Description	Process
Simple random sampling	In a simple random sample, every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.
Stratified sampling	This sampling method is appropriate when the population has mixed characteristics, and you want to ensure that every characteristic is proportionally represented in the sample. You divide the population into subgroups (called strata) based on the relevant characteristic (e.g. gender, age range, income bracket, job role).
Quota sampling	In this method, the population is first segmented into mutually exclusive sub-groups, just as in stratified sampling. Dimension sampling is a part of it.
Snowball sampling	If the population is hard to access, snowball sampling can be used to recruit participants via other participants. The number of people you have access to “snowballs” as you get in contact with more people.
Systematic sampling	This method is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.

The correct answer is (A), (B), (D), (E) only

Key Points Non Sampling Errors:

• All sources of errors that are unrelated to sampling are referred to as non-sampling error. All survey types, including censuses and administrative data, can have non-sampling mistakes.
• They can occur for a variety of reasons, including an inadequate data frame, inaccurate data reporting by certain respondents, missing data for some respondents, etc.

Non-sampling errors can be classified into two groups:

Random Error: Random errors are errors whose effects approximately cancel out if a large enough sample is used, leading to increased variability.

Systematic errors are errors that tend to go in the same direction, and thus accumulate over the entire sample leading to a bias in the final results.

Important Points Non-sampling error can occur in different forms. Non-sampling errors can occur due to the following reasons -

• Ambiguous definitions used in data collection.
• Wrong method of data collection
•  Wrong entry made in the question list
• To avoid census study, only part of the population is observed, and is expected to be observed.

Hence, option (d) is correct.

Sampling Error

• A sampling error is a statistical error that occurs when an analyst does not select a sample that represents the entire population of data and the results found in the sample do not represent the results that would be obtained from the entire population.
• Two major types of errors in research are sampling error and non-sampling error. Sampling error occurs when the sample used in the study is not representative of the whole population. Non-sampling error encompasses all types of errors, mostly caused by human error, such as questionnaire wording, data entry errors, and biased decisions.

Ways to minimize sample error

1. Increase the sample size by selecting more subjects to observe.
2. Divide the population into the group.

Ways to minimize non-sampling error

1. Avoid rushed or short data collection period.
2. Send reminders to respondents
3. Ensure confidentiality.

Conclusion: from the definition, it is clear that sample error arises when such a sample is selected that doesn’t represent the entire population. So, option (1) is correct.

Key Points

Statement I : In case of qualitative phenomena, we have data on the basis of presence or absence of an attribute or more than one attribute.

• Statement I is true because qualitative phenomena are those that can be described in terms of quality, rather than quantity.
• For example, whether or not a person has a certain disease is a qualitative phenomenon. In such cases, we can only have data on the presence or absence of the attribute.

Statement II : With such data as mentioned in Statement I above the sampling distribution may take the form of a Binomial probability distribution.

• Statement II is true because the sampling distribution of a Binomial probability distribution is a discrete probability distribution that describes the probability of obtaining a certain number of successes in a fixed number of trials, where each trial has only two possible outcomes, success or failure.
• This is the case for qualitative phenomena, where each trial is the observation of a single individual and the two possible outcomes are "has the attribute" and "does not have the attribute."

Therefore, the correct answer is (1). Both Statement I and Statement II are true.

• A. Quota sampling - 
  •    It is a non-probability sampling technique, where the researcher selects individuals from different subgroups of the population based on pre-determined quotas.
  • To ensure that the sampling represents the diversity of the population, quotas are set on the basis of certain characteristics such as age, gender, occupation, etc.
  • However, the selection of individuals within each subgroup is non-random, making it a non-probability sampling method.
• B. Snowball sampling -
  • Snowball sampling is a non-probability sampling technique used when the target population is difficult to reach or identify.
  • In this method, the researcher starts with a small group of individuals who meet the criteria for the study, called the core.
  • These initial participants then refer or nominate other individuals who meet the criteria and the process continues like a snowball effect.
    This method is often used when studying hidden populations or sensitive issues.
• C. Cluster sampling -
  • Cluster sampling is a probability sampling technique where the researcher divides the population into groups or subgroups based on geographic or other criteria.
  • Then, a random sampling of groups is chosen and all individuals from the chosen groups are included in the study.
  • Cluster sampling is used when the population is spread over a wide geographical area and it helps in reducing the cost and effort required to collect data.

• D. Systematic sampling -
  • Systematic sampling is a probability sampling technique, where the researcher selects every nth person from a list of the population.
  • The starting point is chosen randomly and then every ninth person is included in the sample until the desired sample size is reached.
  • This method ensures equal probability of selection for each individual in the population, making it a simple and efficient probability sampling method.

Hence, the correct match is 'A – IV ( Quota Sampling), B – III (Snowball Sampling), C – I (Cluster Sampling), D – II (Systematic Sampling)'.

The correct answer is 88

Key Points Systematic Random Sampling:

• Systematic sampling is a form of probability sampling technique in which sample members are chosen from a wider population using a defined, periodic interval but a random beginning point.
• It is calculated by dividing the population size by the required sample size, this interval, which is known as the sampling interval.

Important Points Target Size (N) = 600

 Sample Size (n) = 30

 Sample Interval = N/n = 600/30 = 20

 First name selected = 8

 Next name selected = 8+20 = 28 and so on

So selected names will be 

8, 28, 48 ............ 348, 368.....408, 428.....528,548, 568

 Hence, 558 will not be selected.