Types of Sampling Techniques MCQ Quiz - Objective Question with Answer for Types of Sampling Techniques - Download Free PDF

Sampling MCQs offer a straightforward way to understand the fundamentals of this key statistical method. They cover various aspects such as types of sampling, sampling error, and sample size determination. Regular practice with Sampling MCQs will consolidate your understanding of these principles. The beauty of Sampling MCQs is how they clarify complex topics, making them a must-use resource for anyone wanting to learn more about sampling.

Latest Types of Sampling Techniques MCQ Objective Questions

Types of Sampling Techniques Question 1:

If a research project is set up so that everybody in the population of interest has an equal chance of being included in the sample, the research involves :

1. quota sampling
2. judgemental sampling
3. probability sampling
4. convenience sampling

Answer (Detailed Solution Below)

Types of Sampling Techniques Question 1 Detailed Solution

The correct answer is 'probability sampling'

Key Points

• Probability Sampling:
  • Involves random selection, allowing each member of the population an equal chance of being included in the sample.
  • This method ensures that the sample is representative of the population, reducing sampling bias.
  • Types of probability sampling include simple random sampling, stratified sampling, cluster sampling, and systematic sampling.

  

  Additional Information

  • Quota Sampling:
    • Involves selecting a sample that reflects the characteristics of the entire population based on specific quotas.
    • Not random; the researcher decides the quotas and selects participants accordingly.
    • Also known as purposive sampling; the researcher uses their judgment to select participants who are most beneficial to the study.
    • Not random; relies on the researcher's expertise and knowledge.
    • Participants are selected based on their availability and willingness to take part.
    • Not random and highly susceptible to bias, as it doesn't represent the entire population.
    India’s #1 Learning Platform Start Complete Exam Preparation Daily Live MasterClasses Practice Question Bank Mock Tests & Quizzes Trusted by + Students

    Types of Sampling Techniques Question 2:

    Which of the following refers to a method of sampling in which a core group of participants who are initially sampled for a research study recruit or recommend other potential participants ?

    1. Simple random sampling
    2. Snowball sampling
    3. Quota sampling
    4. Stratified random sampling

    Answer (Detailed Solution Below)

    Option 2 : Snowball sampling

    Types of Sampling Techniques Question 2 Detailed Solution

    Snowball sampling refers to the method where a core group of participants recruit or recommend other potential participants for a research study.

    

    Key Points

    • Simple random sampling: This involves selecting participants entirely at random, ensuring everyone in the population has an equal chance of being chosen.
    • Snowball sampling: This method leverages existing social networks of initial participants to identify others with similar characteristics, making it ideal for studying hidden or hard-to-reach populations.
    • Quota sampling: This selects participants based on predetermined quotas for specific subgroups within the population, ensuring proportional representation in the sample.
    • Stratified random sampling: This divides the population into subgroups (strata) and then randomly selects participants from each subgroup, ensuring the sample reflects the overall population composition.

    Since snowball sampling relies on referrals from existing participants, it's the most relevant choice for the given description.

    India’s #1 Learning Platform Start Complete Exam Preparation Daily Live MasterClasses Practice Question Bank Mock Tests & Quizzes Trusted by + Students

    Types of Sampling Techniques Question 3:

    Identify the sampling issues that are problematic in online surveys: (A) Many people use more than one internet service provider. (B) Every person has only one (unique) email address. (C) A house hold may have one computer but several users. (D) Internet users are a biased sample of the population. Choose the correct answer from the options given below:

    1. (A), (B) and (C) Only
    2. (B), (C) and (D) Only
    3. (A), (B) and (D) Only
    4. (C) and (D) Only

    Answer (Detailed Solution Below)

    Option 4 : (C) and (D) Only

    Types of Sampling Techniques Question 3 Detailed Solution

    The correct answer to the question on identifying sampling issues that are problematic in online surveys is (C) and (D) only. This choice reflects the common concerns regarding the representative nature and potential biases in online survey methodologies.

    

    Key Points

    • Option (A), "Many people use more than one internet service provider," is not inherently a sampling issue for online surveys. While it might impact aspects related to the technical delivery or tracking of survey participation, it doesn't directly affect the representativeness or bias of the sample in the manner that options (C) and (D) do.
    • Option (B), "Every person has only one (unique) email address," is incorrect as stated and is not typically a sampling issue. In reality, many people have multiple email addresses, but the statement itself (as phrased in the query) doesn't represent a challenge to sampling methodologies in the context of this discussion.
    • Option (C), "A household may have one computer but several users," is recognized as a sampling issue. This situation can lead to underrepresentation or overrepresentation of individuals within a household if not properly accounted for, as different members may have varying access to the survey depending on household dynamics and computer usage patterns.
    • Option (D), "Internet users are a biased sample of the population," is a significant sampling concern. Online surveys inherently bias towards those with internet access, thereby potentially excluding segments of the population that are less likely to use or have access to the internet, such as older individuals or those in lower socioeconomic brackets.

    Therefore, the issues that accurately reflect sampling problems in online surveys are specifically tied to the access and representation biases (options C and D), whereas options A and B do not directly address core sampling challenges faced by these survey methodologies.

    India’s #1 Learning Platform Start Complete Exam Preparation Daily Live MasterClasses Practice Question Bank Mock Tests & Quizzes Trusted by + Students

    Types of Sampling Techniques Question 4:

    In sampling, the lottery method is used for
    1. Interpretation
    2. Theorisation
    3. Conceptualisation
    4. Randomisation

    Answer (Detailed Solution Below)

    Option 4 : Randomisation

    Types of Sampling Techniques Question 4 Detailed Solution

    The correct answer is Randomisation.

    

    Key Points From the provided options, the randomisation best describes the purpose of the lottery method in sampling.

    Here's why:

    Interpretation: This refers to assigning meaning to data or findings within a specific context. It's not directly related to the selection process of the sample.
    Theorisation: This involves developing theories or explanations based on observations or data. While understanding a sample might inform theory development, it's not the primary purpose of the lottery method.
    Conceptualisation: This refers to the process of developing abstract ideas or models. It's not directly involved in the selection process of the sample.
    Randomisation: This refers to the process of selecting individuals by chance, ensuring all members have an equal probability of being chosen. This aligns perfectly with the objective of the lottery method in obtaining a simple random sample.
    Therefore, among the given options, randomisation best captures the essence of the lottery method in the context of sampling.

    India’s #1 Learning Platform Start Complete Exam Preparation Daily Live MasterClasses Practice Question Bank Mock Tests & Quizzes Trusted by + Students

    Types of Sampling Techniques Question 5:

    Which of the following sampling methods involves dividing the population into subgroups or strata, then randomly selecting individuals from each stratum?

    1. Stratified Random Sampling
    2. Snowball Sampling
    3. Quota Sampling
    4. Convenience Sampling

    Answer (Detailed Solution Below)

    Option 1 : Stratified Random Sampling

    Types of Sampling Techniques Question 5 Detailed Solution

    The sampling method that involves dividing the population into subgroups or strata, then randomly selecting individuals from each stratum, is stratified random sampling

    

    Key Points

    • Stratified random sampling is a probability sampling method that allows researchers to select a sample that is representative of the population from which it is drawn. This is done by dividing the population into subgroups or strata based on a shared characteristic, such as age, gender, or income. Once the population has been stratified, the researcher randomly selects individuals from each stratum.
    • Stratified random sampling is a more complex sampling method than random sampling, but it has the advantage of producing a more representative sample. This is because stratified random sampling ensures that all subgroups of the population are represented in the sample.
    • Stratified random sampling is often used in research studies where it is important to have a representative sample, such as surveys and polls.
    • Here are some examples of stratified random sampling:
      • A researcher wants to study the opinions of adults on a new political candidate. They divide the population into age groups (e.g., 18-24, 25-34, 35-44, etc.) and randomly select individuals from each age group.
      • A researcher wants to study the effects of a new educational intervention on student achievement. They divide the population into schools and randomly select schools to participate in the study.
      • A researcher wants to study the attitudes of employees towards a new company policy. They divide the population into departments and randomly select departments to participate in the study.
      India’s #1 Learning Platform Start Complete Exam Preparation Daily Live MasterClasses Practice Question Bank Mock Tests & Quizzes Trusted by + Students

      Top Types of Sampling Techniques MCQ Objective Questions

      Types of Sampling Techniques Question 6

      From the list given below, identify those which are called ‘Non-Probability Sampling’ procedures: (i) Simple random sampling (ii) Dimensional sampling (iii) Snowball sampling (iv) Cluster sampling (v) Quota sampling (vi) Stratified sampling Choose the correct option:

      1. (i), (ii) and (iii)
      2. (ii), (iv) and (v)
      3. (i), (iii) and (iv)
      4. (ii), (iii) and (v)

      Answer (Detailed Solution Below)

      Option 4 : (ii), (iii) and (v)

      Types of Sampling Techniques Question 6 Detailed Solution

      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
         

      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 characteristics, 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.

      

      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.
      1. 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.).
      2. 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.