Sampling Techniques in Statistics
30 Questions
0 Views

Choose a study mode

Play Quiz
Study Flashcards
Spaced Repetition
Chat to Lesson

Podcast

Play an AI-generated podcast conversation about this lesson

Questions and Answers

Which of the following types of probability samples involves selecting participants at regular intervals from a sorted list?

  • Systematic random sample (correct)
  • Simple random sample
  • Stratified random sample
  • Cluster sample

What is a characteristic of non-probability sampling?

  • Every subject has an equal chance of selection.
  • Samples are chosen based on the researcher’s judgment. (correct)
  • Samples are selected randomly.
  • Samples can be statistically representative of a population.

What type of probability sample divides the population into subgroups and then randomly selects samples from each subgroup?

  • Cluster sample
  • Simple random sample
  • Multi-stage random sample
  • Stratified random sample (correct)

Which sampling method uses a two or more stage process to select samples?

<p>Multi-stage random sample (C)</p> Signup and view all the answers

Which sampling method is NOT considered a non-probability sampling technique?

<p>Simple random sampling (C)</p> Signup and view all the answers

Which of the following statements correctly defines purposive sampling?

<p>It involves selecting participants based on specific criteria defined by the researcher. (A)</p> Signup and view all the answers

In which type of probability sampling are entire groups or clusters randomly selected, and then all or some individuals from those groups are studied?

<p>Cluster sample (B)</p> Signup and view all the answers

In which scenario would non-probability sampling be the preferred choice?

<p>When time and resource constraints limit the ability to conduct random sampling. (B)</p> Signup and view all the answers

Which sampling method ensures that every individual in the population has an equal chance of being selected?

<p>Simple random sample (B)</p> Signup and view all the answers

What is a potential drawback of using non-probability sampling methods?

<p>They can lead to biased results due to lack of randomization. (A)</p> Signup and view all the answers

What is one of the primary conditions for creating strata in a population?

<p>Each stratum should be homogeneous as possible. (D)</p> Signup and view all the answers

What does stratification help ensure regarding minority subgroups?

<p>Adequate representation of minority subgroups of interest. (C)</p> Signup and view all the answers

Which of the following is NOT a goal of stratification?

<p>To create heterogeneous groups of individuals. (D)</p> Signup and view all the answers

In stratification, what is the significance of having homogeneous strata?

<p>It enhances the precision of comparisons made during analysis. (B)</p> Signup and view all the answers

How can stratification be implemented effectively?

<p>By identifying relevant criteria to form distinct strata. (D)</p> Signup and view all the answers

What is an advantage of using stratified sampling?

<p>Each stratum is guaranteed to be represented in the final sample. (A)</p> Signup and view all the answers

Why is it beneficial to use the same sampling fraction for all strata in stratified sampling?

<p>It ensures that every stratum is accurately represented in proportion to its size. (B)</p> Signup and view all the answers

What is one limitation of stratified sampling compared to other methods?

<p>It requires more complex data collection techniques. (D)</p> Signup and view all the answers

How does stratified sampling enhance the quality of the research findings?

<p>By increasing the representativeness of the sample. (D)</p> Signup and view all the answers

Which statement is not true about the advantages of stratified sampling?

<p>It guarantees that every unit has an equal chance of being selected. (A)</p> Signup and view all the answers

What must be prepared separately for each stratum when creating a sampling frame?

<p>The sampling frame of the entire population (A)</p> Signup and view all the answers

What issue arises when examining multiple criteria in stratified designs?

<p>Stratifying variables may only relate to some criteria (D)</p> Signup and view all the answers

How can the design of stratified sampling potentially be hindered?

<p>By examining criteria that are unrelated to stratifying variables (C)</p> Signup and view all the answers

Why can the utility of created strata be reduced?

<p>If stratifying variables are improperly related to certain criteria (A)</p> Signup and view all the answers

What is a potential consequence of having poorly defined stratifying variables?

<p>Limited insight into stratification benefits (B)</p> Signup and view all the answers

What defines a cluster sample in the context of multistage random sampling?

<p>All members from a selected subset of the population are included. (B)</p> Signup and view all the answers

Which situation best exemplifies a cluster sampling scenario?

<p>Selecting an entire school district and surveying all students in that district. (A)</p> Signup and view all the answers

In cluster sampling, what is meant by 'en toto'?

<p>Including all individuals from the selected cluster. (B)</p> Signup and view all the answers

Which of the following is NOT a characteristic of cluster sampling?

<p>It encompasses individuals from a single demographic group only. (C)</p> Signup and view all the answers

What is a potential benefit of using cluster sampling in research?

<p>It simplifies the sampling process by reducing logistical complexities. (B)</p> Signup and view all the answers

Flashcards

Purposive Sampling

A sampling method where individuals are chosen based on the researcher's specific criteria, not randomly. This allows for targeted insights but might not be representative of the overall population.

Random Sampling

A sampling method where every member of the population has an equal chance of being selected. This aims to create a representative sample and ensure unbiased results.

Non-Probability Sampling

Sampling methods where the selection of participants isn't based on probability or randomness. Examples include convenience sampling, snowball sampling, and quota sampling.

Convenience Sampling

A type of non-probability sampling where participants are chosen based on their easy accessibility and availability to the researcher.

Signup and view all the flashcards

Quota Sampling

A type of non-probability sampling where participants are selected based on pre-determined quotas or characteristics, ensuring representation of various subgroups.

Signup and view all the flashcards

Simple Random Sample

Every member of the population has an equal chance of being selected. The selection process is completely random, like drawing names from a hat.

Signup and view all the flashcards

Systematic Random Sample

A systematic approach where you select every kth member of the population (e.g., every 5th person). The starting point is chosen randomly to ensure fairness.

Signup and view all the flashcards

Stratified Random Sample

The population is divided into subgroups based on shared characteristics (e.g., age, gender). A simple random sample is then drawn from each subgroup, ensuring representation of all groups.

Signup and view all the flashcards

Multi-Stage Random Sample

A multi-level process where you first sample large units (e.g., cities), then smaller units within those (e.g., neighborhoods), and so on until you reach individuals.

Signup and view all the flashcards

Cluster Sample

The population is divided into groups (clusters) based on natural groupings (e.g., schools, classrooms). A random sample of these clusters is selected, and then all individuals within the chosen clusters are included.

Signup and view all the flashcards

Equal Chance Selection

Each element within a specific group has an equal probability of being selected for the sample.

Signup and view all the flashcards

Proportionate Representation

Ensuring that the proportion of each group in the sample matches the proportion of that group in the overall population.

Signup and view all the flashcards

Stratified Sampling

A sampling technique where the population is divided into groups (strata) based on shared characteristics, and a sample is drawn proportionally from each stratum.

Signup and view all the flashcards

Consistent Sampling Fraction

Using the same sampling fraction for all strata in stratified sampling, ensuring that each group contributes proportionally to the sample.

Signup and view all the flashcards

Accurate Subgroup Representation

A benefit of stratified sampling where each population subgroup is accurately represented in the sample, providing trustworthy insights about the overall population.

Signup and view all the flashcards

Stratification in Sampling

Dividing the population into smaller, similar groups based on characteristics like age, gender, or ethnicity.

Signup and view all the flashcards

Homogeneous Strata

Each group within the population should have similar characteristics to ensure a representative sample.

Signup and view all the flashcards

Adequate Representation of Subgroups

The goal is to represent all subgroups proportionally, ensuring a balanced and accurate picture of the population.

Signup and view all the flashcards

Ensuring Representation of Minority Subgroups

Stratification helps ensure that subgroups of interest are accurately represented in the sample.

Signup and view all the flashcards

Stratification for Representative Sampling

Stratification is a technique for creating a representative sample by dividing the population into homogeneous strata.

Signup and view all the flashcards

Stratified Sampling: Individual Frames

Each stratum requires its own unique sampling frame. This creates a greater administrative burden for the survey.

Signup and view all the flashcards

Stratification and Multiple Criteria

When studying multiple factors, some might be related to the chosen stratification, while others may not. This can make the design less efficient and potentially reduce the usefulness of the strata.

Signup and view all the flashcards

Cluster Sampling

A sampling method where the entire population within selected clusters is included in the sample. Clusters are naturally occurring groups (e.g., schools, neighborhoods).

Signup and view all the flashcards

Representative Cluster Sample

A cluster sample where all demographic groups (age, gender, socioeconomic status) are proportionally represented within the selected clusters.

Signup and view all the flashcards

Random Selection at Each Stage

Each stage of a multi-stage sampling process involves selecting units (groups or individuals) randomly from a larger pool.

Signup and view all the flashcards

Study Notes

Sampling Techniques

  • A sample is a part of a population, selected to represent the population's variables.
  • Sampling is essential in research as comprehensive studies are often impractical.

Advantages of Sampling

  • Lower cost than comprehensive surveys
  • Faster data collection
  • Potentially richer information
  • Necessary when a comprehensive survey is impossible (e.g., studying fish, animals, or nomadic populations, quality control of industrial products, examining patient blood)

Types of Sampling

  • I. Non-Probability (Non-Random) Samples
    • a. Purposive samples: Chosen based on judgment; results cannot be generalized.
    • b. Pre-test or pilot study: Used to test studies, identifying crucial parameters and eliminating unnecessary variables.
    • c. Quota sample: Used (e.g., in U.S.A. by Gallup Institute prior to voting) to gather information from a specific number of individuals in different groups; not used in community medicine or clinical practice.
    • d. Convenience sample: Made up of people easy to reach, a common type of non-probability sampling.
  • II. Probability (Random) Samples
    • Characteristics: Selection probability is determined; standard error calculated; results can be generalized.
    • Types:
      • Simple random sample
      • Systematic random sample
      • Stratified random sample
      • Multistage random sample
      • Cluster sample

Simple Random Sample

  • Methods:

    • Selection from a frame of equal-sized papers labeled with serial numbers from the population. Papers are mixed, and desired number are chosen.
    • Coin tossing
    • Selecting random balls from a container.
    • Generating random numbers via computer

  • Advantages:

    • Basic type of probability sampling
    • Every population member has an equal chance of selection

  • Disadvantages:

    • Frame construction can be difficult for large populations.
    • Sample members may be concentrated in one population segment (e.g., only females).
    • Not suitable for populations with high variability.

Systematic Random Sample

  • Methods: Selects every 'kth' member from the population list, starting from a randomly selected point (e.g., if k = 10 and the random starting point is 4, the sequence is 4, 14, 24, 34...).

  • Advantages:

    • Easy to select
    • Well-distributed throughout the population.
    • More precise than simple random sampling.

  • Disadvantages:

    • Difficulty is constructing the frame for large populations.
    • Possible unequal selection probabilities if population listing has a periodic pattern.

Stratified Random Sample

  • Characteristics: The population is divided into multiple homogenous strata (e.g., based on education level, occupation, socioeconomic status). A random sample is selected from each stratum in proportion to its size in the population.

  • Advantages:

    • Each unit has an equal chance of selection
    • Proportional representation in the sample.

  • Disadvantages:

    • Requires a sampling frame for each stratum.
    • Complicated stratification of multiple variables.

Multistage Random Sample

  • Methodology: Sampling of units occurs at multiple levels (e.g., selecting governorates, districts, talukas, and then villages as the final units to interview).

  • Advantages:

    • Easy to construct the frame.
    • Efficient for very large populations.
    • Cost-effective (travel and administrative costs).

  • Disadvantages:

    • May not be as precise as simpler sampling methods if the sample size is the same.

Cluster Sampling

  • Characteristics: Population divided into clusters, then a random sample of clusters is selected. All individuals within the selected clusters are sampled.

  • Advantages:

    • Cost savings in preparing sampling frames.
    • Reduces travel and administrative costs.

  • Disadvantages:

    • Higher sampling errors compared to other random sampling methods.

Studying That Suits You

Use AI to generate personalized quizzes and flashcards to suit your learning preferences.

Quiz Team

Related Documents

Sampling Techniques Lecture Notes PDF

Description

Test your knowledge of various sampling methods in statistics with this quiz. Delve into probability and non-probability sampling techniques, their characteristics, and when each method is best applied. Perfect for students studying statistics or research methodologies.

More Like This

Non-Probability Sampling Techniques
43 questions

Non-Probability Sampling Techniques

IlluminatingRomanesque avatar
IlluminatingRomanesque
Types of Non-Probability Sampling
10 questions

Types of Non-Probability Sampling

WieldyToad avatar
WieldyToad
Probability Sampling Techniques Quiz
10 questions

Probability Sampling Techniques Quiz

EngagingGhost avatar
EngagingGhost
Técnicas de Muestreo No Probabilísticas
5 questions

Técnicas de Muestreo No Probabilísticas

DiplomaticColosseum avatar
DiplomaticColosseum
Use Quizgecko on...
Browser
Open
Browser
Browser