Podcast
Questions and Answers
Which type of nonprobability sampling involves selecting respondents based on their accessibility?
Which type of nonprobability sampling involves selecting respondents based on their accessibility?
What is a major disadvantage of using nonprobability sampling methods?
What is a major disadvantage of using nonprobability sampling methods?
In which sampling method are participants selected based on specific characteristics or criteria?
In which sampling method are participants selected based on specific characteristics or criteria?
What does snowball sampling rely on for participant recruitment?
What does snowball sampling rely on for participant recruitment?
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What is a key characteristic of quota sampling?
What is a key characteristic of quota sampling?
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Why is sample size calculation important in research studies?
Why is sample size calculation important in research studies?
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Which type of error results from an inaccurate response in sampling?
Which type of error results from an inaccurate response in sampling?
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What defines a convenience sample?
What defines a convenience sample?
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What is the main advantage of using sampling over conducting a census?
What is the main advantage of using sampling over conducting a census?
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What defines the 'target population' in sampling?
What defines the 'target population' in sampling?
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Which of the following is NOT one of the three elements of the sampling process?
Which of the following is NOT one of the three elements of the sampling process?
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What is meant by the term 'sampling fraction'?
What is meant by the term 'sampling fraction'?
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Why might a researcher resurvey their sample?
Why might a researcher resurvey their sample?
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Which method best describes the process of statistical inference?
Which method best describes the process of statistical inference?
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What is a sampling scheme?
What is a sampling scheme?
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Which statement correctly describes a census?
Which statement correctly describes a census?
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What is a consequence of an oversized study in research?
What is a consequence of an oversized study in research?
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What impact does a more stringent significance level (e.g., 1%) have on sample size?
What impact does a more stringent significance level (e.g., 1%) have on sample size?
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Which factor determines the capacity of a study to detect differences or effects present in a population?
Which factor determines the capacity of a study to detect differences or effects present in a population?
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What is the effect of larger variability in a population on required sample size?
What is the effect of larger variability in a population on required sample size?
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Which study hypothesis type generally requires a smaller sample size to identify differences?
Which study hypothesis type generally requires a smaller sample size to identify differences?
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When estimating sample size, what factor must researchers account for to ensure viability of their results?
When estimating sample size, what factor must researchers account for to ensure viability of their results?
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What method is NOT typically used for calculating sample size?
What method is NOT typically used for calculating sample size?
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What does the p-value represent in statistical analysis?
What does the p-value represent in statistical analysis?
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What is a characteristic of probability sampling methods?
What is a characteristic of probability sampling methods?
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What defines systematic sampling?
What defines systematic sampling?
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What is the primary advantage of simple random sampling?
What is the primary advantage of simple random sampling?
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Which of the following statements regarding non-probability sampling is true?
Which of the following statements regarding non-probability sampling is true?
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What is a disadvantage of using simple random sampling?
What is a disadvantage of using simple random sampling?
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How is the sampling interval calculated in systematic sampling?
How is the sampling interval calculated in systematic sampling?
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In systematic sampling, how is the first element selected?
In systematic sampling, how is the first element selected?
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What does representativeness in sampling refer to?
What does representativeness in sampling refer to?
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What is a key advantage of systematic sampling?
What is a key advantage of systematic sampling?
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Which of the following accurately describes stratified sampling?
Which of the following accurately describes stratified sampling?
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What is a common drawback of using cluster sampling?
What is a common drawback of using cluster sampling?
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Which of the following best summarizes a disadvantage of stratified sampling?
Which of the following best summarizes a disadvantage of stratified sampling?
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Which of the following is true about multi-stage sampling?
Which of the following is true about multi-stage sampling?
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What is a characteristic of clusters in cluster sampling?
What is a characteristic of clusters in cluster sampling?
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How does systematic sampling typically affect cost and time efficiency?
How does systematic sampling typically affect cost and time efficiency?
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What is a primary goal when conducting stratified sampling?
What is a primary goal when conducting stratified sampling?
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Study Notes
Sampling Techniques & Sample Size Estimation
- Sampling is the process of selecting a group of items from a population.
- A census involves a complete enumeration of all items in the population.
- A census offers the highest accuracy but requires significant time, money, and energy.
- Sampling is more practical for large populations because it reduces costs, time, and possible bias.
- Statistical inference is based on the process of sampling to draw conclusions about the population.
Population & Sample
- Population: The collection of all responses, measurements, or counts of interest to the researcher.
- Sample: A subset of the population.
- A population could be composed of individuals, families, groups, organizations, events, objects, or items. Examples include: presidents, professors, books, students, and red blood cells (RBCs).
Sampling: Process and Elements
- Sampling involves these three elements: selecting the sample, collecting information, and making inferences about the population.
Importance of Sampling
- Sampling allows researchers to gather information about large populations.
- It is more economical than studying the entire population.
- It reduces the time required for data collection.
- It can improve accuracy in certain situations.
- Sampling is often necessary due to the large sizes of many populations when it isn't feasible to study the entire population.
Statistical Terms
- Target Population: The population the researcher wants to generalize his results to.
- Sampling frame: A list of all the sampling units from which the sample is drawn.
- Sampling unit: The people or items that have been sampled; the target population is the collection of sampling units.
- Sampling fraction: The ratio of the sample size to the population size.
- Sample criteria: These include the criteria for inclusion and exclusion criteria needed for the study.
- Sampling scheme: The method of selecting sampling units from the sampling frame.
Steps of the Sampling Process
- Identify the target population.
- Identify the accessible population to be sampled (N).
- Determine the sample size needed (n) and the sample criteria.
- Select the sampling technique.
- Implement the plan.
Sampling Methods
- Probability samples: Each member of the population has a known chance (probability) of being selected in the study.
- Non-probability samples: The chances (probability) of selecting members from the population are unknown.
- Examples of probability sampling methods include: simple random sampling, systematic sampling, stratified sampling, and cluster sampling.
- Examples of non-probability sampling methods include: convenience sampling, judgmental sampling, quota sampling, and snowball sampling.
Probability Samples: Advantages
- Probability samples allow generalization of results.
- They ensure representativeness and precision.
Simple Random Sampling
- Procedure involves defining the population, choosing the sample size, creating a sampling frame by numbering all units, and randomly selecting units.
- The lottery method, table of random numbers and computer-generated random numbers can be used.
Simple Random Sampling: Advantages and Disadvantages
- Advantages: known and equal chance of selection, simple, easy process.
- Disadvantages: requires the complete sampling frame, not great for diverse groups.
Systematic Sampling
- Procedure involves selecting a suitable sampling frame, numbering each element, determining the sampling interval (I = N/n), selecting a random number (s) between 1 and k, and using the formula s, s+k, s+2k,..., s+(n-1)k to select the sample.
Systematic Sampling: Advantages and Disadvantages
- Advantages: known and equal chance of selection, less expensive and faster.
- Disadvantages: possible small loss in sampling precision
Stratified Sampling
- Principle: The population is partitioned into subgroups (strata) and elements are randomly selected from each stratum.
- Procedure includes identifying and defining the population, determining the desired sample size, identifying the variable.
- Randomly selecting elements from each stratum.
Stratified Sampling: Advantages and Disadvantages
- Advantages: more accurate.
- Disadvantages: more complex, sampling plan requiring different sample sizes for each stratum.
Cluster Sampling
- Principle: The population is divided into subpopulations (clusters), a random sample of clusters is selected.
- Procedure includes identifying and defining the population, determining the desired sample size, identifying and defining a logical cluster, listing all clusters, estimating the average number of population members per cluster, determining the number of clusters needed by dividing sample size by estimated size of a cluster.
- Randomly selecting the needed number of clusters, including all population members in each selected cluster.
Cluster Sampling: Advantages and Disadvantages
- Advantages: time and cost-efficient, particularly for samples that are geographically spread, high external validity.
- Disadvantages: more complex to plan, cluster members are more likely to be alike.
Multi-Stage Sampling
- Principle: Sampling is repeated at several levels, e.g. Country → State → City → School.
Non-probability Samples
- These sampling methods do not guarantee a known chance of selection for each member of the population.
- Examples include convenience sampling, judgmental sampling, quota sampling, and snowball sampling.
Convenience Sampling
- A sample is selected from population elements that are readily available.
- Often, respondents are selected because they happen to be in the right place at the right time.
- This type of sampling is also known as accidental sampling.
Judgmental/Purposive Sampling
- Participants are intentionally selected based on their characteristics, experiences, or other criteria.
Quota Sampling
- The population is divided into strata (e.g., male and female students).
- A proportion from each stratum is selected, but not randomly.
Snowball Sampling
- Participants are recruited through referrals from initial respondents (chain referral, friend-to-friend).
Non-probability Samples: Advantages and Disadvantages
- Advantages: simple, easy to use, helpful for pilot studies, short duration, cost-effectiveness.
- Disadvantages: highly vulnerable to selection bias, unclear generalizability, high sampling error.
Sampling Errors
- Random errors (sampling error): The difference between the sample and the population from which the sample is drawn. The size can be measured, expressed as the standard error of mean/proportion.
- Standard error depends on the sample size and distribution of the measured variable.
- Systematic errors (bias): Inaccurate response (info bias) or selection bias.
Sample Size Calculation
- Why calculate sample size? Small samples can lead to inaccurate conclusions.
- Undersized studies can expose participants to potential harm without providing meaningful conclusions.
- Oversized studies waste resources and possibly expose participants unnecessarily.
- Factors considered when determining minimum sample size include: significance or confidence level, power (1-β), effect size, variability, and the alternative study hypothesis (one-tail, two-tail).
Factors Determining Sample Size
- Significance level (alpha).
- Power (1 - beta).
- Effect size.
- Variability.
- Desired Confidence Level.
Significance Level (Alpha Level)
- It is the probability of making a Type I error (incorrectly rejecting a true null hypothesis).
- Most studies set alpha at 0.05, meaning they accept a 5% chance of making a Type I error.
- Higher confidence levels (e.g., 99%) will result in a larger required sample size.
Power (1 – Beta Level)
- It is the probability of detecting a true difference if one exists in the population.
- A higher power level (e.g. 0.80) means that the study is more likely to detect a difference if one exists in the population.
Effect Size
- The minimum difference a study should detect.
- Clinically significant differences.
- Generally, smaller effect sizes require larger samples.
Variability
- Measures how spread out the data is in a population.
Alternative Study Hypothesis
- One-tailed easier to identify differences (so smaller samples needed).
- Two-tailed more difficult to identify, so a larger sample size is required
Tools for Calculating Sample Size
- Formulas (specific to each study design).
- Nomograms.
- Prepared tables.
- Computer software.
Descriptive Equation for a Single Proportion
- Used in calculating sample size for proportions. The equation needs specific values for Z, P, Q, confidence level, and degree of precision.
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Description
Test your knowledge on various nonprobability sampling methods and their characteristics. This quiz covers key concepts such as convenience sampling, snowball sampling, and sample size calculation. Enhance your understanding of sampling methods used in research studies.
