Randomness and Survey Sampling
Über den Vortrag
Der Vortrag „Randomness and Survey Sampling“ von David Spade, PhD ist Bestandteil des Kurses „Statistics Part 1“. Der Vortrag ist dabei in folgende Kapitel unterteilt:
- Randomness and Survey Sampling
- Random Sampling
- Selecting a Representative Sample
- Stratified Sampling
- Systematic Sampling
- Types of Bias
Quiz zum Vortrag
What is NOT an advantage of random sampling?
- Any random sample will be representative of the population about which it is used to make inferences.
- By randomly sampling, we ensure that separate outcomes do not affect each other.
- Sampling methods are prone to overemphasizing some characteristics of the population.
- Several software packages exist that will do the sampling for you.
- A subset of individuals is picked randomly without preference.
What is NOT a limitation of a census?
- A census is not representative of the population.
- A census can be extremely expensive to carry out.
- A census may not give information about the population that is as accurate as what is obtained from a random sample.
- A census can be very complex.
- A census may require a great deal of manpower.
What do we call the list of individuals from which the sample is drawn?
- This list is known as the sampling frame.
- This list is known as the population.
- This list is known as the sample.
- This list is known as a census.
- The list is known as the subgroup.
Suppose we ask the first 100 people who enter a store in the morning, whether they prefer to get up early and start the day or to sleep in. This sample is likely to be biased. What type of sampling scheme have we used?
- We have used a convenience sampling scheme.
- We have used a voluntary response sampling scheme.
- We have used a cluster sampling scheme.
- We have used a stratified sampling scheme.
- We have used a census sampling scheme.
Suppose that we ask 100 randomly selected married men whether they have ever cheated on their wives. What type of bias is this survey likely to suffer from?
- This survey will likely suffer from response bias.
- This survey should not suffer from any bias because the respondents were selected randomly.
- This survey will likely suffer from nonresponse bias.
- This survey will likely suffer from undercoverage.
- This survey will suffer from attrition bias.
Why do we sample?
- To learn about the population
- To learn about the sample
- To learn about the census
- To learn about bias
- To learn about regressions
Which sample size is likely to be the most representative of the population?
- 500
- 100
- 200
- 300
- 400
Which sample size is the best given their costs and benefits?
- Benefits = 200 ; Costs = 100
- Benefits = 100 ; Costs = 100
- Benefits = 200 ; Costs = 200
- Benefits = 150 ; Costs = 130
- Benefits = 10 ; Costs = 0
What is NOT true?
- Cluster sampling is also known as stratified sampling.
- Random sampling implies that the sample is representative of the population.
- A larger sample size is more accurate than a small sample size.
- The sample is always smaller than the population.
- The sampling strategy with the highest net benefit is chosen.
Dozent des Vortrages Randomness and Survey Sampling
David Spade, PhD
Dr. David Spade is an Assistant Professor of Mathematical Sciences and Statistics at the University of Wisconsin-Milwaukee and holds a courtesy appointment as an Assistant Professor of Statistics at the University of Missouri-Kansas City, USA.
He obtained his MS in Statistics in 2010 and then completed his PhD in Statistics from Ohio State University in 2013.
An experienced mathemathics instructor, Dr. Spade has been teaching diverse statistics courses from the introductory to the graduate level since 2007.
Within Lecturio, he teaches courses on Statistics.
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