MMC 3420 EXAM 2 | Questions with 100% Verified Answers | Latest Update
Question: Distinguish between a population and a sample
Answer:
Population is everyone, sample is just a portion of the population.
Question: Why do we take samples?
Answer:
Obtaining a group of people from a population in such a way as to be representative of that population
Question: What is the key difference between probability sampling and non-probability sampling?
Answer:
-Random or Probability Sampling: Everyone in the population has an equal chance of being included
Follows mathematical guidelines and theory that allow us to calculate with 95% confidence that the
sample results can be generalized
-Not everyone in the population has an equal chance of being chosen (Sampling for a specific purpose.
Only those who fit certain criteria)
Question: What is a sampling frame?
Answer:
All messages or people to be surveyed within our population (List of registered voters in Orange County)
Question: Universe
Answer:
-Specify boundaries of what's being explored
-Operational definitions: Topic area, time period, sources
Question: What are the types of non-probability samples?
Answer:
-Available/Convenience ("man on the street"): Mall intercept
-Purposive: Focus group participants (Selected for specific purpose, not to be representative)
-Quota: Selected to represent quotas of participants that exist in your population (Previous research said
population is 40% male, so you survey 40/100 males)
-Snowball: Identify small sample of population and then use a "pass-along" method
Question: What are the types of probability samples?
Answer:
-Simple Random Selection
-Random Sampling
,-Single or multi stage
-Systematic Random Selection
-Stratified Random Selection
Question: What is Simple Random Selection?
Answer:
-Ex. List of all home addresses in prime TV viewership market
-Start at an arbitrary point
-Follow our list of all household addresses in the TV market
-Use a random number generator until we meet our sample size
-Uses a randomly selected start number Each unit has an equal chance of being selected With or Without
Replacement
Question: Random Sampling
Answer:
Everyone in the sample has an equal chance of being chosen and responding (Regardless of researcher
biases) Allows researcher to infer beyond the sample to the population by knowledge of Population
parameters (characteristics) Estimating accuracy Estimating error of measurement
Question: Single or Multi stage
Answer:
Cross-sectional (single time) Longitudinal
-Trend: Randomly selected different people from a --population over time
-Panel: The same randomly selected people from a population over time
Question: Systematic Random Selection
Answer:
-Uses a sampling interval to select every nth unit; Widely used
-Subject to periodicity issues: Order of units in a list may introduce bias into selection
Question: Stratified Random Selection
Answer:
Chosen units represent a % of the population drawn at random from that population; Similar to quota
sampling, but done so with measures put into place to make it random
Question: Standard Error
Answer:
the standard deviation (or how samples deviate from the population)
-Regular old random error
, -Standard error decreases as sample size increases
Question: Standard Error continued...
Answer:
-allows you to calculate a confidence interval around a particular sample mean
-Tells you much error to expect between your sample mean and population mean
-This is determined by Size of the sample, and the Standard Deviation assoc. with the variable of interest
-A low sampling error indicates less variability or range in the sampling distribution of scores
Question: Sampling Error
Answer:
(margin of error, confidence interval) how representative sample is to the population
-May be statistically estimated, but only for probability samples
Question: Sampling Error continued...
Answer:
-The discrepancy between the sample statistic and the true population
-It is the margin of error - it can be set prior to data collection to help you determine necessary sample
size Our class is a sample of UCF students (N = 155) of a total -60,000 UCF student population
Question: What is the central limit theorem?
Answer:
For samples of a sufficiently large enough size, the real distribution of means is almost always about
normal. The original variable can have any distribution
Question: Law of Large Numbers?
Answer:
The smaller the size of a random sample, the less accurate it approximates the true population parameter
Question: Confidence Level
Answer:
The degree of assurance that an actual mean in a sampling distribution accurately represents the true
population mean
-We set this at 95% in the social sciences
Question: Confidence Interval (sampling error) Why does it matter that we sample appropriately in
research?
Answer:
Question: Distinguish between a population and a sample
Answer:
Population is everyone, sample is just a portion of the population.
Question: Why do we take samples?
Answer:
Obtaining a group of people from a population in such a way as to be representative of that population
Question: What is the key difference between probability sampling and non-probability sampling?
Answer:
-Random or Probability Sampling: Everyone in the population has an equal chance of being included
Follows mathematical guidelines and theory that allow us to calculate with 95% confidence that the
sample results can be generalized
-Not everyone in the population has an equal chance of being chosen (Sampling for a specific purpose.
Only those who fit certain criteria)
Question: What is a sampling frame?
Answer:
All messages or people to be surveyed within our population (List of registered voters in Orange County)
Question: Universe
Answer:
-Specify boundaries of what's being explored
-Operational definitions: Topic area, time period, sources
Question: What are the types of non-probability samples?
Answer:
-Available/Convenience ("man on the street"): Mall intercept
-Purposive: Focus group participants (Selected for specific purpose, not to be representative)
-Quota: Selected to represent quotas of participants that exist in your population (Previous research said
population is 40% male, so you survey 40/100 males)
-Snowball: Identify small sample of population and then use a "pass-along" method
Question: What are the types of probability samples?
Answer:
-Simple Random Selection
-Random Sampling
,-Single or multi stage
-Systematic Random Selection
-Stratified Random Selection
Question: What is Simple Random Selection?
Answer:
-Ex. List of all home addresses in prime TV viewership market
-Start at an arbitrary point
-Follow our list of all household addresses in the TV market
-Use a random number generator until we meet our sample size
-Uses a randomly selected start number Each unit has an equal chance of being selected With or Without
Replacement
Question: Random Sampling
Answer:
Everyone in the sample has an equal chance of being chosen and responding (Regardless of researcher
biases) Allows researcher to infer beyond the sample to the population by knowledge of Population
parameters (characteristics) Estimating accuracy Estimating error of measurement
Question: Single or Multi stage
Answer:
Cross-sectional (single time) Longitudinal
-Trend: Randomly selected different people from a --population over time
-Panel: The same randomly selected people from a population over time
Question: Systematic Random Selection
Answer:
-Uses a sampling interval to select every nth unit; Widely used
-Subject to periodicity issues: Order of units in a list may introduce bias into selection
Question: Stratified Random Selection
Answer:
Chosen units represent a % of the population drawn at random from that population; Similar to quota
sampling, but done so with measures put into place to make it random
Question: Standard Error
Answer:
the standard deviation (or how samples deviate from the population)
-Regular old random error
, -Standard error decreases as sample size increases
Question: Standard Error continued...
Answer:
-allows you to calculate a confidence interval around a particular sample mean
-Tells you much error to expect between your sample mean and population mean
-This is determined by Size of the sample, and the Standard Deviation assoc. with the variable of interest
-A low sampling error indicates less variability or range in the sampling distribution of scores
Question: Sampling Error
Answer:
(margin of error, confidence interval) how representative sample is to the population
-May be statistically estimated, but only for probability samples
Question: Sampling Error continued...
Answer:
-The discrepancy between the sample statistic and the true population
-It is the margin of error - it can be set prior to data collection to help you determine necessary sample
size Our class is a sample of UCF students (N = 155) of a total -60,000 UCF student population
Question: What is the central limit theorem?
Answer:
For samples of a sufficiently large enough size, the real distribution of means is almost always about
normal. The original variable can have any distribution
Question: Law of Large Numbers?
Answer:
The smaller the size of a random sample, the less accurate it approximates the true population parameter
Question: Confidence Level
Answer:
The degree of assurance that an actual mean in a sampling distribution accurately represents the true
population mean
-We set this at 95% in the social sciences
Question: Confidence Interval (sampling error) Why does it matter that we sample appropriately in
research?
Answer:
