ISDS 2000 : ISDS EXAM 2
15 Mar 2019
School
Department
Course
Professor
Frame
• A listing of items that make up the population.
• Data sources such as: population list, directories, or maps
• Samples are drawn down from frames
Nonprobability Sample
• You select items or individuals without knowing their probabilities of selection
• Cannot be used for statistical inference
• Types of Nonprobability Sampling: Convenience Sampling and Judgment Sampling
❖ Convenience Sampling
▪ Items selected are easy, inexpensive, or convenient to sample
▪ Advantages: speed and low cost
❖ Judgment Sampling
▪ You get the opinions of preselected experts in the subject matter. Although
the experts may be well informed, you cannot generalize their results to the
population
Probability Sample
• You select items based on know probabilities
• Whenever possible you should use probability sampling methods
• Allows you to make inferences about the population of interest
• Types of Probability Sample: Simple Random Sample, Systematic Sample, Stratified
Sample, and Cluster Sample
❖ Simple Random Sample
▪ Every item from a frame has the same chance of selection as every other item.
▪ Every sample of a fixed size has the same chance of selection as every other
sample of that size
▪ Simple random sampling is the most elementary random sampling technique
▪ You use n to represent the sample size and N to represent the frame size, you
number every item in the frame from 1 to N. The chance that you will select
any particular member of the frame one the first selection is 1/N.
o Sampling With Replacement
➢ Meant that after you select an item, you return it to the frame,
where it has the same probability of being selected again.
o Sampling Without Replacement
➢ Means that once you select an item, you cannot select it again.
➢ The chance that you will select any card not previously
selected on the second draw is 1/N out of N – 1.
o Table of Random Numbers
➢ A table of random numbers consists of a series of digits listed
in a randomly generated sequence.
➢ This probability is 1 out of 10. Hence, if you generate a
sequence of 800 digits, you would expect about 80 to be the
digit 0, 80 to be the digit 1, and so on.
❖ Systematic Samples
▪ You parathion the N items in the frame into n groups of k items, where K=N/n
▪ You first chose the first item to be selected at from the first k items in the
frame.
▪ Then you select the remaining n – 1 items by taking every kth item thereafter
from the entire frame.
▪ Frames consist mostly of:
✓ Pre-numbered checks
✓ Sales Receipts
✓ Invoices
▪ Convenient mechanism for collecting data from:
✓ Telephone Books
✓ Class Rosters
✓ Consecutive items
coming off an assembly
line
▪ If there is a pattern in the frame, you could have serve selection bias.
▪ Simple random sampling an systematic sampling are simpler then other, more
sophisticated probability sampling methods, but generally require a larger
sample size.
❖ Stratified Samples
▪ You first subdivide the N items in the frame into separate subpopulations, or
strata (defined by some common characteristic, such as gender or year in
school).
▪ You select a simple random sample with each of the strata and combine the
results from the separate simple random samples
▪ More efficient than either simple random sampling or systematic sampling
because you rare ensured the representation of items across the entire
population.
❖ Cluster Samples
▪ You divide the N items in the frame into several clusters so that each cluster is
representative of the entire population.
▪ Clusters are naturally occurring designations, such as countries, elections
districts, city blocks, households, or sales territories.
▪ You then take a random sample of one or more clusters and study all items in
each selected clusters.
Document Summary
Frame: a listing of items that make up the population, data sources such as: population list, directories, or maps, samples are drawn down from frames. Nonprobability sample: you select items or individuals without knowing their probabilities of selection, cannot be used for statistical inference, types of nonprobability sampling: convenience sampling and judgment sampling. Items selected are easy, inexpensive, or convenient to sample: advantages: speed and low cost. Judgment sampling: you get the opinions of preselected experts in the subject matter. Although the experts may be well informed, you cannot generalize their results to the population. Probability sample: you select items based on know probabilities, whenever possible you should use probability sampling methods, allows you to make inferences about the population of interest, types of probability sample: simple random sample, systematic sample, stratified. Meant that after you select an item, you return it to the frame, where it has the same probability of being selected again: sampling without replacement.
