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Chapter 5

# SOAN 2120 Chapter Notes - Chapter 5: Telephone Directory, Systematic Sampling, Sapeh

Department
Sociology and Anthropology
Course Code
SOAN 2120
Professor
William Walters
Chapter
5

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SOAN 2120 Chapter 5 notes pg. 116-132
Types of Probability Samples
Simple random sample: researcher develops an accurate sampling frame, selects elements
from the sampling frame according to a mathematically random procedure then locates the
exact element that was selected for inclusion in the sample
- After numbering all elements in a sampling frame, researcher uses list of random
numbers to decide which elements to select
- There needs to be as many random numbers as there are elements to be sampled
oEx. Sample of 100, 100 random numbers needed
oRandom numbers can came from random-number table (table of numbers
chosen in mathematically random way)
- Unrestricted random sampling= random sampling with replacement (replacing an
element after sampling so it can be selected again)
- With simple random sampling researcher ignores elements already selected into the
sample
- Set of many random samples = sampling distribution
oDistribution of different samples that shows the frequency of different sample
outcomes from many separate random samples
- Pattern in sampling distribution suggest that over many separate samples, the true
population parameter is more common than any other result
- When many different random samples are plotted, the sampling distribution looks like a
normal or bell-shaped curve
oThis curve is theoretically important and used throughout statistics
- The central limit theorem from mathematics tells us as the number of different random
samples in sampling distribution increase towards infinity, the pattern of samples
become more predictable
- Random sampling does NOT guarantee every random sample represents the population
oIt means most random samples will be close to the population and one can
calculate the probability of a particular sample being inaccurate
This can be done my using information from the sample to estimate the
sampling distribution
Combine this information with central limit theorem to construct
confidence intervals
-Confidence intervals: range around a specific point used to estimate a population
parameter
oA range is used b/c stats of random process do not = exact point, however they
let the researcher say with high level of confidence the true population parameter
lies within a certain range
Systematic Sampling: simple random sampling with shortcut for random selection
- Virtually equivalent results as simple random sample
- First step: number each element in sampling frame
oInstead of using list of random numbers, researcher calculates a sampling
interval which becomes their quasi-random selection method
- The sampling interval tells researcher how to select elements in the frame before
selecting one for a sample
- Systematic sampling can’t be substituted for simple random sampling when the
elements in a sample are organized in a kind of cycle or pattern
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