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Chapter 5

School

University of GuelphDepartment

Sociology and AnthropologyCourse Code

SOAN 2120Professor

William WaltersChapter

5This

**preview**shows half of the first page. to view the full**3 pages of the document.**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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