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Lecture

# SOAN 2120 Lecture Notes - Simple Random Sample, Nonprobability Sampling, Central Limit Theorem

Department
Sociology and Anthropology
Course Code
SOAN 2120
Professor
David Walters

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November 27, 2012
Handing in the Final Assignment
- due outside of class Thursday (1-4pm)
- the late penalty is 5% per day
- after 5 pm on Thursday, it is LATE
- after the deadline, submit the assignment under the door (614 MACK)
- turnitin.com
o ID: 5632747
- TOMORROW AFTERNOON (LAB): 20 MACK 1 PM 4PM
- Chapter 5 [ THIS SHIT IS IMPORTANT]
o Nonprobability sampling
Sample size in advance they do not know the sample size in
Random they do not know if it is random
Knowledge of population they know very little
Mathematical theory
Types of nonprobability samples (types, page 109)
o Probability sampling [EXACT OPPOSITE OF NONPROBABILITY]
Random must have
Knowledge of population
Mathematical theory sampling methods are based heavily on
this
o Goal of Probability Sample?
To make
Populations, elements, and sampling frames
Population: boundaries
Geographical location
Employers at an organization
Students in the school
People who became parents I 2012
o Populations are abstract
Variable
Nevertheless, we need to estimate it
Generate list of what the population looks like
The list is the sampling frame (telephone directories, tax
Sample element: unit of analysis within the population
Sample frame should be a good representation of the
population
What if it’s not?
o Eg. telephone directories? Not everyone has a
telephone.

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Sampling technique in order to collect sample
We want it to estimate the probability that everyone in
the population has a chance to be in our sample
SRS (simple random sampling)
Assign a number to each element in the sampling frame
Use a random number generator
Equal probability
Benefit of Random Sampling sampling distribution
(clt) inferences
Only with random sampling are the results we get from
our statistical test valid. The central limit theorem
applies only in the context that our sample was taken
randomly or through the weights….
Systematic sampling
First decide on your sample size
Population 1000
Sample 100
Sampling interval: 10
pick one random number and then every 10 thereafter
Stratified Sampling
Divide population into sub-populations and then
sample within stratum
Stratum are picked by the researcher
o Eg. provinces, educational groups, religious
organizations, etc.
o So you stratify your population in these
groupings this guarantees you get one of
each
Guarantees stratums are represented in the survey
(a certain number of people are chosen from each
stratum… like P.E.I., Inuit, Ph.D)
Weight variables allow us to adjust probability…
Cluster Sampling
Identify clusters (i.e. cities, blocks, households)
o Simple random sample clusters
o Simple random sample units within clusters
Population Guelph
o 1: City blocks
o 2: households (within the blocks)
o 3: individuals (within the households)
implications: we get three levels of sources of
random error