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Carleton University
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Communication Studies
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COMM 2002
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Heather Pyman
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Lecture

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Communication Studies

COMM 2002

Heather Pyman

Fall

Description

Sampling
•Representative Samples
o Populations - Parameters
o Samples - Statistics
o Probability sampling vs Non-probability sampling
• How do we know that samples we select are representative of the
population?
• Collect data in a random sampling method -> key is that each person has
an equal and likely chance to be selected for the sample
Have to know the population and then have to be able to illustrate
and prove that every person ahd the same chance in being selected for
tehs ample as everyone else does
• When we collect them, the analysis ran creates statistics: descriptive
statistical analysis
• Inferential statics: Generalizing from a sample to a situation
If we are going to say the sample is representative of the
population, we have to conduct a probability based method
Random, representative or probability based method -> know the
probability of any one person being chosen form that sample
Researchers are not interested in the population
• Quality research -> ontological and epistemological is more attitude and
behaviour rather than inferring form the research that they've done form a
population -> non probability, sometimes do quantitative work
•Quality of a probability sample
o Representative - allows for generalization from sample to population
o Dependent on three things
• Accuracy f the sampling frame
• Sample size
• Sampling method
Willing to take a 5% risk to what the actual sample is
How to collect the sample; accurately describe the population ->
determine a sampling frame, frame where we are going to collect the
sample
• If the population is all students from Carleton, could be the
registar's list, student IDs, etc.
•Probability sampling
o Inferential statistical tests
o Sample statistics can be used to estimate population parameters
o Standard error (SE): Estimate f discrepancy between sample mean and
population
• Standard deviation of sample distribution to estimate the discrepancy
between the population value and other
o
Remember that sample statistics are not likely to be actual population values
•Unrepresentative Probability Samples
o Not using a random sampling method
o Sampling frame is too small
o
Non-response
o Sampling error • Frame could be too small -> not enough sample to represent all of the
population
Could happen when elements of the population [in the sampling
frame] are not known to the researcher
Researchers are looking for a 50% response rate, that is the
people who did not answer, are not significant and are not a larger
amount of group that did answer the survey
• Sample size
o Absolute size matters more than relative size
o
The larger the sample, the more precise and representative it is likely to be
o As sample size increases, sampling error decreases, BUT there is a point of
diminishing returns
• If you decrease a number, would be looking at a sampling error, cut the
sample size into a quarter means you've reduced error by 50%
• If you went to a sample size of 100 from 1000 then it is error rates of 3%
• Think of sample as absolute -> no
• Things to be determined when determining sample size
o Homogeneity of the sample
o
Number of variables in the study
o The desired degree of accuracy
o Time and cost
o Non-response
o
Kind of analysis to be carried out
• What we need to consider during the data analysis is how much we want
to break the survey down
•

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