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Lecture 7

HLTB15H3 Lecture Notes - Lecture 7: Normal Distribution, Systematic Sampling, Standard Deviation


Department
Health Studies
Course Code
HLTB15H3
Professor
Caroline Barakat
Lecture
7

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Cluster Sampling
Sampling units are not easily identified!
Cluster sampling - sampling population is
i)divided into groups clusters
ii)elements within each cluster are selected
Different levels of clustering are possible!
Example:
Cluster 1 all provinces or territories
Cluster 2 major cities
Cluster 3 school boards
Cluster 4 schools
This process is called multi-stage cluster sampling
NON-RANDOM (NON-PROBABILITY) SAMPLING DESIGNS
Does not follow the theory of probability!
Number of elements either unknown or cannot be individually identified
Types of non-random sampling designs
Quota Sampling
Access
Visible characteristic
Consent to participate
Accidental sampling
-not based on an obvious or visible characteristic
-common among market research and media
Judgemental or purposive sampling
-Who can provide the best information?
-Application - construction of a historical reality or description of a phenomenon
Advantages:
-Least expensive
-No need for information on sampling frame, sample elements, location....
-Inclusion of participants needed for the study
Disadvantages:
-Findings cannot be generalized
Snowball Sampling
Networks
Few individuals are selected
Asked to identify other individuals
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