the logic of sampling
Post on 31-Dec-2015
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Key Sampling Concepts
• Sampling (two types)• Element• Population• Sample• Sampling Frame• Representative Sample
Types of Samples
• Probability -- Strictly following two rules.• Non Probability -- Failing to follow the two
rules
Types of Probability Samples
• Simple Random Sample (SRS)• Systematic Random Sample• Stratified Random Sample• Cluster Sample
Simple Random Sample
• Every element has an equal chance of selection
• No element can be selected more than once
Simple Random Sample
• Every element has an equal chance of selection
• No element can be selected more than once
Non probability samples
• Convenience (available to researcher)• Snowball (available connections)• Quota (stratified without randomness) • Informant (case study/social history)• Focus Groups
What’s the difference?How important is the difference?
Probability samples can be generalized to a population; while non-probability samples cannot.
Non-probability offer an in depth understanding and are most often: “I don’t know what I am seeking until after I find it.”
Following is an illustration:
Overview of Sample ProblemsHite, S. (1987). Women in Love: A Cultural Revolution in Progress. NY: Alfred A. Knopf.
Laumann, E. O., Gagnon, J. H., Michael, R. T. & Michaels, S. (1994). The Social Organization of Sexuality: Sexual Practices in the United States. Chicago: University of Chicago Press.
Sample Size Selection
The problem with the following formula:It is calibrated for dichotomous data. The sample size will increase with the number of options given to the subject.
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