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Ch-10-Sampling-Size-and-Error

# Ch-10-Sampling-Size-and-Error - Sampling Demystified Sample...

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Dr. G. Johnson, www.ResearchDe 1 Sampling Demystified: Sample Size and Errors Research Methods for Public Administrators Dr. Gail Johnson

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Dr. G. Johnson, www.ResearchDe 2 Samples: How Many? When working with non-random samples, size is not that important because researchers know that they can not generalize to the larger population Face validity is sufficient
Dr. G. Johnson, www.ResearchDe 3 Sample: How Many? When working with random sample data, size matters Researchers want a big enough sample so they can be reasonably confident that the results are a fairly accurate reflection of the population Statisticians have figured this out.

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Dr. G. Johnson, www.ResearchDe 4 Random Sample Size Sample size is a function of three things: Size of the population of interest Decision about how important is it to be accurate ? Confidence level Decision about how important is to be precise ? Sampling error (also called margin of error) or confidence interval In general, accuracy and precision is improved by increasing the sample size
Dr. G. Johnson, www.ResearchDe 5 Sample Size Based on probability theory and the concept of normal distributions Statisticians have figured this all out I believe, I believe!! We will focus on the concepts and application

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Dr. G. Johnson, www.ResearchDe 6 Random Samples is Based on Probabilities If we selected 1,000 random samples, the results for average height would theoretically form a bell- shaped curve (normal curve) This means that 95% of the samples would show an average height that was plus or minus 2 standard deviations. This statistical magic allows statisticians to estimate the probability of getting results from a random sample that are outside of that 95%
Dr. G. Johnson, www.ResearchDe 7 Bell-Shaped Curve (Normal Curve) http://commons.wikimedia.org/wiki/File:Standard_deviation_diagram.svg , Jeremy Kemp, on 2005-02-09

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Dr. G. Johnson, www.ResearchDe 8 Normal Curve Explained This is called a normal distribution. If we were to line up 1,000 people on the soccer field according to their height, they would look like a bell.
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