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|Title:||A Simulation Study to Examine a New Method of Sampling When Information on the Target Population Is Very Limited|
|Keywords:||Statistics and Probability;Statistics and Probability|
|Abstract:||<p>A new sampling method was developed for use in areas where standard methods for sampling from a population may be difficult or impossible to use. The new method uses Global Positioning System (GPS) points and satellite photos to identify datapoints (locations) within selected towns. A circle (of a specified radius) is drawn around each datapoint, which may include several buildings or sometimes none. A single household is sampled from each circle and then one adult (aged 18 years or older) is interviewed from the household, based on who had the most recent birthday. Two issues arise with this new sampling method: first, the probability of sampling any household may vary, depending on the datapoint its chosen from; and second, circles surrounding the chosen datapoints may overlap. The thesis used simulations to see whether the first issue affected the point estimates of two population parameters. The second issue was too complex to investigate so it was ignored when sampling households. Simulations were run to test the sampling method on two different hypothetical towns, one with denser population than the other. Results from the simulations showed that estimates did not always match what was expected, but the observed differences were not substantial. It was presumed that the reason for differences was due to the issue of multiple probabilities for selection as well as overlapping circles (which had been ignored during sampling), since both issues were inherent in this sampling method. Although some differences were observed from true values, they appeared not to be very different from the population values, so I conclude that this sampling method is a useful one. Future work may look more closely at understanding the issue of overlapping circles and how it affects the point estimates.</p>|
|Appears in Collections:||Open Access Dissertations and Theses|
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