However a 10 interval may be considered unreasonably large Should more

However a 10 interval may be considered unreasonably

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However, a 10% interval may be considered unreasonably large . Should more precision be required (i.e., a smaller, more useful Margin of Error) or greater confidence desired (0.01), the other columns of the table should be employed. 06/03/20 19
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An Example Thus, if you have 5000 customers and you want to sample a sufficient number to generate a 95% confidence interval that predicted the proportion who would be repeat customers within plus or minus 2.5%, you would need responses from a (random) sample of 1176 of all your customers. As you can see, using the table is much simpler than employing a formula. 06/03/20 20
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Response Rates About 20 – 30% usually return a questionnaire Follow up techniques could bring it up to about 50% Still, response rates under 60 – 70% challenge the integrity of the random sample How the survey is distributed can affect the quality of sampling 06/03/20 21
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Data Collection Methods 06/03/20 22
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Where do data come from? We’ve seen our data for this lab, all nice and collated in a database – from: Insurance companies (claims, medications, procedures, diagnoses, etc.) Firms (demographic data, productivity data, etc.) Take a step back – if we’re starting from scratch, how do we collect / find data? Secondary data Primary data 06/03/20 23
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What sort of Data are these- Primary or secondary? Cross-sectional Panel Time series 06/03/20 24
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Secondary Data – Examples of Sources Secondary data – data someone else has collected County health departments Vital Statistics – birth, death certificates Hospital, clinic, school nurse records Private and foundation databases City and county governments Surveillance data from state government programs Ghana statistics Service - Census, GLSS data, etc. 06/03/20 25
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Secondary Data – Limitations What did you find on the frustrating side as you looked for data on the state’s websites? When was it collected? For how long? May be out of date for what you want to analyze. May not have been collected long enough for detecting trends. 06/03/20 26
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Secondary Data – Limitations Is the data set complete? There may be missing information on some observations Unless such missing information is caught and corrected for, analysis will be biased. Are there confounding problems? Sample selection bias? Source choice bias? In time series, did some observations drop out over time? 06/03/20 27
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Secondary Data – Limitations Are the data consistent/reliable?
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