Stat2Lab16

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Statistics

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Apr 3, 2024

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Stat 2 Lab #16: Sample surveys Learning objectives: Identify various types of bias in sample surveys Critique the design of sample surveys and analyze the impact of a flawed survey design Describe probability and non-probability sampling methods 1. A study conducted at Stanford University in 2005 wanted to evaluate what percentage of students had used the drug Ecstasy. Two assistants were stationed on the main campus plaza and instructed to interview all students who passed through at specified times. As it turned out, 19% of 369 students interviewed said they had used Ecstasy at least once. Does the investigator’s procedure give a probability sample of Stanford students? Answer yes or no, and explain. If this is not a probability sample, what term best describes it? 2. Suppose that a simple random sample of 100 residential phone numbers is taken in a large city (you may assume that each house has exactly one phone line) and an interviewer receives 68 responses after calling back at a variety of different times. In order to avoid nonresponse, the interviewer chooses additional random houses (same method) until the desired 100 responses are obtained. The total number of people living at those 100 households is 287, so the interviewer estimates the average household size in the city as 2.87. Is this estimate likely to be biased to be too low, too high, or unbiased? Explain. This is not a probability sample, since a specific group of individuals were not selected to be focused on. Instead, this is a non response biased sample and sample of convenience because students who don’t care as much could have not responded and the assistants only included students who were close in proximity to them. The estimate is likely to be too high, since homes with more people in it were more likely to have the phone answered. This means that houses with fewer people were excluded from the sample, making the estimate an overshooting of the average household size.
3. One of the findings after the 2016 U.S. Presidential election was that polls tended to have an over-representation of those with college degrees. This kind of over- representation may be due to selection bias (who is called) and/or non-response bias (who answers the phone). In this problem, we’ll look at how it could come about through non-response bias. Data for the problem is loosely based on voter registration data from Allegheny County in Pennsylvania. Suppose a particular county contains 950,000 registered voters. 380,000 of them (40%) have college degrees and 570,000 of them (60%) do not. Among those with college degrees, 304,000 (80%) support the Democratic candidate in a presidential election. Among those without college degrees, 313,500 (55%) support the Democratic candidate. (a) Based on this information, what percent of all registered voters in the county support the Democratic candidate? _____% In reality, of course, we would not know what percent of these voters supported the Democratic candidate; we’d have to collect data about it. Suppose that the phone numbers of all 950,000 registered voters were available and that a polling organization choses some of them at random to call. (b) If 8% of those with college degrees and 4% of those without college degrees will answer their phones, but these rates do not depend on which candidate the voter supports, then what is the chance that someone who DOES answer the phone supports the Democratic candidate? _____% (Hint: First find the number of those with and without college degrees who will answer their phones. Now find the number within each of these groups who support the Democratic candidate. Can you put this information together to answer the question?) 65 support dam . 000 + 313500 : - - 950 , 000 total registered voters = 0 . 65 7 1 . 6 7 #college degree who answer : 380000 < 0 08 : 30400 -320 + 8360 = &dem : 80 % - > 30400 x 0 8 = 24320 30400 + 15200 45600 = 0 7167 # non college degree who answer : 380000 x 0 04 = 15200 Idem : 33 % - > 15700 < 0 . 55 : 8360
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