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Introduction to Sampling (Dr. Monticino)

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Assignment Sheet Read Chapter 19 carefully Quiz # 10 over Chapter 19 Assignment # 12 (Due Monday April 25 th ) Chapter 19 Exercise Set A: 1-6,8,11

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Overview Language of statistics Obtaining a sample

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Statistical Terms Population The whole class of individuals of interest Voters Customers Marbles in a box Parameter Numerical facts about the population Percentage who will vote for candidate A Average income Proportion of white marbles

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Statistical Terms Sample Part of a population 1000 eligible voters called at random First 400 customers on Tuesday morning 5 marbles drawn from the box with replacement Statistic Numerical value obtained from sample used to estimate population parameter

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Sampling Generally, determining population parameters by studying the whole population is impractical Thus, inferences about population parameters are made from sample statistics This requires that the sample represent the population

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Sampling To obtain a representative sample, probability methods are used Employ an objective chance process to pick the sample No discretion is left to the interviewer The probability of any particular individual in the population being selected in the sample can be computed

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Simple Random Sampling Most straightforward sampling method is simple random sampling Individuals in the sample are drawn at random from the population without replacement Each individual is equally likely to be selected and each possible subset of individuals is equally likely to be selected Care must be taken to ensure that the selection process is not biased

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Other Sampling Techniques Multi-stage cluster sampling

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Other Sampling Techniques Quota sampling Sample is hand-picked to resemble the population with respect to selected key characteristics Selection bias Response/Non-response bias

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Good and Bad Samples Samples obtained by probability methods give a good representation of the population In theory, simple random sampling gives best representation Cluster samples, properly weighted, provide reasonable compromise between representing population and practical issues

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Good and Bad Samples Quota samples typically introduce selection and response/non-response bias Samples of convenience rarely represent the population. Avoid these When a sampling procedure is biased, taking a larger sample does not help

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Good and Bad Samples When examining a sample survey, ask: What is the population? What is the parameter being estimated? How was the sample chosen? What was the response rate? Address these same questions when designing a sampling procedure

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Sampling Error Even a well designed sampling procedure may result in an estimate which differs from the true value of the population parameter Bias Chance error It is important to have a measure of the sampling error of the parameter estimate (Dr. Monticino)

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Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 1.1 Chapter Five Data Collection and Sampling.

Copyright © 2005 Brooks/Cole, a division of Thomson Learning, Inc. 1.1 Chapter Five Data Collection and Sampling.

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