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Stratified and Cluster Sampling Stratified and Cluster Sampling Robb T. Koether Stratified and Cluster Sampling Introduction Stratified Lecture 8 Random Samples Sections 2.6, 2.8 Example Estimating Parameters Cluster Robb T. Koether Samples Example Hampden-Sydney College Stratified vs. Cluster Assignment Tue, Jan 27, 2008 Outline Stratified and Cluster Sampling 1 Introduction Robb T. Koether 2 Stratified Random Samples Introduction Stratified Example Random Samples Example 3 Estimating Parameters Estimating Parameters 4 Cluster Samples Cluster Samples Example Example Stratified vs. Cluster 5 Stratified vs. Cluster Assignment 6 Assignment Introduction Stratified and Cluster Sampling Suppose we know that our population is 60% male and Robb T. Koether 40% female. Introduction Wouldn’t it make sense to ensure that our sample is Stratified also 60% male and 40% female. Random Samples Example After all, our goal is representativeness, not Estimating randomness, right? Parameters If our sample size were n = 100, we could Cluster Samples Select a simple random sample of 60 males. Example Select a simple random sample of 40 females. Stratified vs. Cluster Is this a good idea? Assignment If we left it to chance, we might end up with 50 males and 50 females. Introduction Stratified and Cluster Sampling Suppose we know that our population is 60% male and Robb T. Koether 40% female. Introduction Wouldn’t it make sense to ensure that our sample is Stratified also 60% male and 40% female. Random Samples Example After all, our goal is representativeness, not Estimating randomness, right? Parameters If our sample size were n = 100, we could Cluster Samples Select a simple random sample of 60 males. Example Select a simple random sample of 40 females. Stratified vs. Cluster Is this a good idea? Assignment If we left it to chance, we might end up with 50 males and 50 females. Introduction Stratified and Cluster Sampling Suppose we know that our population is 60% male and Robb T. Koether 40% female. Introduction Wouldn’t it make sense to ensure that our sample is Stratified also 60% male and 40% female. Random Samples Example After all, our goal is representativeness, not Estimating randomness, right? Parameters If our sample size were n = 100, we could Cluster Samples Select a simple random sample of 60 males. Example Select a simple random sample of 40 females. Stratified vs. Cluster Is this a good idea? Assignment If we left it to chance, we might end up with 50 males and 50 females. Introduction Stratified and Cluster Sampling Suppose we know that our population is 60% male and Robb T. Koether 40% female. Introduction Wouldn’t it make sense to ensure that our sample is Stratified also 60% male and 40% female. Random Samples Example After all, our goal is representativeness, not Estimating randomness, right? Parameters If our sample size were n = 100, we could Cluster Samples Select a simple random sample of 60 males. Example Select a simple random sample of 40 females. Stratified vs. Cluster Is this a good idea? Assignment If we left it to chance, we might end up with 50 males and 50 females. Introduction Stratified and Cluster Sampling Suppose we know that our population is 60% male and Robb T. Koether 40% female. Introduction Wouldn’t it make sense to ensure that our sample is Stratified also 60% male and 40% female. Random Samples Example After all, our goal is representativeness, not Estimating randomness, right? Parameters If our sample size were n = 100, we could Cluster Samples Select a simple random sample of 60 males. Example Select a simple random sample of 40 females. Stratified vs. Cluster Is this a good idea? Assignment If we left it to chance, we might end up with 50 males and 50 females. Introduction Stratified and Cluster Sampling Suppose we know that our population is 60% male and Robb T. Koether 40% female. Introduction Wouldn’t it make sense to ensure that our sample is Stratified also 60% male and 40% female. Random Samples Example After all, our goal is representativeness, not Estimating randomness, right? Parameters If our sample size were n = 100, we could Cluster Samples Select a simple random sample of 60 males. Example Select a simple random sample of 40 females. Stratified vs. Cluster Is this a good idea? Assignment If we left it to chance, we might end up with 50 males and 50 females. Introduction Stratified and Cluster Sampling Suppose we know that our population is 60% male and Robb T. Koether 40% female. Introduction Wouldn’t it make sense to ensure that our sample is Stratified also 60% male and 40% female. Random Samples Example After all, our goal is representativeness, not Estimating randomness, right? Parameters If our sample size were n = 100, we could Cluster Samples Select a simple random sample of 60 males. Example Select a simple random sample of 40 females. Stratified vs. Cluster Is this a good idea? Assignment If we left it to chance, we might end up with 50 males and 50 females. Introduction Stratified and Cluster Sampling Suppose we know that our population is 60% male and Robb T. Koether 40% female. Introduction Wouldn’t it make sense to ensure that our sample is Stratified also 60% male and 40% female. Random Samples Example After all, our goal is representativeness, not Estimating randomness, right? Parameters If our sample size were n = 100, we could Cluster Samples Select a simple random sample of 60 males. Example Select a simple random sample of 40 females. Stratified vs. Cluster Is this a good idea? Assignment If we left it to chance, we might end up with 50 males and 50 females. Introduction Stratified and Cluster Sampling Robb T. Koether Introduction On the other hand, is this necessary? Stratified What is we did end up with 50 males and 50 females? Random Samples Example Suppose male opinion in our sample was divided 20 Estimating yes, 30 no and female opinion was divided 10 yes, 40 Parameters no. Cluster Samples How would we come up with an overall estimate for the Example Stratified vs. proportion of yeses in the population? Cluster Assignment Introduction Stratified and Cluster Sampling Robb T. Koether Introduction On the other hand, is this necessary? Stratified What is we did end up with 50 males and 50 females? Random Samples Example Suppose male opinion in our sample was divided 20 Estimating yes, 30 no and female opinion was divided 10 yes, 40 Parameters no. Cluster Samples How would we come up with an overall estimate for the Example Stratified vs. proportion of yeses in the population? Cluster Assignment Introduction Stratified and Cluster Sampling Robb T. Koether Introduction On the other hand, is this necessary? Stratified What is we did end up with 50 males and 50 females? Random Samples Example Suppose male opinion in our sample was divided 20 Estimating yes, 30 no and female opinion was divided 10 yes, 40 Parameters no. Cluster Samples How would we come up with an overall estimate for the Example Stratified vs. proportion of yeses in the population? Cluster Assignment Introduction Stratified and Cluster Sampling Robb T. Koether Introduction On the other hand, is this necessary? Stratified What is we did end up with 50 males and 50 females? Random Samples Example Suppose male opinion in our sample was divided 20 Estimating yes, 30 no and female opinion was divided 10 yes, 40 Parameters no. Cluster Samples How would we come up with an overall estimate for the Example Stratified vs. proportion of yeses in the population? Cluster Assignment Stratified Random Sample Stratified and Cluster Sampling Definition (Homogeneous) Robb T. Koether A group is homogeneous if its member all have similar Introduction characteristics with regard to the variables of interest. Stratified Random Samples Definition (Stratum) Example Estimating A stratum is a homogeneous subset of the population. Parameters Cluster Samples Definition (Stratified random sampling) Example Stratified vs. Stratified random sampling is a sampling method in which Cluster the population is first divided into strata. Then a simple Assignment random sample is taken from each stratum. The results constitute the sample. Examples Stratified and Cluster Sampling Robb T. Koether Introduction Stratified Possible strata: Random Samples Male and female strata. Example Resident and non-resident strata. Estimating Parameters White, black, Hispanic, and Asian strata. Cluster Protestant, Catholic, Jewish, Muslim, etc., strata. Samples Example Stratified vs. Cluster Assignment Stratified Random Sampling Stratified and Cluster Sampling Robb T. Koether Introduction Stratified Random Samples Example Estimating Parameters Cluster Samples Example Stratified vs. Cluster Assignment The population Stratified Random Sampling Stratified and Cluster Sampling Robb T. Koether Introduction Stratified Random Samples Example Estimating The population Parameters Cluster Samples Example Stratified vs. Cluster Assignment The strata Stratified Random Sampling Stratified and Cluster Sampling Robb T. Random sample from this stratum Koether Introduction Stratified Random Samples Example Estimating The population Parameters Cluster Samples Example Stratified vs. Cluster Assignment The strata Stratified Random Sampling Stratified and Cluster Sampling Robb T. Random sample from this stratum Koether Introduction Stratified Random Samples Example Estimating The population Parameters Cluster Samples Example Stratified vs. Cluster Assignment The strata Stratified Random Sampling Stratified and Cluster Sampling Robb T. Koether Introduction Stratified Random Samples Example Estimating The population Parameters Cluster Samples Example Stratified vs. Cluster Assignment Random samples from all strata Stratified Random Sampling Stratified and Cluster Sampling Robb T. Koether Introduction Stratified Random Samples Example Estimating Parameters Cluster Samples Example Stratified vs. Cluster Assignment The stratified sample Example Stratified and Cluster Sampling Robb T. Koether Example (Stratified random sample) Introduction Stratified Let the population consist of males Anthony, Benjamin, Random Christopher, Daniel, Ethan, Francisco, Gabriel, and Samples Example Hunter and females Isabella, Jasmine, Kayla, Lily, Estimating Parameters Madison, Natalie, Olivia, and Paige.
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