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Statistics for Business and Economics 6th Edition Chapter 20 Sampling: Additional Topics in Sampling Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-1 Chapter Goals After completing this chapter, you should be able to: Explain the basic steps of a sampling study Describe sampling and nonsampling errors Explain simple random sampling and stratified sampling Analyze results from simple random or stratified samples Determine sample size when estimating population mean, population total, or population proportion Describe other sampling methods Cluster Sampling, Two-Phase Sampling, Nonprobability Samples Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-2 Steps of a Sampling Study Step 6: Conclusions? Step 5: Inferences From Step 4: Obtaining Information? Step 3: Sample Selection? Step 2: Relevant Population? Step 1: Information Required? Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-3 Sampling and Nonsampling Errors A sample statistic is an estimate of an unknown population parameter Sample evidence from a population is variable Sample-to-sample variation is expected Sampling error results from the fact that we only see a subset of the population when a sample is selected Statistical statements can be made about sampling error It can be measured and interpreted using confidence intervals, probabilities, etc. Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-4 Sampling and Nonsampling Errors (continued) Nonsampling error results from sources not related to the sampling procedure used Examples: The population actually sampled is not the relevant one Survey subjects may give inaccurate or dishonest answers Nonresponse to survey questions Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-5 Types of Samples Probability Sample Items in the sample are chosen on the basis of known probabilities Nonprobability Sample Items included are chosen without regard to their probability of occurrence Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-6 Types of Samples (continued) Samples Probability Samples Simple Random Non-Probability Samples Stratified Systematic Judgement Cluster Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Convenience Quota Chap 20-7 Simple Random Samples Suppose that a sample of n objects is to be selected from a population of N objects A simple random sample procedure is one in which every possible sample of n objects is equally likely to be chosen Only sampling without replacement is considered here Random samples can be obtained from table of random numbers or computer random number generators Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-8 Systematic Sampling Decide on sample size: n Divide frame of N individuals into groups of j individuals: j=N/n Randomly select one individual from the 1st group Select every jth individual thereafter N = 64 n=8 First Group j=8 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-9 Finite Population Correction Factor Suppose sampling is without replacement and the sample size is large relative to the population size Assume the population size is large enough to apply the central limit theorem Apply the finite population correction factor when estimating the population variance finite population correction factor Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Nn N Chap 20-10 Estimating the Population Mean Let a simple random sample of size n be taken from a population of N members with mean μ The sample mean is an unbiased estimator of the population mean μ The point estimate is: 1 n x xi n i1 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-11 Estimating the Population Mean (continued) An unbiased estimation procedure for the variance of the sample mean yields the point estimate 2 s N n 2 σˆ x n N Provided the sample size is large, 100(1 - )% confidence intervals for the population mean are given by x z α/2σˆ x μ x z α/2σˆ x Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-12 Estimating the Population Total Consider a simple random sample of size n from a population of size N The quantity to be estimated is the population total Nμ An unbiased estimation procedure for the population total Nμ yields the point estimate NX Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-13 Estimating the Population Total An unbiased estimator of the variance of the population total is 2 s N2σˆ 2x N(N n) n Provided the sample size is large, a 100(1 - )% confidence interval for the population total is Nx z α/2Nσˆ x Nμ Nx z α/2Nσˆ x Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-14 Confidence Interval for Population Total: Example A firm has a population of 1000 accounts and wishes to estimate the total population value A sample of 80 accounts is selected with average balance of $87.6 and standard deviation of $22.3 Find the 95% confidence interval estimate of the total balance Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-15 Example Solution N 1000, n 80, x 87.6, s 22.3 2 2 s (22.3) N σˆ N(N n) (1000)(920 ) 5718835 n 80 2 2 x Nσˆ x 5718835 2391.41 Nx z α/2N σˆ x (1000)(87. 6) (1.96)(239 1.41) 82912.84 Nμ 92287.16 The 95% confidence interval for the population total balance is $82,912.52 to $92,287.16 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-16 Estimating the Population Proportion Let the true population proportion be P Let p̂ be the sample proportion from n observations from a simple random sample The sample proportion, p̂ , is an unbiased estimator of the population proportion, P Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-17 Estimating the Population Proportion (continued) An unbiased estimator for the variance of the population proportion is ˆ (1 pˆ ) (N n) p σˆ n 1 N 2 pˆ Provided the sample size is large, a 100(1 - )% confidence interval for the population proportion is pˆ zα/2σˆ pˆ P pˆ zα/2σˆ pˆ Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-18 Stratified Sampling Overview of stratified sampling: Divide population into two or more subgroups (called strata) according to some common characteristic A simple random sample is selected from each subgroup Samples from subgroups are combined into one Population Divided into 4 strata Sample Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-19 Stratified Random Sampling Suppose that a population of N individuals can be subdivided into K mutually exclusive and collectively exhaustive groups, or strata Stratified random sampling is the selection of independent simple random samples from each stratum of the population. Let the K strata in the population contain N1, N2,. . ., NK members, so that N1 + N2 + . . . + NK = N Let the numbers in the samples be n1, n2, . . ., nK. Then the total number of sample members is n1 + n2 + . . . + nK = n Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-20 Estimation of the Population Mean, Stratified Random Sample Let random samples of nj individuals be taken from strata containing Nj individuals (j = 1, 2, . . ., K) Let K K Nj N and n j n j1 j1 Denote the sample means and variances in the strata by Xj and sj2 and the overall population mean by μ An unbiased estimator of the overall population mean μ is: 1 K x st Nj x j N j1 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-21 Estimation of the Population Mean, Stratified Random Sample (continued) An unbiased estimator for the variance of the overall population mean is σˆ 2xst where 1 2 N K 2 2 N j σˆ x j j1 2 s (N j n j ) j 2 σˆ x j nj Nj Provided the sample size is large, a 100(1 - )% confidence interval for the population mean for stratified random samples is x st zα/2σˆ xst μ x st zα/2σˆ xst Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-22 Estimation of the Population Total, Stratified Random Sample Suppose that random samples of nj individuals from strata containing Nj individuals (j = 1, 2, . . ., K) are selected and that the quantity to be estimated is the population total, Nμ An unbiased estimation procedure for the population total Nμ yields the point estimate K Nx st Nj x j j1 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-23 Estimation of the Population Total, Stratified Random Sample (continued) An unbiased estimation procedure for the variance of the estimator of the population total yields the point estimate K N2σˆ 2xst N2jσˆ 2xst j1 Provided the sample size is large, 100(1 - )% confidence intervals for the population total for stratified random samples are obtained from Nx st z α/2Nσˆ st Nμ Nx st z α/2Nσˆ st Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-24 Estimation of the Population Proportion, Stratified Random Sample Suppose that random samples of nj individuals from strata containing Nj individuals (j = 1, 2, . . ., K) are obtained Let Pj be the population proportion, and p̂ j the sample proportion, in the jth stratum If P is the overall population proportion, an unbiased estimation procedure for P yields K 1 pˆ st Njpˆ j N j1 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-25 Estimation of the Population Proportion, Stratified Random Sample (continued) • An unbiased estimation procedure for the variance of the estimator of the overall population proportion is σˆ p2ˆ st 1 K 2ˆ2 2 Nj σ pˆ j N j1 where pˆ j (1 pˆ j ) (N j n j ) σˆ nj 1 Nj 2 pˆ j is the estimate of the variance of the sample proportion in the jth stratum Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-26 Estimation of the Population Proportion, Stratified Random Sample (continued) Provided the sample size is large, 100(1 - )% confidence intervals for the population proportion for stratified random samples are obtained from pˆ st zα/2σˆ pˆ st P pˆ st zα/2σˆ pˆ st Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-27 Proportional Allocation: Sample Size One way to allocate sampling effort is to make the proportion of sample members in any stratum the same as the proportion of population members in the stratum If so, for the jth stratum, nj n Nj N The sample size for the jth stratum using proportional allocation is nj Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Nj N n Chap 20-28 Optimal Allocation To estimate an overall population mean or total and if the population variances in the individual strata are denoted σj2 , the most precise estimators are obtained with optimal allocation The sample size for the jth stratum using optimal allocation is nj N jσ j n K N σ i1 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. i i Chap 20-29 Optimal Allocation (continued) To estimate the overall population proportion, estimators with the smallest possible variance are obtained by optimal allocation The sample size for the jth stratum for population proportion using optimal allocation is nj N j Pj (1 Pj ) K N i1 i Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. n Pi (1 Pi ) Chap 20-30 Determining Sample Size The sample size is directly related to the size of the variance of the population estimator If the researcher sets the allowable size of the variance in advance, the necessary sample size can be determined Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-31 Sample Size, Mean, Simple Random Sampling Consider estimating the mean of a population of N members, which has variance σ2 2 If the desired variance, σ x of the sample mean is specified, the required sample size to estimate the population mean through simple random sampling is Nσ 2 n (N 1)σ 2x σ 2 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-32 Sample Size, Mean, Simple Random Sampling (continued) Often it is more convenient to specify directly the desired width of the confidence interval for the population mean rather than σ 2x Thus the researcher specifies the desired margin of error for the mean Calculations are simple since, for example, a 95% confidence interval for the population mean will extend an approximate amount 1.96 σ x on each side of the sample mean, X Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-33 Required Sample Size Example 2000 items are in a population. If σ = 45, what sample size is needed to estimate the mean within ± 5 with 95% confidence? N = 2000, 1.96 σ x = 5 → σ x = 2.551 Nσ 2 (2000)(45) 2 n 269.39 2 2 2 2 (N 1)σ x σ (1999)(2.5 51) (45) So the required sample size is n = 270 (Always round up) Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-34 Sample Size, Proportion, Simple Random Sampling (continued) Consider estimating the proportion P of individuals in a population of size N who possess a certain attribute 2 If the desired variance, σpˆ , of the sample proportion is specified, the required sample size to estimate the population proportion through simple random sampling is NP(1 P) n (N 1)σ p2ˆ P(1 P) Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-35 Sample Size, Proportion, Simple Random Sampling (continued) The largest possible value for this expression occurs when the value of P is 0.25 nmax 0.25N (N 1)σ p2ˆ 0.25 A 95% confidence interval for the population proportion will extend an approximate amount 1.96 σpˆ on each side of the sample proportion Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-36 Required Sample Size Example How large a sample would be necessary to estimate the true proportion of voters who will vote for proposition A, within ±3%, with 95% confidence, from a population of 3400 voters? Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-37 Required Sample Size Example (continued) Solution: N = 34000 For 95% confidence, use z = 1.96 1.96 σ pˆ s = .03 → σ pˆ s = .015306 nmax 0.25N (0.25)(340 00) 1035.47 2 2 (N 1)σ pˆ 0.25 (33999)(.0 153) 025 So use n = 1036 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-38 Sample Size, Mean, Stratified Sampling Suppose that a population of N members is subdivided in K strata containing N1, N2, . . .,NK members Let σj2 denote the population variance in the jth stratum An estimate of the overall population mean is desired If the desired variance, σ 2xst , of the sample estimator is specified, the required total sample size, n, can be found Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-39 Sample Size, Mean, Stratified Sampling (continued) For proportional allocation: K 2 N σ j j j1 n Nσ 2 xst 1 K N jσ 2j N j1 For optimal allocation: 1 K 2 N jσ j N j1 n 1 K 2 Nσ x s t N jσ 2j N j1 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-40 Cluster Sampling Population is divided into several “clusters,” each representative of the population A simple random sample of clusters is selected Generally, all items in the selected clusters are examined An alternative is to chose items from selected clusters using another probability sampling technique Population divided into 16 clusters. Randomly selected clusters for sample Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-41 Estimators for Cluster Sampling A population is subdivided into M clusters and a simple random sample of m of these clusters is selected and information is obtained from every member of the sampled clusters Let n1, n2, . . ., nm denote the numbers of members in the m sampled clusters Denote the means of these clusters by x1, x 2, , xm Denote the proportions of cluster members possessing an attribute of interest by P1, P2, . . . , Pm Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-42 Estimators for Cluster Sampling (continued) The objective is to estimate the overall population mean µ and proportion P Unbiased estimation procedures give Mean Proportion m xc n x i1 m i n i 1 m i i Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. p̂c n p i1 m i i n i1 i Chap 20-43 Estimators for Cluster Sampling (continued) Estimates of the variance of these estimators, following from unbiased estimation procedures, are Mean σˆ 2xc Proportion m 2 2 n ( x x ) i i c M m i1 Mm n 2 m 1 σˆ p2ˆ c m 2 2 ˆ n (P p ) i i c M m i1 Mm n 2 m 1 m Where n n i1 m i is the average number of individuals in the sampled clusters Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-44 Estimators for Cluster Sampling (continued) Provided the sample size is large, 100(1 - )% confidence intervals using cluster sampling are for the population mean xc zα/2σˆ xc μ xc zα/2σˆ xc for the population proportion pˆ c zα/2σˆ pˆ c P pˆ c zα/2σˆ pˆ c Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-45 Two-Phase Sampling Sometimes sampling is done in two steps An initial pilot sample can be done Disadvantage: takes more time Advantages: Can adjust survey questions if problems are noted Additional questions may be identified Initial estimates of response rate or population parameters can be obtained Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-46 Non-Probability Samples Samples Probability Samples Simple Random Non-Probability Samples Stratified Systematic Judgement Cluster Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Convenience Quota Chap 20-47 Non-Probability Samples (continued) It may be simpler or less costly to use a nonprobability based sampling method Judgement sample Quota sample Convience sample These methods may still produce good estimates of population parameters But … Are more subject to bias No valid way to determine reliability Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-48 Chapter Summary Reviewed basic steps in a sampling study Defined sampling and nonsampling errors Examined probability sampling methods Simple Random Sampling, Systematic Sampling, Stratified Random Sampling, Cluster Sampling Identified Estimators for the population mean, population total, and population proportion for different types of samples Determined the required sample size for specified confidence interval width Examined nonprobabilistic sampling methods Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 20-49