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**Introduction Simple Random Sampling Stratified Random Sampling**

Chap 7: Survey Sampling Introduction Simple Random Sampling Stratified Random Sampling

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7.1: Introduction For small pop’n, a census study are used because data can be gathered on all. For large pop’n, sample surveys will be used to obtain information from a small (but carefully chosen) sample of the pop’n. The sample should reflect the characteristics of the pop’n from which it is drawn. Sampling methods are classified as either probabilistic or non-probabilistic in nature.

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**Sampling methods: Probability Sampling: Random Sampling**

Systematic Sampling Stratified Sampling NonProbabilitySampling ConvenienceSampling Judgment Sampling Quota Sampling Snowball Sampling

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**The winner is: Probability Sampling.**

In non-probability sampling, members are selected from the pop’n in some non-random manner and the sampling error (=degree to which a sample might differ from the pop’n) is unknown. In probability sampling, each member of the pop’n has a specified probability of being included in the sample. Its advantage is that sampling error can be calculated.

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7.2: Pop’n parameters Definition: Parameters are those numerical characteristics of the pop’n that we will estimate from a sample. Notations:

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**Pop’n mean, total, variance:**

Pop’n total: Pop’n variance: its square root is the StdDev

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**7.3: Simple Random Sampling**

The most elementary form of sampling is s.r.s. where each member of the pop’n has an equal and known chance of being selected at most once. There are possible samples of size n taken without replacement. In this section, we will derive some statistical properties of the sample mean.

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**7.3.0: The Sample Mean: The sample mean estimates the pop’n mean where**

so that will estimate the pop’n total

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**7.3.1: Expectation & Variance of the Sample Mean:**

Theorem A (UNBIASEDNESS) : under s.r.s. , Theorem B: under s.r.s. , Recall: The variance of in sampling without replacement differs from that in sampling with replacement by the factor which is called the finite population correction. The ratio is called the sampling fraction.

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**7.3.2: Estimation of the Population Variance:**

Theorem A: under s.r.s., where Corollary A: An unbiased estimator of is given by

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**7.3.3: Normal approximation to the sampling dist’n of the sample mean**

We will be using the CLT (Central Limit Theorem, see Section 5.3) in order to find the probabilistic bounds for the estimation error. Application 1: the probability that the error made in estimating by is using the CLT. Application 2: a CI (Confidence Interval) for the pop’n mean is given by using the CLT.

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**7.4: Estimating a ratio: Ratio arises frequently in Survey Sampling.**

If a bivariate sample is drawn, then the ratio is estimated by We wish to derive E(R) and Var(R) using approximation methods seen in Section 4.6 because R is a nonlinear function.

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**7.4: Estimating a ratio (cont’d)**

Theorem A: With s.r.s., the approximate variance of R is Since the population correlation pop’n is then

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**7.4: Estimating a ratio (cont’d)**

Theorem B: With s.r.s., the approximate expectation of R is

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**Standard Error estimate of R:**

The estimate variance of R is where and the pop’n covariance is estimated by

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**Confidence Interval for r:**

An approximate CI (Confidence Interval) for the ratio of interest r is given by

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**7.5: Stratified Random Sampling:**

7.5.1: Introduction (S.R.S.) The pop’n is partitioned into sub-pop’s or strata (stratum, singular) that are then independently sampled and are combined to estimate pop’n parameters. A stratum is a subset of the population that shares at least one common characteristic Example: males & females; age groups;…

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**Why is Stratified Sampling superior to Simple Random Sampling?**

S.R.S. reduces the sampling error S.R.S. guarantees a prescribed number of observations from each stratum while s.r.s. can’t The mean of a S.R.S. can be considerably more precise than the mean of a s.r.s., if the pop’n members within each stratum are relatively homogeneous and if there is enough variation between strata.

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**7.5.2: Properties of Stratified Estimates:**

Notation: Let be the total pop’n size if denote the pop’n sizes in the L strata. The overall pop’n mean is a weighted average of the pop’n means of the L strata, where denotes the fraction of the pop’n in the stratum.

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**7.5.2: Properties of Stratified Estimates: (cont’d)**

Stratified sampling requires two steps: Identify the relevant strata in the pop’n Use s.r.s. to get subject from each stratum Within each stratum, a s.r.s. of size is taken to obtain the sample mean in the stratum will be denoted by where denotes the observation in the stratum.

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**7.5.2: Properties of Stratified Estimates: (cont’d)**

Theorem A: The stratified estimate, , of the overall pop’n mean is UNBIASED. Since we assume that the samples from different strata are independent of one another and that within each stratum a s.r.s. is taken, then the variance of can be easily calculated in: Theorem B: The variance of the stratified sample is

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**Neglecting / Ignoring the finite population correction:**

Approximation: If the sampling fractions within all strata were small, Theorem B will then reduce to:

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**Expectation and Variance of the stratified estimate of the pop’n total:**

This is a corollary of Theorems A & B. Practice with examples A & B in the textbook on pages to get healthy with these calculations.

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**7.5.3: Methods of allocation:**

For small sampling fractions within strata i.e. when neglecting/ignoring the finite pop’n correction, Question: How to choose to minimize subject to the constraint when resources of a survey allowed only a total of n units to be sampled? Note: We could include finite pop’n corrections but the results will be more complicated. Try it!

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7.5.3 a: Neyman allocation Theorem A: The samples sizes that minimize subject to the constraint are given by Corollary A: stratified estimate & optimal allocations

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**7.5.3 b: Proportional allocation**

If a survey measures several attributes for each pop’n member, it will be difficult to find an allocation that is simultaneously optimal for each of those variables. Using the same sampling fraction in each stratum will provide a simple and popular alternative method of allocation.

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**7.5.3b:Proportional allocation (cont’**

Theorem B: With stratified sampling based on proportional allocation, ignoring the finite pop’n correction, Theorem C: With stratified sampling based on both allocation methods, ignoring the finite pop’n correction,

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7.6:Conclusions A mathematical model for Survey Sampling was built using s.r.s. and probabilistic error bounds for the estimates derived. The theory and techniques of survey probability sampling include Systematic Sampling, Cluster Sampling, etc…as well as non-probability sampling methods such as Quota Sampling, Snowball Sampling,…

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