Presentation on theme: "Estimation in Stratified Random Sampling"— Presentation transcript:
1Estimation in Stratified Random Sampling SADC Course in StatisticsEstimation in Stratified Random Sampling(Session 07)
2Learning Objectives By the end of this session, you will be able to explain what is meant by stratification, how a stratified sample is drawn, and its advantagesexplain proportional or Neyman’s allocation of sample sizes to each stratumcompute estimates of the population mean and population total from results of a stratified random sampledetermine measures of precision for the above estimates
3Review of stratified sampling We recall first that stratification is done when it is possible to divide the population into groups (strata) so that the within group variance is small, ideally as small as possible.From each stratum, a sample of suitable size is drawn, usually using simple random sampling.The greatest challenge is in defining a suitable stratification variable.It is useful when information is required for each stratum (e.g. each region in a country) as well as for the whole population.
4Advantages of stratification Sampling from each stratum guarantees that the overall sample is more representative of the whole population compared to a simple random sampleIf each stratum is more homogeneous, i.e. less variable than the population as a whole with respect to key responses of interest, then estimates will be more preciseLikely to be administratively convenient, e.g. when different sampling procedures need to be applied to different strata (see ELUS example in Practical 2 for large sized estates of >500ha)
5Sampling with proportional allocation Suppose there are m strata and a sample of size ni is chosen from the Ni units in stratum i.Then total population size is N = Ni , while the sample size is n = ni .Often convenient to choose ni so thatThis is called proportional allocation
6Sampling using Neyman’s allocation If costs of sampling are the same in each stratum, but variability is different (although homogeneous within strata), then sensible to take more samples where there is greater variability, i.e. sample in proportion to the standard deviation.The appropriate value of ni in this case, see below, is called Neyman’s (or optimum) allocation.
7Other issues and allocation methods Above assumes within-stratum variances Si are known. A pilot run or a previous study may give estimates.But results from a pilot run may give very poor estimates, since they will often be based on very small sample sizesAlso note that Neyman’s allocation may lead to very few units being sampled from some strata – not useful if separate results for each stratum are also needed.Other methods of allocation exists, e.g. incorporating possible differences in sampling costs
8Estimating the population mean First carry out computations for each stratum, i.e. find mean and variance for ith stratum.The estimate the population mean is then, with variance
9Estimating the population total As with the mean, first find an estimate for the total in ith stratum, i.e.The estimate the population total is then, with varianceNote: Use expressions on the previous page in computing these estimates
10An exampleGovernment agricultural inspectors carry out a survey of cattle ownership in a region divided into 3 administrative areas. Five farms are selected from each area and the number of cattle recorded as shown below. The total number of farms is 636.AreaNumber of farmsNo of cattle11868, 50, 92, 60, 3422140, 0, 4, 12, 24323616, 4, 28, 46, 28
11Questions to answer Ni 1 - fi Note: fi = ni/Ni in ith stratum. What is the mean number of cattle per farm?What is the total number of cattle in the region?First need to compute some summaries:AreaNi1 - fi118648.8969.20.973122148.0104.00.9766323624.4244.80.9788Note: fi = ni/Ni in ith stratum.
12Answers for estimating mean The mean number of cattle per farm is estimated as:= /636 =i.e. Approximately 26 cows per farm.This has variance:=Hence its std. error = 5.0
13Answers for estimating total The total number of cattle in the region is estimated as:= 636 x = 16547This has variance:= (636)2 xHence its standard error is636 x =
14Estimating population proportion As with the mean, first find an estimate for proportion in ith stratum, i.e. pi = ri/niThe estimate the population proportion is then, with variance
15ReferencesBarnett, V. (1974) Elements of Sampling Theory. Edward Arnold. ISBNLevy, P.S. and Lemeshow, S. (1999) Sampling and Populations: Methods and Applications (3rd edition) Wiley, New York. ISBNLohr, S.L. (1999) Sampling: Design and Analysis. International Thomson Publishing. ISBN