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Soc 232 March 20.doc

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SOC 232
Stuart Leard

Sth 232 March 20 2013 1 Example of multi-stage cluster sampling Random Sample of Canadian Adult Population: Randomly select clusters, e.g. five provinces or territories from a list of all provinces and territories. Randomly select a number of subunits from each cluster (province/territory) e.g. census districts. From each census district selected, randomly select census subdivisions. Randomly select a certain number of residential blocks from each census subdivision selected. Randomly select a number of households from each block selected. Randomly select a person to be included in the study from each selected household. Cluster samples are usually stratified as well, i.e. divided into strata or subgroups before the clusters or subunits are selected . In our example, to ensure regional representation, the provinces and territories might be categorized into regions: BC, Prairies, Ontario, Quebec, Atlantic provinces, and the northern territories. A certain number of provinces or territories could be randomly selected from each region that contains more than one province or territory. Then the next steps would be taken. Qualities of a probability sample representative - allows for generalization from sample to population. inferential statistical tests. sample means can be used to estimate population means. standard error (SE): estimate of discrepancy between sample mean and population mean. 95% of sample means fall between +/- 1.96 SE from population mean. Sampling Error Probability samples with sufficient sample sizes minimize the amount of sampling error, but some sampling error is bound to occur. e.g. there is usually some difference between a sample mean and the population mean (μ) that it is designed to represent. About 95 per cent of all sample means lie within 1.96 standard errors of the mean. Hence this range is called a confidence interval, a 95% confidence interval. Sample Size The absolute size of the sample matters. (not the proportion of the population that it comprises.) Sth 232 March 20 2013 2 As sample size increases, sampling error tends to decrease. Common sample sizes:100, 400, 900, 1600, 2500. Each size increase cuts the sampling error by 1/2, then 1/3, then 1/4, and then 1/5 respectively. The biggest change occurs between 100 and 400. Is an increased sample size worth the time and effort? Often sample size is dictated by financial concerns. Non-response The response rate is the percentage of the sample that participates in the study. Is there some particular issue common to the non-responders that brings them to differ in some important way from those who participate? Heterogeneity of the Population Generally, the greater the heterogeneity of the population on the characteristics of interest, the larger the sample size should be. Kind of Analysis The sample size needed may vary depending on what sort of analysis will be done. Types
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