Survey Design and Analysis - Coursework Example

Download file to see previous pages (i) Correctness of arguments used in explanations. (75%) (ii) Clarity of arguments used in explanations. (25%) The accuracy of confidence interval calculated from survey data increases as the survey's sample size increases because the standard error involved in survey data is inversely proportional to the sample size and as the sample size increases, standard error decreases and hence the accuracy of confidence interval increases so that the sample mean approaches more close to the population mean in probability (ie. consistency improves to great extent). For example if the confidence interval is wider enough ie.99% there is a high confidence of the population mean falling into the confidence interval rather than 95% confidence interval. Here the Sample Mean plus or minus 2 times the Standard Error is the confidence interval and it leads to prediction of population mean with 95% confidence where it is called as 2sigmal limits. Whenever the confidence interval is widening as much as possible, it has high probability of including the population mean. This section reserved for feedback from tutor Mark: [XX] out of 10 Comment: Part Two In the box below, give recommendations as to when stratified sampling can be useful when conducting a survey. (10 marks) Marks will be awarded according to the following criteria. (i) Correctness of arguments used in recommendations. ...
When stratification is done the within sample variance will decrease and the accuracy improves to a considerable extent. The stratification can be according to geographic constraint, economic consideration, educational consideration and the stratification reduces the sample variance. The stratification can be accompanied by cost or without cost. When it is done proportional to the population size, it is called Neyman allocation. When the cost constraint is involved, the cost per stratum should be proportional to the inverse of the variance. The cost per stratum is ch and the stratum variance is Sh. Within each stratum we have to adopt to sampling (simple random sampling) and compute the stratum mean or variance. Stratification leads to reduction in the population variance when compared to other sampling methods viz. systematic sampling and simple random sampling. This section reserved for feedback from tutor Mark: [XX] out of 10 Comment: Part Three In the box below, give recommendations as to when cluster sampling can be useful when conducting a survey. (10 marks) Marks will be awarded according to the following criteria. (i) Correctness of arguments used in recommendations. (75%) (ii) Clarity of arguments used in recommendations. (25%) Cluster sampling can be useful when the sample size is equal among different sampling methods. It is widely used in marketing research where a huge population is divided into groups (clusters) and a sample of the groups is selected. After selecting groups, subsamples from each group forms the sample for this type of sampling. For a given stipulated expenses, it gives large sample size. Cluster sampling can be one stage cluster sampling, two stage cluster sampling or multi stage cluster sampling. For example consider sampling ...Download file to see next pagesRead More