EST 521 Learning Objectives- Introduction and Chapter 1
List the advantages of sampling compared with a census.
Distinguish between a target population and a sampled population when appropriate.
Recognize a sampling unit and propose appropriate sampling frames for selecting these units.
List the advantages of probability-based samples and classify nonprobability samples into categories.
Distinguish between sampling error and nonsampling sources of error.
Paraphrase the concepts of bias, precision, accuracy, and consistency.
Learning Objectives- Chapter 2 Simple Random Sample
Describe the 2 properties that constitute a simple random sample.
Determine the probability that a sampling unit is included in a SRS of size n.
Apply the procedure for selecting a SRS to any sampling frame.
Prove that the sample mean from a SRS is an unbiased estimator of the population mean.
Determine if the sample variance (or std. dev.) is an unbiased estimator of the finite population variance (or std. dev.).
Apply Cornfields method of using an indicator variable in deriving estimators of variance.
Justify the choice of sampling without or with replacement in certain scenarios.
Learning Objectives- Chapter 3 Confidence Intervals
Explain what a confidence interval represents and how it should be interpreted.
Construct confidence intervals for SRS estimators of population mean and total.
Explain what the actual coverage of a confidence interval method is.
List methods of confidence interval construction when the estimator is nonnormal.
Learning Objectives- Chapter 4 Sample Size Estimation
Explain the components underlying a margin of error (interval estimator framework).
Compute the estimated sample size for estimating a population mean (or total) within specified conditions.
Explain the difference in finite and infinite population approaches to sample size estimation
Use a relative margin of error approach to sample size estimation.
Learning Objectives- Chapter 5 Proportions
Explain the similarity in estimating a population proportion and population mean.
Judge when to use which of 3 confidence interval construction methods for proportions.
Estimate the necessary sample size for estimating a population proportion within specified conditions.
Learning Objectives- Chapter 6 Unequal Probability Sampling
Distinguish between equal probability and unequal probability samples.
Propose situations under which unequal probability sampling is reasonable.
Compute the Hansen-Hurwitz estimator of the population total and its estimated variance.
Prove the Hansen-Hurwitz estimator is unbiased. Explain the derivation of the variance estimator.
Demonstrate the use of probability proportional to size sampling and modify the Hansen-Hurwitz estimator accordingly.
Compute the Horvitz-Thompson estimator of the population total and its estimated variance.
Prove the Horvitz-Thompson estimator is unbiased. Explain the unbiasedness proof of the variance estimator.
Justify the choice of the Hansen-Hurwitz or Horvitz-Thompson estimator for a given unequal probability sample.
Learning Objectives- Chapter 11 Stratified Sampling
List the Advantages of stratified random sampling.
Apply a stratified design to a sampling problem and compute estimators of population total, mean and their associated variances.
Explain why a stratified sample is more precise than a SRS
Recognize the differences among methods of Allocation- Proportional, Optimal, Neyman- i.e., what considerations enter sample size allocation and apply them when appropriate.
Apply Poststratification to a set of data
List the advantages and disadvantages of poststratification over prestratification
Learning Objectives- Chapter 12 Systematic and Cluster Sampling
Identify the similarities and differences between the two approaches.
List the advantages and disadvantages of each approach.
Compute estimates of population total and mean and their respective estimated variances.
Explain the conditions under which gain in precision occurs
Explain the variance estimation problem with single systematic sample and present possible solutions
Recognize the use of PPS sampling within each of these approaches.
Learning Objectives- Adaptive Cluster Sampling
Describe the methodology and explain when it is advantageous (see chapter 24 for refresher)
Learning Objectives- Chapter 13 Multistage Sampling
Distinguish between primary and secondary units
Compute estimates of the population total, primary unit average, secondary unit average and their estimated variances.
Explain the relationship to cluster sampling, and why it is useful
Estimate population size and its estimated variance using with or without replacement sampling
Learning Objectives- Chapter 7.1 and 8.1 Auxiliary Data, Ratio and Regression Estimators
Explain the benefit of using auxiliary data, ratio estimator and regression estimator
Compute estimates of the population total and mean and estimated variances using ratio or regression estimator.
Recognize why a ratio estimator is biased, and why it might be a better estimator than an unbiased estimator.
Compare the precision of SRS, ratio and regression estimators.
Know conditions under which a ratio or regression estimator will have high precision.
Demonstrate the equality of the ratio estimator variance structure to that of a SRS.