The course will deepen your knowledge about sample surveys and their planning. The aim of this course is to cover sampling design and analysis methods that would be useful for research and management in many fields. A well designed sampling procedure ensures that we can summarize and analyze data with a minimum of assumptions and complications.

In this course, we’ll cover the basic methods of sampling and estimation and then explore selected topics and recent developments.

This course starts with an introduction to sampling; we then provide an overview to sampling; and the distinction between probability sampling and non-probability (e.g., quota) sampling is discussed.

we then proceed to talk about how to estimate population mean, population total and population proportion under simple random sampling, stratified sampling, systematic sampling and cluster sampling (with associated estimation and confidence interval methods)

We focus how to use auxiliary information about the population to estimate unknown population parameters of interest in ratio and regression estimation

We also covered some recently developed non-probability sampling designs

After completing the course students should be able to

explain the advantages and disadvantages of standard sampling designs

choose appropriate sampling designs for different selection problems

choose suitable estimators depending on the problem and the access to auxiliary information

carry out estimation and precision estimation on data from some standard sampling designs with and without auxiliary information

describe and practically use some basic estimation methods for non-probability problems

Sampling: Introduction and preliminaries, Sample survey and complete enumeration, Steps in a sample survey, Planning of sample survey, Sampling and non-sampling errors, Bias, Accuracy and Precision, Probability and nonprobability sampling.

Simple Random Sampling: Introduction, Simple random sampling with and without replacement, Drawing a simple random sample, Estimation of population characteristics- mean, total, proportion, and their confidence intervals, Determination of sample size.

Stratified Random Sampling: Introduction and principles of stratification, Estimation of population mean, total and their variances, Allocation of sample size in different strata, Ideas of post and deep stratifications.

Systematic Sampling: Linear and circular systematic sampling, Estimation of mean, total and their variances, Sample size determination, Comparison of systematic sampling with other sampling methods, Two-dimensional systematic sampling.

Cluster Sampling: Cluster sampling with  equal and unequal size, Estimation of mean, total and their variances, Determination of cluster size,  Estimation of mean and its variance, Optimum allocation of sample size at different stages.

Use of Auxiliary Information:  Ratio, Difference, Regression and Product methods of estimation, Estimation of the population parameter and their variances, Mean square error, Separate, combined, ratio and regression estimators, Comparison of the estimators.

Non-probability Sampling: Purposive sampling, Judgment sampling, Quota sampling, Convenience sampling. Snowball sampling, with their Merits, Demerits and applications.

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