# Syllabus: Sampling Technique-I

Introduction:
basic concepts of sampling, sampling frame, sample survey vs census, requirements of a good sample, selection bias, measurement bias, sampling and nonsampling errors, probability & nonprobability samples, framework for probability sampling.
Simple probability Samples:
Simple random sampling (SRS), estimates of population characteristics, standard errors, confidence intervals, sampling for proportions, randomization theory results for SRS, a model for SRS, situations where an SRS is appropriate.

Ratio Estimation:
Use of auxiliary data in ratio estimation, regression estimation, regression models, design implications of regression models, comparison of ratio & regression estimation method.

Systematic Sampling:
Estimation of population characteristics, systematic sampling in some special population.

Stratified Sampling:
Definition and basic ideas, theory of Stratified Sampling, allocation of observations to strata, a model for stratified sampling, post-stratification, stratified vs quota sampling.

Cluster Sampling with equal probabilities:
Notation for cluster sampling, one-stage cluster sampling, designing a cluster sample, models for cluster sampling. Comparison with SRS and systematic sampling. Determination of optimum cluster size. Stratified cluster sampling.

Sampling with unequal probabilities with replacement:
One-stage sampling with replacement, two-stage sampling with replacement, examples, randomization theory results and proofs, models for unequal probability sampling with replacement.

Sample Size:
Concept of sample size estimation. Determination of sample size for estimating mean and proportions. Design effect, sample size for comparisons of two means or proportions.