1. Sampling & Sampling Designs

2. Concepts Population Sampling frame Sample Sampling unit Sampling Census

3. Steps in Sample Design Type of the universe Sample unit Source list Size of the sample ( Prob: sd Rs. 30 with margin error Rs 7 estimate sample size at 90% confidence – 1.645) Parameters of interest Budgetary constraint

4. Criteria of selecting a sampling procedure Two things to be considered -Non sampling error -Sampling error

5. Reasons for systematic bias Inappropriate sampling frame Defective measuring device Non-respondents Indeterminacy principle Natural bias

6. Features of a good sample design Should be representative Small sampling error Must be viable Systematic bias can be controlled Results can be applied to the universe

7. Sampling designs Probability sampling Non-probability sampling

8. Non- Probability Sampling Purposive sampling Judgement sampling Convenience sampling Quota sampling Snow ball sampling

9. Probability Sampling Random sample Systematic sampling Stratified sampling -Eg N= 8000, N1 = 3600, N2 = 2400 and N3 = 2000 and sample size is 30 Cluster sampling Area sampling

10. Contd Multi-stage sampling Sequential sampling

11. Problems (when p is known) A consumer electronics company wants to determine the job satisfaction levels of its employees. For this, they ask a simple question, “are you satisfied with your job”? It is estimated that no more than 30% of the employees would answer yes. What should be the sample size for this company to estimate the population proportion to ensure a 95% confidence (1.96) and to be within 0.04 of the true population proportion?

12. Problem on Sample Size A manager of a department store would like to study women’s spending per year on cosmetics. He is interested in knowing the population proposition of women who purchase their cosmetics primarily from the store. If he wants to have a 90% confidence of estimating the true proposition to be within + or – 0.045. What sample size is needed. Z is 1.645