Sampling & Sampling Designs

Concepts Population Sampling frame Sample Sampling unit Sampling Census

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

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

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

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

Sampling designs Probability sampling Non-probability sampling

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

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

Contd Multi-stage sampling Sequential sampling

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?

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 – What sample size is needed. Z is 1.645