Sampling design
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A sample design is a plan for selecting a sample from a population that considers factors like the type of population, sampling unit, sample size, parameters of interest, and budget. There are two types of sampling designs: probability sampling, where every item has an equal chance of selection, and non-probability sampling, which does not assign selection probabilities. Probability sampling methods include systematic, stratified, multi-stage, and cluster sampling.
Sampling design
- 2. Sample Design A sample design is a definite plan for obtaining a sample from a given population. It refers to the technique or the procedure the researcher would adopt in selecting items for the sample. Steps in Sample Design Type of universe Sampling unit Source list Size of sample Parameters of interest Budgetary constraint Sampling procedure
- 3. Criteria of selecting a Sampling Procedure There are two types of costs are involved in a sampling analysis:- Cost of collecting the data Cost of an incorrect inference Two causes of incorrect inferences:- Systematic bias Sampling error Characteristics of a Good Sample Design Small sampling errors Viable in the context of funds available Systematic bias can be controlled in a better way
- 4. Types of Sample Designs Non-probability sampling Probability sampling Non-probability Sampling Non-probability sampling is that sampling is procedure which does not afford any basis for estimating the probability that each item in the population has of being included in the sample. Probability Sampling Probability sampling is also known as ‘random sampling’ or chance sampling’. Under this sampling design, every item of the universe has an equal chance of inclusion in the sample.
- 5. Probability Sampling Systematic sampling Stratified sampling Multi-stage sampling Cluster sampling Area sampling Sequential sampling Sampling with probability proportional to size