Sampling Methods
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This document discusses sampling methods used in statistics. It defines sampling as making inferences about a whole population by examining a subset of selected units. The main purposes of sampling are to provide statistical information about a population more efficiently and accurately than a complete census. Probability sampling methods like simple random sampling, stratified random sampling, and systematic random sampling aim to give all population units an equal chance of selection. Non-probability methods like convenience sampling and judgmental sampling do not use random selection. The document also outlines different sampling types and the steps involved in the sampling process.
Sampling Methods
- 2. Meaning of sampling The process of testing some data based on a small sample is called sampling. Eg: From a cup of tea, a tea-taster determines the quality of the brand of tea.
- 4. Definition of sampling Sampling is the process by which inference is made to the whole by examining a part.
- 5. THE PURPOSE OF SAMPLING The purpose of sampling is to provide various types of statistical information of a qualitative or quantitative nature about the whole by examining a few selected units.
- 6. ADVANTAGES OF SAMPLING • Greater economy • Greater scope • It takes less time
- 7. LIMITATIONS OF SAMPLING •Errors due to sampling may be high for small administrative areas. •Sampling may not be feasible for problems that require very high accuracy.
- 8. TYPES OF SAMPLING 1. Probability sampling: • Simple Random Sampling • Stratified Random Sampling • Systematic Random Sampling • Cluster Or Multistage Sampling
- 9. 2. Non-probability sampling: • Convenience/ opportunity sampling. • Purposive/ judgemental sampling • Quota sampling • Snowball sampling
- 10. • SIMPLE RANDOM SAMPLING: In which each and every item has an equal chance of selection.
- 11. •SYSTEMATIC RANDOM SAMPLING: In which one or two items are selected randomly, but other items are selected by adding the average sampling interval to the item selected randomly.
- 12. •STRATIFIED RANDOM SAMPLING: It is the process of segregating the population into groups and select by systematic sampling method from each strata or cell.
- 13. • CLUSTER SAMPLING: The population is divided into clusters like families and then simple random sampliing is applied.
- 14. • CONVENIENCE SAMPLING: The process of including whoever happens to be available at the time. It also called “accidental” or “haphazard” sampling
- 15. • JUDGEMENTAL SAMPLING: The process whereby the researcher selects a sample based on experience or knowledge of the group to be sampled. It is also called purposive sampling
- 16. • QUOTA SAMPLING: The process whereby a researcher gathers data from individuals possessing identified characteristics and quotas. For example, an interviewer may be told to sample 200 females and 300 males between the age of 45 and 60.
- 17. • SNOWBALL SAMPLING: The sampling procedure in which the initial respondents are chosen by probability or non- probability methods, and then additional respondents are obtained by information provided by the initial respondents
- 18. STEPS IN SAMPLING PROCESS STEP 1: Define the target population STEP 2: Identify the sampling frame STEP 3: Specifying the sampling unit. STEP 4: Selection of the sampling method. STEP 5: Determination of sample size. STEP 6: Specifying the sampling plan. STEP 7: Selecting the sample.
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