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SAMPLING TECHNIQUES

SAMPLING TECHNIQUES. SAMPLING. Procedure by which some members of a given population are selected as representatives of the entire population. UNIVERSE. the larger group from which individuals are selected to participate in a study SAMPLE

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SAMPLING TECHNIQUES

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  1. SAMPLING TECHNIQUES

  2. SAMPLING Procedure by which some members of a given population are selected as representatives of the entire population.

  3. UNIVERSE the larger group from which individuals are selected to participate in a study SAMPLE the representatives selected for a study whose characteristics exemplify the larger group from which they were selected

  4. PURPOSE OF SAMPLING To gather data about the population in order to make an inference that can be generalized to the population POPULATION INFERENCE SAMPLE

  5. Process Of Sampling

  6. Define the Population Develop Sampling Frame Select a Sampling Method Determine the Sample Size Execute the Sampling Process The Sampling Process

  7. Sampling and representativeness Sample Sampling Population Target Population Target Population  Sampling Population  Sample

  8. Sampling Techniques Fixed Vs Sequential sampling Attributes Vs Variables Sampling Probability Vs Non-probability sampling

  9. PROBABILITY SAMPLING • Every element in the target population or universe [sampling frame] has equal probability of being chosen in the sample for the survey being conducted. • Scientific, operationally convenient and simple in theory. • Results may be generalized. NON-PROBABILITY SAMPLING • Every element in the universe [sampling frame] does not have equal probability of being chosen in the sample. • Operationally convenient and simple in theory. • Results may not be generalized.

  10. CLASSIFICATION OF SAMPLING TECHNIQUES

  11. SIMPLE RANDOM SAMPLING Simple random sampling is a method of probability sampling in which every unit has an equal non zero chance of being selected for the sample. Methods of selecting random sample: • Lottery Method • Tables of Random Numbers

  12. STRATIFIED RANDOM SAMPLING Stratified random sampling is a method of probability sampling in which the population is divided into different subgroups and samples are selected from each of them. Steps:- • All units of population are divided into different stratas in accordance with their characteristics. • Using random sampling, sample items are selected from each stratum.

  13. Systematic Random Sampling or Quasi-Random Sampling Systematic random sampling is a method of probability sampling in which the defined target population is ordered and the 1st unit of sample is selected at random and rest of the sample is selected according to position using a skip interval (every Kth item) K = N n Where, K = Sampling/ Skip interval N = Universe/ Population Size n = Sample Size

  14. MULTISTAGE RANDOM SAMPLING • Used in large scale investigations • First stage- preparation of large sized sampling units • Randomly selecting a certain number • Second stage- Another list prepared from them • Sub-samples drawn by random sampling

  15. CLUSTER SAMPLING The process of randomly selecting intact groups, not individuals, within the defined population sharing similar characteristics Steps :- • Defined population is divided into number of mutually exclusive and collectively exhaustive subgroups or clusters • Select an independent simple random sample of clusters.

  16. Area Sampling • One special type of cluster sampling is called area sampling, where pieces of geographical areas such as districts, housing blocks or townships are selected. • Area sampling could be one-stage, two-stage, or multi-stage. • Generally used by Govt. agencies and agricultural statistics.

  17. Non-probability Sampling Methods

  18. Convenience sampling the process of including whoever happens to be available at the time…called “accidental” or “haphazard” sampling.

  19. Purposive sampling the process whereby the researcher selects a sample based on experience or knowledge of the group to be sampled…called “judgment” sampling

  20. Quota sampling the process whereby a researcher gathers data from individuals possessing identified characteristics and quotas

  21. Other Non-probability Sampling Methods Intensity sampling: selecting participants who permit study of different levels of the research topic Homogeneous sampling: selecting participants who are very similar in experience, perspective, or outlook Criterion sampling: selecting all cases that meet some pre-defined characteristic Snowball sampling relies upon respondent referrals of others with like characteristics

  22. Factors to Consider in Sample Design Research objectives Degree of accuracy Resources Time frame Knowledge of target population Research scope Statistical analysis needs

  23. THANK YOU

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