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  1. 1. Sampling
  2. 2. Sampling DesignPopulation• The whole group of people, items, objects, events etc. having some common characteristic and which is being researched on is called a population.• A single person, item etc. is called an element of the population.Sample• A sample is a part of a population.• The process of selection of one or more elements from the population is called sampling.• The elements in a sample are selected in such a way that the sample represents the population in every possible characteristic and feature.
  3. 3. Representativeness• Sample • Population_X, µ.S, σ.S2 σ2
  4. 4. Normal Curve
  5. 5. Why Sampling?Benefits of sampling• Cost effective• Saves time• Can be more accurate• In a situation where the research results in destruction, deformation, mutilation or contamination of the elements sampled sampling is essentialTotal study• in this case sampling is not required• this is done in a case where the population is too small• this is done when the research so intends that all the elements in the population must be included in the study
  6. 6. Sampling design• Sampling design is created keeping in view the purpose and focus of the research.• Sampling design consists of five sequential and interrelated steps.• Each step has relevance to all aspects of the research.• A sample selected using a biased or technically wrong method will lead to irrelevant information, which in turn will lead to incorrect or distorted conclusions of the research.• Steps in a sampling design• 1. Define the target population• 2. Specify the sampling frame• 3. Decide on a sampling procedure• 4. Determine a method of determining the sample size• 5. Determine the optimum sample size
  7. 7. Define the target population• The target population is that entire group of items or individuals or cases from where the information is to be collected.• This may also be called the ‘subject’ of research.• In an empirical study, the target population consists of physical objects like people or items or events.• In a case study it contains just one object or event.• In fundamental research it can be infinite, where it is required to know something that is true for every object or event of the given type in the universe.• A total study gives a complete and accurate description of the population, but it is possible only if the population is not too large and if all the elements in the population are available for study.
  8. 8. Specify the sampling frame• A sampling frame is a list of all the elements in the target population• Telephone directory, mailing list, register maintained at office are examples of sampling frames
  9. 9. Sampling ProcedureProbabilistic methods Non-probabilistic methods(1) Simple random sampling (1) Convenience sampling(2) Stratified random sampling (2) Judgment sampling (purposive)(3) Cluster sampling (3) Quota sampling (proportional)(4) Systematic sampling (4) Snowball sampling(5) Multi-stage sampling
  10. 10. Probabilistic Methods Simple Random Sampling Every element in the population has an equal probability of being selected in the samplePros Cons• 1. Comparatively easy method • If the population has• 2. Softwares for generating subgroups that may be of random numbers are available research interest, they may not and simple to use. be adequately and proportionately represented by the sample. • 2. If the population size is too large, allocating numbers to its elements and
  11. 11. Stratified random sampling The population is divided into strata. A stratum is a subset of the population that share at least one common characteristic. Every element in a stratum has an equal probability of being selected in the sample.• This is a more representative • Calculation more complicated form of the population. that simple random sampling.• Gives good results when • Population and sample size for studies involve subgroups as each strata should be known. gender, age, income group, education level, socio- economic category, religion, geographical location, etc.
  12. 12. Cluster sampling divides the population into groups or clusters. Anumber of clusters are selected randomly to represent the population, and then all units within selected clusters areincluded in the sample. No units from non-selected clusters are included in the sample. This differs from stratifiedsampling, where some units are selected from each group.• 1. Clustering helps in • Usually the general reducing data collection assumption is that the time and cost. clusters are alike – if this• 2. In case it is impossible is violated the sample will and impractical to get the be biased. list of the entire • 2. It is better to increase population, this method is the number of clusters useful. and thereby reduce the cluster size.
  13. 13. Systematic sampling Every ith numbered element is selected. That is there is uniform gap between selected elements .• Very convenient to • If there is an existing use. recurring pattern in• Only the 1st element the population, this needs to be selected may produce a bias in randomly. the sample.
  14. 14. Multi-stage sampling similar to cluster sampling, but involves selecting a sample within each chosen cluster.• 1. This does not require a • Same as in cluster complete list of members sampling in the target population, which greatly reduces sample preparation cost.• 2. The list of members is required only for those clusters used in the final stage.
  15. 15. Non Probabilistic MethodsConvenient sampling :Elements in the population who / that are readily available is included in the sample.• Cost-effective, time- • Since the sample is saving practical so chosen, it is method unlikely to be representative of the population.
  16. 16. Judgement sampling (Purposive sampling) The researcher selects the sample based on judgment.• 1. Useful in • 1. The researcher exploratory research. should be fully aware• 2. Makes certain that of the purpose and the widest variety of objective of the elements is chosen in research. the sample. • 2. The bias of the researcher may affect the representativeness of the sample.
  17. 17. Quota samplingThe population is divided into groups. Then convenience orjudgment sampling is used to select the required number of subjects from each group.• Useful when prior • Records relating to knowledge of groups proportions of groups exist. must be complete, correct and up-to-da
  18. 18. Snowball sampling This method is used when the desired sample characteristic is difficult to find or cost prohibitiveSnowball sampling relies on references from initial subjects to generate additional subjects .• 1. This unique technique • Data collected from can reduce research ‘snowballed’ sample may costs. not be a measure of what• 2. This is a good method is to be actually collected. for such populations that are not well defined or properly listed
  19. 19. Determine a method of determining the sample sizeSample Size is the number of elements in the sample. The concern is to decide on the size of a sample, so that the sample• will not lose its usability,• will give us data reliable enough about the population,• will be able to represent the population in its truest form.Five interrelated factors that play a role in decision about the sample size:• heterogeneity of the population• required precision level• sampling procedure• resources available• time constraints• number of major sub-divisions in the research, each needing separate sampling, and sample sizes to be determined for each of these samplings.
  20. 20. Determine the optimum sample sizeThe size of the sample may be determined in two ways – subjective and objective.Subjective• the researcher decides subjectively on the size according to his understanding of the research,• past experience with similar type of research• time and cost constraints• availability of elements to be included in the sample• this method has no consideration for statistical theoryObjective• statistically decided sample size,• predetermined limits of sampling error and confidence level are required,• the researcher’s subjectivity has no role to play in this method.
  21. 21. The following table gives optimum sample sizes for ± 5% sampling error with 95% confidence level.Population Size Sample Size Percentage of the population size 10 10 100 20 19 95 50 44 88 100 80 80 250 152 61 500 217 43 1,000 278 28 2,500 333 13 5,000 350 7 10,000 370 4
  22. 22. ExerciseA medical inspector desires to estimate the overall average monthly occupancy rates of the cancer wards in 80 different hospitals that are evenly located in the different suburbs of Delhi NCR
  23. 23. ExerciseIn an article in the wall street journal titled “Kellogg to study work of salaried staff, setting stage for possible job cutbacks”, it was started that the kellogg’s earnings remained under heavy competitive pressure and its cereal market continued to slip. It was also stated that kellogg was seeking to regain its lost momentum through the first three strategies listed below, to which last two are added.1. Increasing production efficiencies2. Developing new products3. Increasing product promotion through advertising effectiveness4. Tapping creative ideas from organizational members at different levels5. Assessing perception of organizational health and vitalityDiscuss sampling design for each of the five strategies. Give the reasons for ur choice.
  24. 24. ExerciseCare for elderly relatives is a concern for many working parents. If you were to do a scientific study of this , what kind of sampling design you would use? Discuss your response with reasons for the choice of population and sample
  25. 25. Any doubts? Thank you