26738157 sampling-design

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sampling theory

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26738157 sampling-design

  1. 1. SAMPLING DESIGN
  2. 2. The Nature of Sampling <ul><li>The basic idea of sampling is that by </li></ul><ul><li>selecting some of the elements in </li></ul><ul><li>population, we may draw conclusions </li></ul><ul><li>about the entire population </li></ul>
  3. 3. Nature of Sampling <ul><li>A population element is the unit of study </li></ul><ul><li>The unit of study might be a person or just about anything else </li></ul>
  4. 4. Why Sample? <ul><li>Lower cost </li></ul><ul><li>Greater accuracy of results </li></ul><ul><li>Greater speed of data collection </li></ul><ul><li>Availability of Population elements. </li></ul>
  5. 5. What is Good Sample? <ul><li>How well it represents the characteristics of the population it purports to represent </li></ul><ul><li>In measurement terms, the sample must be valid. </li></ul><ul><li>Validity of a sample depends on two considerations </li></ul><ul><li>Accuracy and precision. </li></ul>
  6. 6. Accuracy <ul><li>Degree to which bias is absent from the sample. </li></ul><ul><li>Some sample elements underestimate the population values being studied and other overestimate them. </li></ul>
  7. 7. How do Bring in Accuracy? <ul><li>Under-estimation and over-estimation offset each other and gives a sample value that is generally close to the population value. </li></ul><ul><li>Offsetting requires large number of elements </li></ul>
  8. 8. Precision <ul><li>No sample will fully represent its population in all respects </li></ul><ul><li>Differences in the sample and population values occurs due to random fluctuations inherent in the sampling process. </li></ul><ul><li>This is called sampling error and reflects the influences of chance in drawing the sample members. </li></ul>
  9. 9. Sampling Error <ul><li>What is left after all known sources of systematic variance have been accounted for. </li></ul><ul><li>In theory, sampling error consists of random fluctuations only </li></ul><ul><li>Some unknown systematic variance may be included when too many or too few sample elements possess a particular characteristic. </li></ul>
  10. 10. Precision <ul><li>Measured by the standard error of estimate </li></ul><ul><li>Type of standard deviation measurement </li></ul><ul><li>The smaller the standard error of estimate, the higher is the precision </li></ul><ul><li>Samples of the same size can produce different amounts of error variance. </li></ul>
  11. 11. Classification of Sample Techniques Sampling Techniques Probability Non-Probability
  12. 12. Probability Sampling Probability Sampling Simple Random Sampling Systematic Sampling Cluster Sampling Stratified Random Sampling Proportion ate Dis Proportion ate One- Stage Two Stage Multi- Stage
  13. 13. Non-Probability Non- Probability Convenience Sampling Quota Sampling Judgment Sampling Snowball Sampling
  14. 14. Steps in Sampling Design <ul><li>What is the Relevant Population? </li></ul><ul><li>The definition of the population </li></ul><ul><li>Whether the population consists of individuals, households, families or a combination of these </li></ul>
  15. 15. What are the Parameters of Interest? <ul><li>Population parameters are summary descriptors (proportion, mean, variance) of variables of interest in the population. </li></ul><ul><li>Sample statistics are descriptors of the relevant variables computed from sample data. </li></ul><ul><li>Sample statistics are used as estimators of population parameters </li></ul>
  16. 16. What is the Sampling Frame? <ul><li>The sampling frame is closely related to the population. </li></ul><ul><li>It is the list of elements from which the sample is actually drawn. </li></ul><ul><li>Ideally, it is a complete and correct list of population members only. </li></ul>
  17. 17. What is the Type of Sample? <ul><li>Choosing a probability sampling technique has several consequences. </li></ul><ul><li>A researcher must follow appropriate procedures, so that : </li></ul>
  18. 18. What is the Type of Sample? <ul><li>Interviewers cannot modify the selections made. </li></ul><ul><li>Only those selected elements from the original sampling frame are included. </li></ul><ul><li>Substitutions are excluded except as clearly specified and controlled according to pre-determined decisions rules. </li></ul>
  19. 19. What Size Sample is Needed ? <ul><li>Some Myths </li></ul><ul><li>A sample must be large or it is not representative. </li></ul><ul><li>A sample should bear some proportional relationship to the size of the population from which it is drawn. </li></ul>
  20. 20. Some principles that influence sample size include : <ul><li>The greater the dispersion or variance within the population, the larger the sample must be to provide estimation precision. </li></ul><ul><li>The greater the desired precision of the estimate, the large the sample must be. </li></ul><ul><li>The narrower the interval range, the larger the sample must be. </li></ul>
  21. 21. Some Principles that Influence Sample Size Include : <ul><li>The higher the confidence level in the estimate, greater the sample size must be </li></ul><ul><li>If the calculated sample size exceeds 5 percent of the population, sample size may be reduced without sacrificing precision. </li></ul>
  22. 22. How Much Will it Cost? <ul><li>Almost all studies have some budgetary </li></ul><ul><li>constraint, and this may encourage a </li></ul><ul><li>researcher to use a non-probability sample </li></ul><ul><li>Probability sample surveys incur list costs for sample frames, and other costs that are not necessary when more haphazard or arbitrary methods are used. </li></ul>
  23. 23. Probability Sampling <ul><li>Based on the concept of random selection </li></ul><ul><li>A controlled procedure </li></ul><ul><li>Assures that each population element is given a known nonzero chance of selection. </li></ul>
  24. 24. Non-probability Sampling <ul><li>In contrast, is arbitrary (nonrandom) and subjective </li></ul><ul><li>Allowing interviewers to choose sample elements “at random” </li></ul>
  25. 25. Probability Sampling- Simple Random Sampling <ul><li>Each element in the target population has an equal chance or probability of being selected in the population </li></ul><ul><li>Numbers can be randomly generated by computers or picked out of a box </li></ul><ul><li>In small population random sampling is done without replacement </li></ul>
  26. 26. Requisites <ul><li>Target population size is small </li></ul><ul><li>Homogeneous sampling frame is defined </li></ul><ul><li>Not much information is available regarding the population </li></ul>
  27. 27. Advantages <ul><li>Free of classification error </li></ul><ul><li>Requires minimum advance knowledge about the population </li></ul><ul><li>Elimination of human bias </li></ul><ul><li>Non-dependency on the availability of the element </li></ul>
  28. 28. Disadvantages <ul><li>Imperative to list every item in the population prior to sampling </li></ul><ul><li>Requires constructing very large sampling frames </li></ul><ul><li>Hence requires extensive sampling calculations </li></ul><ul><li>Hence excessive costs </li></ul>
  29. 29. Systematic Sampling <ul><li>Selecting every k th from a sampling frame </li></ul><ul><li>K represents the skip interval </li></ul><ul><li>Formula </li></ul><ul><li>k = population size / </li></ul><ul><li>Sample Size </li></ul>
  30. 30. Advantages <ul><li>Used in industrial operations where equipments in the production are checked for defects </li></ul><ul><li>Questioning people in a sample survey </li></ul><ul><li>Necessary to select first element randomly and then apply k </li></ul><ul><li>Economical and less time consuming </li></ul>
  31. 31. Stratified Random Sampling <ul><li>Process of grouping members into relatively homogenous groups before sampling </li></ul><ul><li>Each element of the population must be included in a stratum </li></ul><ul><li>Strata should be exhaustive so as not to leave any element of the population </li></ul><ul><li>Then random sampling is applied within each stratum </li></ul>
  32. 32. Proportional Stratified sampling <ul><li>Number selected from each strata depends on the homogeneity and std dev of elements present in it </li></ul><ul><li>Proportional Stratified sampling – A smaller sample can be drawn out of the stratum known to have the same value </li></ul>
  33. 33. Disproportionate Stratified Sampling <ul><li>Samples can be drawn in a much higher proportion from another stratum where values are known to differ. </li></ul><ul><li>Higher number of respondents are required to minimise sampling errors because of the high variability </li></ul>
  34. 34. Advantages and Disadvantages <ul><li>Improves representativeness by reducing sampling error </li></ul><ul><li>Greater statistical efficiency over simple random sampling </li></ul><ul><li>Groups are represented when strata are combined </li></ul><ul><li>There can be errors in designating bases due to time and cost factors </li></ul>
  35. 35. Multistage Cluster Sampling <ul><li>Involves grouping the population into various clusters and then selecting a few clusters for study </li></ul><ul><li>Clusters should be homogenous in nature </li></ul><ul><li>Elements within each cluster should be heterogeneous </li></ul><ul><li>Cluster should be similar to the population </li></ul>
  36. 36. Multistage Cluster Sampling <ul><li>Suitable for studies that cover large geographic areas </li></ul><ul><li>Researcher can go for 1, 2 or multi-stage cluster sampling </li></ul><ul><li>In single stage- all elements from each cluster are studied </li></ul>
  37. 37. Two Stage <ul><li>Two stage - uses random sampling to select a few elements from each of selected clusters </li></ul><ul><li>Multi-stage - selecting a sample in 2 or more successive stages. </li></ul><ul><li>Cluster / units is selected in the first stage and further divided into clusters / units </li></ul>
  38. 38. Non- Probability Sampling

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