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  3. 3. SAMPLE•It is a Unit that selected from population•Representers of the population•Purpose to draw the inference
  4. 4. Very difficult to study each and every unit of thepopulation when population unit are heterogeneousWHY SAMPLE ?Time ConstraintsFinance
  5. 5. It is very easy and convenient to draw the sample fromhomogenous population
  6. 6. The population having significant variations (Heterogeneous),observation of multiple individual needed to find all possiblecharacteristics that may exist
  7. 7. PopulationThe entire group of people of interest from whom theresearcher needs to obtain informationElement (sampling unit)One unit from a populationSamplingThe selection of a subset of the population through varioussampling techniquesSampling FrameListing of population from which a sample is chosen. Thesampling frame for any probability sample is a complete list ofall the cases in the population from which your sample will bedrown
  8. 8. ParameterThe variable of interestStatisticThe information obtained from the sampleabout the parameter
  9. 9. Population Vs. SamplePopulation ofInterestSamplePopulation SampleParameter StatisticWe measure the sample using statistics in order to drawinferences about the population and its parameters.
  10. 10. UniverseCensusSample PopulationSample FrameElements
  11. 11. Characteristics of Good SamplesRepresentativeAccessibleLow cost
  12. 12. Process by which the sample are taken frompopulation to obtain the informationSampling is the process of selecting observations (asample) to provide an adequate description andinferences of the populationSAMPLING
  13. 13. PopulationSampleSamplingFrameSampling ProcessWhat youwant to talkaboutWhat youactuallyobserve inthe dataInference
  14. 14. Steps in Sampling ProcessDefine the populationIdentify the sampling frameSelect a sampling design orprocedureDetermine the sample sizeDraw the sample
  15. 15. Sampling Design ProcessDefine PopulationDetermine Sampling FrameDetermine Sampling ProcedureProbability SamplingSimple Random SamplingStratified SamplingCluster SamplingSystematic SamplingMultistage SamplingNon-Probability SamplingConvenientJudgmentalQuotaSnow ball SamplingDetermine AppropriateSample SizeExecute SamplingDesign
  16. 16. Classification of SamplingMethodsSamplingMethodsProbabilitySamplesSimpleRandomClusterSystematic StratifiedNon-probabilityQuotaJudgmentConvenience SnowballMultistage
  17. 17. Probability SamplingEach and every unit of the population has theequal chance for selection as a sampling unitAlso called formal sampling or random samplingProbability samples are more accurateProbability samples allow us to estimate theaccuracy of the sample
  18. 18. Types of Probability SamplingSimple Random SamplingStratified SamplingCluster SamplingSystematic SamplingMultistage Sampling
  19. 19. Simple Random Sampling The purest form of probability sampling Assures each element in the population has anequal chance of being included in the sample Random number generators
  20. 20. Simple random sampling
  21. 21. Types of Simple Random SampleWith replacementWithout replacement
  22. 22. With replacementThe unit once selected has the chancefor again selectionWithout replacementThe unit once selected can not beselected again
  23. 23. Methods of SRS Tippet methodLottery MethodRandom Table
  24. 24. Random numbers of table6 8 4 2 5 7 9 5 4 1 2 5 6 3 2 14 05 8 2 0 3 2 1 5 4 7 8 5 9 6 2 02 43 6 2 3 3 3 2 5 4 7 8 9 1 2 0 32 59 8 5 2 6 3 0 1 7 4 2 4 5 0 3 6
  25. 25. Advantages of SRS Minimal knowledge of populationneeded External validity high; internalvalidity high; statistical estimationof error Easy to analyze data
  26. 26. Disadvantage High cost; low frequency of use Requires sampling frame Does not use researchers’ expertise Larger risk of random error thanstratified
  27. 27. Stratified Random SamplingPopulation is divided into two or more groupscalled strata, according to some criterion, such asgeographic location, grade level, age, or income,and subsamples are randomly selected from eachstrata.Elements within each strata are homogeneous,but are heterogeneous across strata
  28. 28. Stratified Random Sampling
  29. 29. Types of Stratified Random SamplingProportionate Stratified Random SamplingEqual proportion of sample unit are selected from eachstrataDisproportionate Stratified Random SamplingAlso called as equal allocation technique and sample unitdecided according to analytical consideration
  30. 30. Advantage Assures representation of all groups insample population needed Characteristics of each stratum can beestimated and comparisons made Reduces variability from systematic
  31. 31. Disadvantage Requires accurate information onproportions of each stratum Stratified lists costly to prepare
  32. 32. The population is divided into subgroups(clusters) like families. A simple random sampleis taken of the subgroups and then all members ofthe cluster selected are surveyed.Cluster Sampling
  33. 33. Cluster samplingSection 4Section 5Section 3Section 2Section 1
  34. 34. Advantage Low cost/high frequency of use Requires list of all clusters, but only of individuals withinchosen clusters Can estimate characteristics of both cluster andpopulation For multistage, has strengths of used methods Researchers lack a good sampling frame for a dispersedpopulation
  35. 35. DisadvantageThe cost to reach an element to sample is veryhighUsually less expensive than SRS but not asaccurateEach stage in cluster sampling introducessampling error—the more stages there are, themore error there tends to be
  36. 36. Systematic Random SamplingOrder all units in the sampling frame basedon some variable and then every nth numberon the list is selectedGaps between elements are equal andConstant There is periodicity.N= Sampling Interval
  37. 37. Systematic Random Sampling
  38. 38. Advantage Moderate cost; moderate usage External validity high; internal validityhigh; statistical estimation of error Simple to draw sample; easy to verify
  39. 39. DisadvantagePeriodic orderingRequires samplingframe
  40. 40. Multistage sampling refers to sampling planswhere the sampling is carried out in stagesusing smaller and smaller sampling units at eachstage.Not all Secondary Units Sampled normally usedto overcome problems associated with ageographically dispersed populationMultistage Random Sampling
  41. 41. 12345678910PrimaryClusters123456789101112131415SecondaryClusters Simple Random Sampling within Secondary Clusters
  42. 42. Multistage Random SamplingSelect all schools; then sample withinschoolsSample schools; then measure allstudentsSample schools; then sample students
  43. 43. The probability of each case being selected fromthe total population is not knownUnits of the sample are chosen on the basis ofpersonal judgment or convenienceThere are NO statistical techniques for measuringrandom sampling error in a non-probabilitysample. Therefore, generalizability is neverstatistically appropriate.Non Probability Sampling
  44. 44. Non Probability Sampling Involves non random methods in selection ofsampleAll have not equal chance of being selectedSelection depend upon situationConsiderably less expensiveConvenientSample chosen in many ways
  45. 45. Types of Non probability Sampling Purposive Sampling Quota sampling (larger populations)Snowball samplingSelf-selection samplingConvenience sampling
  46. 46. Purposive SamplingAlso called judgment SamplingThe sampling procedure in which an experiencedresearch selects the sample based on someappropriate characteristic of samplemembers… to serve a purposeWhen taking sample reject, people who do notfit for a particular profileStart with a purpose in mind
  47. 47. Sample are chosen well based on thesome criteriaThere is a assurance of QualityresponseMeet the specific objectiveAdvantage
  48. 48. DemeritBias selection of sample mayoccur Time consuming process
  49. 49. Quota SamplingThe population is divided into cells on the basisof relevant control characteristics.A quota of sample units is established for eachcellA convenience sample is drawn for each celluntil the quota is metIt is entirely non random and it is normallyused for interview surveys
  50. 50. Advantage Used when research budget limited Very extensively used/understood No need for list of population elements Introduces some elements of stratificationDemerit Variability and bias cannot be measuredor controlled Time Consuming Projecting data beyond sample notjustified
  51. 51. Snowball SamplingThe research starts with a key person andintroduce the next one to become a chainMake contact with one or two cases in thepopulationAsk these cases to identify further cases. Stop when either no new cases are given or thesample is as large as manageable
  52. 52. AdvantageDemerit low cost Useful in specific circumstances Useful for locating rare populations Bias because sampling units not independent Projecting data beyond sample not justified
  53. 53. Self selection SamplingIt occurs when you allow each case usuallyindividuals, to identify their desire to take partin the research you thereforePublicize your need for cases, either byadvertising through appropriate media or byasking them to take partCollect data from those who respond
  54. 54. AdvantageDemerit More accurate Useful in specific circumstances to serve thepurpose More costly due to Advertizing Mass are left
  55. 55. Called as Accidental / IncidentalSamplingSelecting haphazardly those cases thatare easiest to obtainSample most available are chosenIt is done at the “convenience” of theresearcherConvenience Sampling
  56. 56. Merit Very low cost Extensively used/understood No need for list of population elementsDemerit Variability and bias cannot be measured orcontrolled Projecting data beyond sample notjustified
  57. 57. Sampling ErrorSampling error refers to differencesbetween the sample and the populationthat exist only because of the observationsthat happened to be selected for thesampleIncreasing the sample size will reduce thistype of error
  58. 58. Types of Sampling ErrorSample ErrorsNon Sample Errors
  59. 59. Sample ErrorsError caused by the act of taking a sampleThey cause sample results to be different from theresults of censusDifferences between the sample and the populationthat exist only because of the observations thathappened to be selected for the sampleStatistical Errors are sample errorWe have no control over
  60. 60. Non Sample ErrorsNon Response ErrorResponse ErrorNot Control by Sample Size
  61. 61. Non Response ErrorA non-response error occurs whenunits selected as part of the samplingprocedure do not respond in wholeor in part
  62. 62. Response ErrorsRespondent error (e.g., lying, forgetting, etc.)Interviewer biasRecording errorsPoorly designed questionnairesMeasurement errorA response or data error is any systematic biasthat occurs during data collection, analysis orinterpretation
  63. 63. Respondent error respondent gives an incorrect answer, e.g. due to prestigeor competence implications, or due to sensitivity or socialundesirability of question respondent misunderstands the requirements lack of motivation to give an accurate answer “lazy” respondent gives an “average” answer question requires memory/recall proxy respondents are used, i.e. taking answers fromsomeone other than the respondent
  64. 64. Interviewer bias Different interviewers administer a survey in differentways Differences occur in reactions of respondents todifferent interviewers, e.g. to interviewers of theirown sex or own ethnic group Inadequate training of interviewers Inadequate attention to the selection of interviewers There is too high a workload for the interviewer
  65. 65. Measurement Error The question is unclear, ambiguous or difficult toanswer The list of possible answers suggested in the recordinginstrument is incomplete Requested information assumes a frameworkunfamiliar to the respondent The definitions used by the survey are different fromthose used by the respondent (e.g. how many part-timeemployees do you have? See next slide for an example)
  66. 66. Key Points on ErrorsNon-sampling errors are inevitable in production ofnational statistics. Important that:- At planning stage, all potential non-sampling errors arelisted and steps taken to minimise them are considered. If data are collected from other sources, questionprocedures adopted for data collection, and dataverification at each step of the data chain. Critically view the data collected and attempt to resolvequeries immediately they arise. Document sources of non-sampling errors so that resultspresented can be interpreted meaningfully.