Sampling and sampling process

Sampling is a process of selecting representative units from an entire population of a study.
Two Types

Probability Sampling Techniques
Non- Probability sampling techniques

1. ravsa
Sampling
By
Mr. Ravi Rai Dangi
Assistant Professor
Fellowship in Neonatal Nursing
MSc. Child Health Nursing

2. ravsa
INTRODUCTION
Samplingisaprocessofselectingrepresentativeunitsfrom
anentirepopulationofastudy.
Sampleisnotalwayspossibletostudyanentirepopulation;
therefore,theresearcherdrawsarepresentativepartofa
populationthroughsamplingprocess.
Inotherwords,samplingistheselectionofsomepartofan
aggregateorawholeonthebasisofwhichjudgmentsor
inferencesabouttheaggregateormassismade.

3. ravsa
Population:
Populationistheaggregationofalltheunits
inwhicharesearcherisinterested.Inotherwords,
populationisthesetofpeopleorentiretowhichthe
resultsofaresearcharetobegeneralized.For
example,aresearcherneedstostudytheproblems
facedbypostgraduatenursesofIndia;inthisthe
‘population’willbeallthepostgraduatenurseswho
areIndiancitizen.

4. ravsa
TargetPopulation:
Atargetpopulationconsistofthetotalnumberof
peopleorobjectswhicharemeetingthedesignatedset
ofcriteria.Inotherwords,itistheaggregateofallthe
caseswithacertainphenomenonaboutwhichthe
researcherwouldliketomakeageneralization.
Accessiblepopulation:
Itistheaggregateofcasesthatconformto
designatedcriteria&arealsoaccessibleassubjectsfora
study.

5. ravsa

6. ravsa
Sample:
Samplemaybedefinedasrepresentativeunit
ofatargetpopulation,whichistobeworkedupon
byresearchersduringtheirstudy.Inotherwords,
sampleconsistsofasubsetofunitswhichcomprise
thepopulationselectedbyinvestigatorsor
researcherstoparticipatesintheirresearchproject

7. ravsa
Samplingerror:
Theremaybefluctuationinthevaluesofthe
statisticsofcharacteristicsfromonesampletoanother,
oreventhosedrawnfromthesamepopulation.
Samplingbias:
Distortionthatariseswhenasampleisnot
representativeofthepopulationfromwhichitwas
drawn.

8. ravsa
PURPOSES OF SAMPLING
Economical:Inmostcases,itisnotpossible&
economicalforresearcherstostudyanentire
population.Withthehelpofsampling,the
researchercansavelotsoftime,money,&resources
tostudyaphenomenon.
Improvedqualityofdata:Itisaprovenfactthat
whenapersonhandleslessamounttheworkof
fewernumberofpeople,thenitiseasiertoensure
thequalityoftheoutcome.

9. ravsa
Quickstudyresults:Studyinganentire
populationitselfwilltakealotoftime,&generating
researchresultsofalargemasswillbealmost
impossibleasmostresearchstudieshavetimelimits
Precisionandaccuracyofdata:Conductinga
studyonentirepopulationgiveryouavoluminous
dataandmaintaintheprecisionofthatdatabecome
morecomplextask.Sosamplingisnecessary.

10. ravsa
Characteristics of Good Sample
Representative
Freefrombiasanderrors
Nosubstitutionandincompleteness
Appropriatesamplesize

11. ravsa
Sampling Process
Sampling process of selecting a part of the assigned
population to represent the entire population.

12. ravsa
Identifying and defining the target population
Describing the accessible population &
ensuring sampling frame
Specifying the sampling unit
Specifying sampling selection methods
Determining the sample size
Specifying the sampling plan
Selecting a desired sample

13. ravsa
FACTORS INFLUENCING SAMPLINGPROCESS

14. ravsa
Sampling Techniques

15. ravsa
Two Types
•Probability Sampling Techniques
•Non-Probability sampling techniques

16. ravsa

17. ravsa
PROBABILITY SAMPLING TECHNIQUE
Itisbasedonthetheoryofprobability.
Itinvolverandomselectionoftheelements/members
ofthepopulation.
Inthis,everysubjectinapopulationhasequalchance
tobeselectedsamplingforastudy.
Inprobabilitysamplingtechniques,thechancesof
systematicbiasarerelativelylessbecausesubjectsare
randomlyselected.

18. ravsa
Features Of The Probability Sampling
Itisatechniquewhereinthesamplearegatheredina
processthatgivenalltheindividualsinthe
populationequalchancesofbeingselected.
Inthissamplingtechnique,theresearchermust
guaranteethateveryindividualhasanequal
opportunityforselection.

19. ravsa
Theadvantageofusingarandomsampleisthe
absenceofbothsystematic&samplingbias.
Theeffectofthisisaminimalorabsentsystematic
bias,whichisadifferencebetweentheresultsfrom
thesample&thosefromthepopulation.

20. ravsa
Simple random sampling
Thisisthemostpure&basicprobabilitysampling
design.
Inthistypeofsamplingdesign,everymemberof
populationhasanequalchanceofbeingselectedas
subject.
Theentireprocessofsamplingisdoneinasingle
step,witheachsubjectselectedindependentlyofthe
othermembersofthepopulation

21. ravsa
Thereisneedoftwoessentialprerequisitesto
implementthesimplerandomtechnique:population
mustbehomogeneous&researchermusthavelistof
theelements/membersoftheaccessiblepopulation

22. ravsa
Thefirststepofthesimplerandomsampling
techniqueistoidentifytheaccessiblepopulation&
preparealistofalltheelements/membersofthe
population.
Thelistofthesubjectsinpopulationiscalledas
samplingframe&sampledrawnfromsamplingframe
byusingfollowingmethods:

23. ravsa
The lottery method
Itismostprimitive&mechanicalmethod.
Eachmemberofthepopulationisassignedaunique
number.
Eachnumberisplacedinabowelorhat&mixed
thoroughly.

24. ravsa
The use of table of random numbers
Thisismostcommonly&accuratelyusedmethodin
simplerandomsampling.
Randomtablepresentseveralnumbersinrows&
columns.
Researcherinitiallyprepareanumberedlistofthe
membersofthepopulation,&thenwithablindfold
choosesanumberfromtherandomtable.

25. ravsa
Thesameprocedureiscontinueduntilthedesired
numberofthesubjectisachieved.
Ifrepeatedlysimilarnumbersareencountered,
they22areignored&nextnumbersareconsidered
untildesirednumberofsubjectareachieved.

26. ravsa

27. ravsa
The use of computer
Nowadaysrandomtablesmaybegeneratedfrom
thecomputer,&subjectsmaybeselectedas
describedintheuseofrandomtable.
Forpopulationswithasmallnumberofmembers,
itisadvisabletousethefirstmethod,butifthe
populationhasmanymembers,acomputer-aided
randomselectionispreferred.

28. ravsa
Merits
Everymemberhaveequalopportunitytoselect
Itrequiresminimumknowledgeaboutthe
populationinadvances.
Unbiased
Sampleerrorcanbecomputed

29. ravsa
Demerits
Requiresallmemberslist
Expensiveandtimeconsumingprocess

30. ravsa
Stratified Random Sampling
Thismethodisusedforheterogeneouspopulation.
Itisaprobabilitysamplingtechniquewhereinthe
researcherdividestheentirepopulationinto
differenthomogeneoussubgroupsorstrata,&then
randomlyselectsthefinalsubjectsproportionally
fromthedifferentstrata.

31. ravsa
Thestrataaredividedaccordingselectedtraitsof
thepopulationsuchasage,gender,religion,socio-
economicstatus,diagnosis,education,geographical
region,typeofinstitution,typeofcare,typeof
registerednurses,nursingarea.

32. ravsa

33. ravsa
Merits
Itensurestherepresentationofallgroup.
Toobservetheexistedfactwithtwoormoregroups.
Higherstatisticalprecision.
Savetime,moneyandefforts.

34. ravsa
Demerits
Itrequiresaccurateinformationontheportionof
populationineachstream
Largepopulationneeded
Possibilityoffaultyclassification

35. ravsa
Systematic Random Sampling
Itcanbelikenedtoanarithmeticprogression,
whereinthedifferencebetweenanytwoconsecutive
numbersisthesame.
ItinvolvestheselectionofeveryK
th
casefromlistof
group,suchasevery10
th
persononapatientlistor
every100thpersonfromaphonedirectory.
Systematicsamplingissometimesusedtosample
everyK
th
personenteringabookstore,orpassing
downthestreetorleavingahospital&soforth

36. ravsa
Systematicsamplingcanbeappliedsothatan
essentiallyrandomsampleisdrawn.
Ifwehadalistofsubjectsorsamplingframe,the
followingprocedurecouldbeadopted.
Thedesiredsamplesizeisestablishedatsomenumber
(n)&thesizeofpopulationmustknoworestimated
(N).

37. ravsa
Forexample,aresearcherwantstochooseabout100
subjectsfromatotaltargetpopulationof500people.
Therefore,
500/100=5.
Therefore,every5thpersonwillbeselected.

38. ravsa

39. ravsa
Merits
Convenientandsimpletocarryout.
Distributedofsampleisspreadevenlyovertheentire
population.
Lesstimeconsumingandcosteffectivealso.
Statisticallymoresignificant.

40. ravsa
Demerits
Subjectisnotrandomlyselectedsoitbecomenon
randomsamplingtechniques.
Sometimesthismayresultinbiasness

41. ravsa
Cluster or multistage Sampling
Itisdonewhensimplerandomsamplingisalmost
impossiblebecauseofthesizeofthepopulation.
Clustersamplingmeansrandomselectionofsampling
unitconsistingofpopulationelements.
Thenfromeachselectedsamplingunit,asampleof
populationelementsisdrawnbyeithersimplerandom
selectionorstratifiedrandomsampling.

42. ravsa
Thismethodisusedincaseswherethepopulation
elementsarescatteredoverawidearea,&itis
impossibletoobtainalistofalltheelements.
Theimportantthingtorememberaboutthis
samplingtechniqueistogivealltheclustersequal
chancesofbeingselected.

43. ravsa

44. ravsa
Merits
Cheap,quickandeasyforlargepopulation
Investigatorcanuseexistingdivisionlikedistrict,
villagesortowns.
Sameclusterscanbeusedforfurtherstudies.

45. ravsa
Demerits
Least representative
Some time same characteristics can present in two
clusters
Possibility of high sample error
If homogenous then not possible

46. ravsa
Sequential Sampling
Itisslightlydifferentfromothers.
Herethesamplesizeisnofixed.
Theresearcherinitiallyselectthesmallsampleand
triesouttomakeinferences;ifnotabletodrawthe
resultresearchercanaddmoresamples.

47. ravsa

48. ravsa
Merits
Bestpossiblesmallestrepresentativesample
Helpsinultimatelyfindingtheinferenceinthestudy.
Demerits
Onepointoftimestudycannonbedone
Requiresrepeatedentries

49. ravsa
Nonprobability Sampling Technique
Nonprobabilitysamplingisthetechniqueswherein
thesamplesaregatheredinaprocessthatdoesnot
givealltheindividualinthepopulationequalchance
ofbeingselectedinthesample.

50. ravsa
Features of the nonprobability sampling
Doesnotgiveequalchancetoselecteachsample.
Savestime,moneyandworkforce
Sampleswillbeselectedbypurposeorpersonal
judgement.
Ifpopulationisunknownthenresearchcannotbe
usedingeneralizedforentirepopulation.

51. ravsa
Uses of non probability sampling
Thistypeofsamplingcanbeusedwhen
demonstratingthataparticulartraitexistsinthe
population.
Itcanalsobeusedwhenresearcheraimstodoa
qualitative,pilot,orexploratorystudy.
Itcanbeusedwhenrandomizationisnotpossiblelike
whenthepopulationisalmostlimitless.

52. ravsa
Itcanbeusedwhentheresearchdoesnotaimto
generateresultsthatwillbeusedtocreate
generalizations.
Itisalsousefulwhentheresearcherhaslimited
budget,time,&workforce.
Thistechniquecanalsobeusedinaninitialstudy

53. ravsa
Purposive Sampling
Itismorecommonlyknownas‘judgmental’or
‘authoritativesampling’.
Inthistypeofsampling,subjectsarechosentobepart
ofthesamplewithaspecificpurposeinmind.
Inpurposivesampling,theresearcherbelievesthat
somesubjectsarefitforresearchcomparedtoother
individual.Thisisthereasonwhytheyarepurposively
chosenassubject.

54. ravsa

55. ravsa
Inthissamplingtechnique,samplesarechosenby
choicenotbychance,throughajudgmentmadethe
researcherbasedonhisorherknowledgeaboutthe
population
Forexample,aresearcherwantstostudythelived
experiencesofpostdisasterdepressionamongpeople
livinginearthquakeaffectedareasofGujarat.

56. ravsa
Merits
Simpletodraw
Usefulinexploratorystudies
Saveresources
Requireslessfieldwork.

57. ravsa
Demerits
Requires considerable knowledge abut the
population
Not always reliable sample
Purposiveness lead to biasness
Misrepresentation of sample may be possible

58. ravsa
Convenience Sampling
Itisprobablythemostcommonofallsampling
techniquesbecauseitisfast,inexpensive,easy,&the
subjectarereadilyavailable.
Itisanonprobabilitysamplingtechniquewhere
subjectsareselectedbecauseoftheirconvenient
accessibility&proximitytotheresearcher.

59. ravsa

60. ravsa
Thesubjectsareselectedjustbecausetheyare
easiesttorecruitforthestudy&theresearcherdid
notconsiderselectingsubjectsthatare
representativeoftheentirepopulation.
Itisalsoknownasanaccidentalsampling.
Subjectsarechosensimplybecausetheyareeasyto
recruit.

61. ravsa
Merits
Easiest,cheapestandleasttimeconsuming.
Inpilotsstudyweusethissamplingtechnique
Demerits
Sampleisnotrepresentativeofentirepopulation
Resultcannotbegeneralizedforentirepopulation.

62. ravsa
Consecutive Sampling
Itisverysimilartoconveniencesamplingexceptthatit
seekstoincludeallaccessiblesubjectsaspartofthe
sample.
Thisnonprobabilitysamplingtechniquecanbe
consideredasthebestofallnonprobabilitysamples
becauseitincludeallthesubjectsthatareavailable,
whichmakesthesampleabetterrepresentationofthe
entirepopulation.

63. ravsa

64. ravsa
Inthissamplingtechnique,theinvestigatorpickupall
theavailablesubjectswhoaremeetingthepreset
inclusion&exclusioncriteria.
Thistechniqueisgenerallyusedinsmall-sized
populations.
Forexample,ifaresearcherwantstostudytheactivity
patternofpostkidney-transplantpatient,hecanselects
allthepostkideneytransplantpatientswhomeetthe
designedinclusion&exclusioncriteria,&whoare
admittedinpost-transplantwardduringaspecific
timeperiod.

65. ravsa
Merits
Little effort
Saves time, money and material
Ensure more representative from population
Demerit
Researcher has not set sampling plans about the
sample
Not guarantee for representative sample
Sampling technique cannot be used to create
conclusion and interpretation for entire population.

66. ravsa
Quota Sampling
Itisnonprobabilitysamplingtechniquewhereinthe
researcherensuresequalorproportionate
representationofsubjects,dependingonwhichtraitis
consideredasthebasisofthequota.
Thebasesofthequotaareusuallyage,gender,
education,race,religion,&socio-economicstatus.

67. ravsa

68. ravsa
Forexample,
ifthebasisofthequotaiscollegelevel&theresearch
needsequalrepresentation,withasamplesizeof100,he
mustselect25first-yearstudents,another25second-
yearstudents,4925third-year,&25fourth-year
students.

69. ravsa
Merits
Economicallycheap,asthereisnoneedtoapproachall
thecandidates.
Suitableforstudieswherethefieldworkhastobe
carriedout,likestudiesrelatedtomarket&public
opinionpolls.
Demits
Notrepresenttheentirepopulation
Intheprocessofsamplingthesesubgroups,othertraits
inthesamplemaybeoverrepresented.
Biasispossible,asinvestigator/interviewercanselect
personsknowntohim.

70. ravsa
Snowball Sampling
Itisanonprobabilitysamplingtechniquethatisused
byresearcherstoidentifypotentialsubjectsinstudies
wheresubjectsarehardtolocatesuchascommercial
sexworkers,drugabusers,etc.
Forexample,aresearcherwantstoconductastudyon
theprevalenceofHIV/AIDSamongcommercialsex
workers.

71. ravsa

72. ravsa
Thistypeofsamplingtechniqueworkslikechain
Afterobservingtheinitialsubject,theresearcherasks
forassistancefromthesubjecttohelpinidentify
peoplewithasimilartraitofinterest.
Theprocessofsnowballsamplingismuchlikeasking
subjectstonominateanotherpersonwiththesame
trait.
Theresearcherthenobservesthenominatedsubjects
&continuesinthesamewayuntilobtaining
sufficientnumberofsubjects.

73. ravsa
Merits
Thechainreferralprocessallowstheresearcherto
reachpopulationsthataredifficulttosamplewhen
usingothersamplingmethods.
Theprocessischeap,simple,&cost-efficient.
Needlittleplanning&lesserworkforce
Demerits
Researcherhaslittlecontroloverthesampling
method.
Representativenessofthesampleisnotguaranteed.
Samplingbiasisalsoafearofresearcherswhenusing
thissamplingtechnique.

74. ravsa
Problems of sampling
Sampling errors
Lack of sample representativeness
Difficulty in estimation of sample size
Lack of knowledge about the sampling process
Lack of resources
Lack of cooperation
Lack of existing appropriate sampling frames for
larger population