Published on

Published in: Technology, Education
1 Comment
No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide


  1. 1. What is sampling? Sampling is the process of selecting a few (sample) from a bigger group (sampling population) so that estimation or prediction is made withregard to the prevalence of a particularunknown piece of information, situation or outcome concerning the big group
  2. 2. SamplingBasically a sample is a subgroup of thepopulation in which one is interested. Thepopulation or study population is usuallydenoted by the letter (N) and sample sizeis (n).Researchers work with samples ratherthan populations because it is moreeconomical and practical. It is very timeconsuming and requires a lot ofresources.
  3. 3. Why is sampling important?Two main reasonsFirstly subject of our enquiry is usuallypeople who are extremely problematicunlike inanimate subjects. People arecomplex, unpredictable, they cluster ingroups, determined by social groups orspecific interest and they are nonresponders, often refuse to provide usinformation we seek
  4. 4. Why sampling is important?Secondly the population we seek to studyare frequently huge and larger thepopulation being studied, the greater therisk that a sample drawn from thatpopulation may be unrepresentative.Because of size, cost time or lack ofaccesibility often makes it impossible forresearchers to collect data directly fromthe entire group of interest.
  5. 5. SOURCE OF SAMPLEIf a research is to be applicable andrelevant to other population, the studysample must be representative of thegroup from which it is drawn, which inturn should be typical of the widerpopulation to whom the researchermight apply.
  6. 6. SAMPLE SIZESample size matters in order to havesufficient power to detect a meaningfulresult at a certain level of statisticalsignificance.Generalisability is possible depending uponthe size of the sample, how representativeit is of the wider population. The larger thesample, the more confidence we mighthave in generalising the findings
  7. 7. Quantitative samplingQuantitative and qualitative researchershave different approaches to sampling.Quantitative select samples that allowresearchers to generalize their results to atarget population and to do this, thesample must be representative.Sample must be largeSample must be randomly selected.
  8. 8. Qualitative samplingAlthough not exclusively, Qualitative researchtypically employs non probability sampling.This means that it is not usually intended thatthe findings of a particular study will begeneralisable.It will apply only to the specific population underinvestigationSample size is not determined by the need toensure generalisability but a desire to fullyinvestigate the chosen topic and provide richdata
  9. 9. Qualitative samplingIn qualitative research, since the aim is toeither to explore or describe phenomena,quantification has little significance. Researchers can find if the results areapplicable outside the research situationand would the findings have meaning toothers in a similar situation.Sample is small but generate a lot of data
  10. 10. AIMS IN SELECTING A SAMPLE1. To achieve maximum precisionin your estimates within a givensample size2.To avoid bias in the selection ofyour sample
  11. 11. TYPES OF SAMPLINGRandom/probability samplingsNon random/ non-probabilitysampling‘Mixed’ sampling
  12. 12. SAMPLING STRATEGIES Probability (Random) sampling Simple random (selection at random) Systematic (selecting every nth case) Stratified (sampling within groups of population) Cluster (surveying whole clusters of population sampled at random) Stage (sampling clusters sampled at random)
  13. 13. Selection of a sampleSimple random sample– Pulling names out of a hatStratified random sample– Separating the units into strata (layers), e.g. age, disease, gender. Including each of these strata in the sample selected.Systematic random sample– Uses systematic intervals e.g. every 9th person, every 3rd house.Cluster random sample– Selecting a cluster, e.g. 20 hospitals, and then choosing 8 to study.
  14. 14. NON PROBABILITY SAMPLINGConvenience (those most convenient) alsoknown as accidental samplingVoluntary (Sample is self selected)Quota Sampling (Convenience sampling withingroups of population)Purposive sampling (Handpicked supposedlytypical or interesting casesDimensional (Multidimensional quota sampling)Snowball (Building up a sample throughinformants
  15. 15. Key terms of samplingProbability sampling methods Simple Random Sampling Stratified Random sampling Systematic Random sampling Cluster random sampling Random route sampling
  16. 16. Simple Random SamplingEach member of a population has anequal chance of being drawn.Sampling is truly random and is based ona comprehensive sampling frame
  17. 17. Quota samplingLooks like stratified sampling on the faceof it.It is non probability samplingSubjects are selected in a such a mannerthat each stratum of the population isproportionately representedResearcher ensure that a sample of maleand female from certain ethnic groups ,age, occupations are selected
  18. 18. Snowball samplingAlso known as nominated samplingIt is non probability sampling in whichsubjects are asked to provide referrals toother study subjectsRespondents are believed to havepertinent information and are asked tonominate others who might be ableprovide further information
  19. 19. Convenience samplingAlso known as accidental sampling and itis a non probability samplingSubjects are selected for a particular studybecause they simply availableThey are in the right place and at the righttime and it is convenient for theresearcher’s purpose
  20. 20. Purposive samplingAlso termed judgemental sampling . It is atype of non probability sampling in whichsubject are selected because they areidentified as knowledgeable with regard tothe topic under investigationThe subjects selected are a typical groupfrom a certain area or
  21. 21. Theoretical samplingThis a non probability sampling most oftenassociated with qualitative researchprimarily with grounded theory