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- 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 with regard to the prevalence of a particular unknown piece of information, situation or outcome concerning the big group
- 2. Sampling Basically a sample is a subgroup of the population in which one is interested. The population or study population is usually denoted by the letter (N) and sample size is (n). Researchers work with samples rather than populations because it is more economical and practical. It is very time consuming and requires a lot of resources.
- 3. Why is sampling important? Two main reasons Firstly subject of our enquiry is usually people who are extremely problematic unlike inanimate subjects. People are complex, unpredictable, they cluster in groups, determined by social groups or specific interest and they are non responders, often refuse to provide us information we seek
- 4. Why sampling is important? Secondly the population we seek to study are frequently huge and larger the population being studied, the greater the risk that a sample drawn from that population may be unrepresentative. Because of size, cost time or lack of accesibility often makes it impossible for researchers to collect data directly from the entire group of interest.
- 5. SOURCE OF SAMPLE If a research is to be applicable and relevant to other population, the study sample must be representative of the group from which it is drawn, which in turn should be typical of the wider population to whom the researcher might apply.
- 6. SAMPLE SIZE Sample size matters in order to have sufficient power to detect a meaningful result at a certain level of statistical significance. Generalisability is possible depending upon the size of the sample, how representative it is of the wider population. The larger the sample, the more confidence we might have in generalising the findings
- 7. Quantitative sampling Quantitative and qualitative researchers have different approaches to sampling. Quantitative select samples that allow researchers to generalize their results to a target population and to do this, the sample must be representative. Sample must be large Sample must be randomly selected.
- 8. Qualitative sampling Although not exclusively, Qualitative research typically employs non probability sampling. This means that it is not usually intended that the findings of a particular study will be generalisable. It will apply only to the specific population under investigation Sample size is not determined by the need to ensure generalisability but a desire to fully investigate the chosen topic and provide rich data
- 9. Qualitative sampling In qualitative research, since the aim is to either to explore or describe phenomena, quantification has little significance. Researchers can find if the results are applicable outside the research situation and would the findings have meaning to others in a similar situation. Sample is small but generate a lot of data
- 10. AIMS IN SELECTING A SAMPLE 1. To achieve maximum precision in your estimates within a given sample size 2.To avoid bias in the selection of your sample
- 11. TYPES OF SAMPLING Random/probability samplings Non random/ non-probability sampling ‘Mixed’ sampling
- 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. Selection of a sample Simple random sample – Pulling names out of a hat Stratified 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. NON PROBABILITY SAMPLING Convenience (those most convenient) also known as accidental sampling Voluntary (Sample is self selected) Quota Sampling (Convenience sampling within groups of population) Purposive sampling (Handpicked supposedly typical or interesting cases Dimensional (Multidimensional quota sampling) Snowball (Building up a sample through informants
- 15. Key terms of sampling Probability sampling methods Simple Random Sampling Stratified Random sampling Systematic Random sampling Cluster random sampling Random route sampling
- 16. Simple Random Sampling Each member of a population has an equal chance of being drawn. Sampling is truly random and is based on a comprehensive sampling frame
- 17. Quota sampling Looks like stratified sampling on the face of it. It is non probability sampling Subjects are selected in a such a manner that each stratum of the population is proportionately represented Researcher ensure that a sample of male and female from certain ethnic groups , age, occupations are selected
- 18. Snowball sampling Also known as nominated sampling It is non probability sampling in which subjects are asked to provide referrals to other study subjects Respondents are believed to have pertinent information and are asked to nominate others who might be able provide further information
- 19. Convenience sampling Also known as accidental sampling and it is a non probability sampling Subjects are selected for a particular study because they simply available They are in the right place and at the right time and it is convenient for the researcher’s purpose
- 20. Purposive sampling Also termed judgemental sampling . It is a type of non probability sampling in which subject are selected because they are identified as knowledgeable with regard to the topic under investigation The subjects selected are a typical group from a certain area or
- 21. Theoretical sampling This a non probability sampling most often associated with qualitative research primarily with grounded theory