RESEARCH METHOD COHEN ok
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RESEARCH METHOD COHEN ok

RESEARCH METHOD COHEN ok RESEARCH METHOD COHEN ok

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CONCLUSION 117 groups does one turn next for data Second, for what theoretical purposes does one seek further data In response to the first, Glaser and Strauss (1967: 49) suggest that the decision is based on theoretical relevance, i.e. those groups that will assist in the generation of as many properties and categories as possible. Hence the size of the data set may be fixed by the number of participants in the organization, or the number of people to whom one has access, but the researcher has to consider that the door may have to be left open for him/her to seek further data in order to ensure theoretical adequacy and to check what has been found so far with further data (Flick et al.2004:170).Inthiscaseitisnotalways possible to predict at the start of the research just how many, and who, the research will need for the sampling; it becomes an iterative process. Non-probability samples also reflect the issue that sampling can be of people but it can also be of issues. Samples of people might be selected because the researcher is concerned to address specific issues, for example, those students who misbehave, those who are reluctant to go to school, those with a history of drug dealing, those who prefer extra-curricular to curricular activities. Here it is the issue that drives the sampling, and so the question becomes not only ‘whom should I sample’ but also ‘what should I sample’ (Mason 2002: 127–32). In turn this suggests that it is not only people who may be sampled, but texts, documents, records, settings, environments, events, objects, organizations, occurrences, activities and so on. Planning a sampling strategy There are several steps in planning the sampling strategy: 1 Decidewhetheryouneedasample,orwhether it is possible to have the whole population. 2 Identifythepopulation,itsimportantfeatures (the sampling frame) and its size. 3 Identify the kind of sampling strategy you require (e.g. which variant of probability and non-probability sample you require). 4 Ensurethataccesstothesampleisguaranteed. If not, be prepared to modify the sampling strategy (step 2). 5 For probability sampling, identify the confidence level and confidence intervals that you require. For non-probability sampling, identify the people whom you require in the sample. 6 Calculatethenumbersrequiredinthesample, allowing for non-response, incomplete or spoiled responses, attrition and sample mortality, i.e. build in redundancy. 7 Decide how to gain and manage access and contact (e.g. advertisement, letter, telephone, email, personal visit, personal contacts/friends). 8 Be prepared to weight (adjust) the data, once collected. Conclusion The message from this chapter is the same as for many of the others – that every element of the research should not be arbitrary but planned and deliberate, and that, as before, the criterion of planning must be fitness for purpose. Theselection of a sampling strategy must be governed by the criterion of suitability. The choice of which strategy to adopt must be mindful of the purposes of the research, the time scales and constraints on the research, the methods of data collection, and the methodology of the research. The sampling chosen must be appropriate for all of these factors if validity is to be served. To the question ‘how large should my sample be’, the answer is complicated. This chapter has suggested that it all depends on: population size confidence level and confidence interval required accuracy required (the smallest sampling error sought) number of strata required number of variables included in the study variability of the factor under study Chapter 4

118 SAMPLING the kind of sample (different kinds of sample within probability and non-probability sampling) representativeness of the sample allowances to be made for attrition and nonresponse need to keep proportionality in a proportionate sample. That said, this chapter has urged researchers to use large rather than small samples, particularly in quantitative research.

118 SAMPLING<br />

the kind of sample (different kinds of<br />

sample within probability and non-probability<br />

sampling)<br />

representativeness of the sample<br />

allowances to be made for attrition and nonresponse<br />

<br />

need to keep proportionality in a proportionate<br />

sample.<br />

That said, this chapter has urged researchers to<br />

use large rather than small samples, particularly in<br />

quantitative research.

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