Sampling and Sample size

3.5.1 Target population

The target population of interest of the investigations should be the starting point for a sampling scheme according to Saks & Allisop (2007, p.157). The population of interest in this study with respect to the study aims involved consumers in health shops in the community and in-patients in a secondary healthcare setting within England. The Tyne and Wear region is a county within the North East of England (Figure 1.1, Chapter 1). The study also included a sample of staff in health shops with respect to the scope of the study to determine their opinions with respect to the study aims (Chapter 4, Section 4.2.3.2).

Page | 69 3.5.2 Convenience sampling

A convenience sample of consumers purchasing alternative medicine (and staff) in health shops in the community and in-patients in a secondary healthcare setting were included in the study.

Convenience sampling, a non-probability method of sampling, is when a sample has not been selected using a random selection method (Bryman, 2008). This implies that some units in the population are more likely to be selected than others (Bryman, 2008). However, in accordance with Trochim (2001), the convenience sampling of the population in this study was likely to get opinions of the target population even though it was likely to overweigh sub-groups in the population that was more readily accessible. This is in contrast to convenience sampling that is accidentally haphazardly not part of the population that you are interested in generalising. Therefore according to Trochim (2001) this does not mean that all cases of non-probability samples are not representative of the population. The case was that with non-probability samples, the population may or may not be represented well and it would be difficult to know how well this had been performed (Trochim, 2001). Therefore, not being able to generalise of the study findings to a larger population would be a limitation.

As Trochim (2001) observed, in some circumstance it would not be feasible, practical, or theoretically sensible to use random sampling (Saks and Allisop, 2007; Bryman (2008). In the case of this study the issues included access to the population and setting, ethical issues, willingness of consumers and in-patient in healthcare settings to participate were matters that needed to be considered (Chapter 4, Section 4.2 and Chapter 5, Section 5.2). More so, random sampling requires all members included in the sample to be listed and assigning random numbers to select them. However, both consumers and in-patients populations were not a static population and it would not have been possible to acquire a list that could be used to work out the random sampling.

Page | 70 3.5.3 Sample size

It is established that the greater the sample size the closer the sample would be to the actual population itself (Trochim, 2001; Bryman, 2008). In the view of Saks and Allisop (2007), the larger the sample size of the sample, the more precise the estimates derived from that sample are likely to be. However, as pointed out, in most projects it is not possible to involve all the people it would be desirable to be involved (Bowling, 2002; Smith, 2005; Bryman, 2008; Saks and Allisop, 2007; Trochim, 2001).

The goal was that within the time frame determined for data collection enough consumers and in-patients could be interviewed to enable the study aims to be met and a reasonable representation of the population studied to be included. There was a better chance for achieving the aims with the consumers since the study included consumers that were directly purchasing alternative medicine in health shops. On the other hand the sample size of in-patients included in the study reported in the results of the investigation in Chapter 5 was based on the size estimated below (Section 3.5.4).

The use of multiple interviewers may have enabled more health shops and hospitals to be covered which would have increased the representation of the sample to the population (Section 3.3.2). However, as previously discussed mainly for obvious reasons that this was a PhD programme, the interviews among the consumers and in-patients in this study were conducted by only the researcher (Chapters 4, Section 4.2 and Chapters 5, Section 5.2). It was mandatory that the student is expected to be involved in the entire process of the research. As with the rest of the study a time structure was drawn that had also influenced the time for the data collection. As Bryman (2008) observed, it is unlikely time and resources to include all members of the population, it is unlikely to be able to send postal questionnaires to all and it is even more unlikely to be able to interview the entire member of the population.

Page | 71 3.5.4 Estimated sample size of in-patients approved by the ethical committee

The sample size of in-patients included in the study (Chapter 5, Section 5.2.3) was based on size estimated below. This was approved by the University and national ethical committees. In respect to the earlier points made in Section 3.5.3 this was to achieve the study aims and to give better precision between the sample and the population.

The estimated sample size was between 86 and 316 on a basis of the 95% confidence interval to have a 6% to 71% prevalence of use of alternative medicine in the UK that was determined from the literature. It appeared that it is possible that different formulas may be recommended for the estimate of sample size. As a result three formulae obtained from the literature were used for the estimate of sample size in order to see whether it made any difference. Almost the same sample size estimate for the study was derived from these three formulae (Eng, 2003; Mathers et al., 2000; Gang XU, 1999). Hence, this could be said to have confirmed that the sample size was accurately estimated. The prevalence of use alternative medicine was the primary outcome for the estimate of sample size below, because it was essentially the basis of the study aims and for the study to be relevant to previous studies in the literature. A theoretical prevalence of alternative medicine use was used in the absence of an equivalent study from the literature or a pilot. These were the least available prevalence rates of use of alternative medicine 6% from a general population study in the UK (Ernst and White, 2000) and 71% was the highest prevalence rate from a patient focused study England (Featherstone et al., 2003) derived from the studies in the UK in the previous literature review (Chapter 2, Section 2.3-2.4).

Formula 1: N = 4(Zcrit) 2 p (1-p), (Eng, 2003)

D 2

For p= 6% N= 4(1.96) 2 0.06 (1-0.06) = 86.67 0.12

For p=71% N= 4(1.96) 2 0.71 (1-0.71) = 316.04 0.12

Page | 72 Zcrit = statistics for the desired level of confidence (level of significance) = 1.96 (Eng, 2003). p = Estimated the prevalence rates of alternative medicine of 6% to 71% in previous studies from the literature see above. D is 5% more or less of the total width of the expected level of significance of a 5% (0.05 level of significance is same as decided for the study in the chi-square test (Section 3.4.1.3)), more or less = 0 to 10% = 0.1

Formula 2: N = p (100-p), (Mathers et al., 2000)

(SE) 2

For p= 6% N= 6 (100-6) = 86.74 (2.55) 2

For p=71% N= 71 (100-71) = 316.28 (2.55) 2

N= Required Sample size. p = Estimated the prevalence rates of alternative medicine from the literature see above. SE = Standard Error SE for a 95% level of significance is 5/1.96= 2.55 (Mathers et al., 2000).

Formula 3: n = Z 2 (p (1-p) (Gang XU,1999) d2

For p= 6% n= 1.96 2 (0.06 (1-0.06) = 86.67

0.052 For p=71% n= 1.962 (0.06 (1-0.06) = 316.04 0.052

n = same as above in 1 and 2, z = same as level of confidence above in formula 1 and 2, p = same as 1 and 2 above, d = is the desired level of significance is 5% =0.05 for this proposed study see D in 2 above.

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