Sample size

There is no agreed sample size for qualitative interview research designs (Charmaz, 2014; Guest, Bunce, & Johnson, 2006; Mason, 2010; Teddlie & Yu, 2007; Yin, 2014). The central

consideration for the qualitative researcher is to ensure sufficient subjects are selected so that quality data is collected to address the research question, and that the selection strategy is appropriate for identifying a relevant sample population. Sample size is not a question of being large or small, but of being too large or too small because “inadequate sample sizes can

undermine the credibility of research findings” (Sandelowski, 1995, p. 179).

A range of literature supports the contention smaller sample sizes do not diminish the credibility of research findings. Collins’ (2010) summary of sampling size recommendations highlights case studies ranging between 3-5, with phenomenological studies ranging between 6- 10, while Creswell and Plano Clark (2011) note that case studies are typically between 4-10. Collins (2010) reviewed six empirical studies which use interviews as a data collection strategy and where sample sizes ranged from 6-9 to 8-12. The average of minimum reported numbers across the six studies is 6.3, and the average of maximum reported numbers is 10.8. In their analysis of sampling size based on the rate of code development in interviews, Guest et al. (2006) found that 73% of their codes were identified within the first six interviews. This had increased to 92% by the conclusion of twelve interviews, leading them to hypothesise that

analysis of interviews in smaller sample sizes can generate sufficient data to approach theoretical saturation.

Figure 4.3. Total population of Australian IB schools - distribution of ICSEA. Source: ACARA (2019)

Yin (2014) argues the language of sampling does not apply to case studies, based on the distinction between analytical and statistical generalisation. Where statistical generalisation relies on the adequacy of sampling to draw generalised conclusions from the broader population, analytic generalisation seeks to develop higher conceptual and theoretical insights which may have reach beyond the specific project. Rather, he advocates that selection of cases be considered for the extent to which they can “shed empirical light” (Yin, 2014, p. 40) on the research topic. Analytic generalisation thus rests not on the quantity of sample cases selected, but on having sufficient relevant data and on the quality of its analytic credibility and integrity. Homogeneity, or the extent to which the sample population has similarity, also supports use of smaller sampling sizes. Guest et al. (2006, p. 75) argue that homogenous sample sizes using a schedule with “a

certain degree of structure within interviews” will likely reach a position of theoretical saturation with lower sample sizes.

The final sample size of seven for this current study falls within these ranges and meets these criteria, although a size of eight was initially sought. Eight schools were identified using the maximum variation purposeful sampling strategy. Principals of these schools were

approached via email during September 2015 seeking their willingness to participate, contingent on ethics approval (Appendix C). The schools were spread across New South Wales, Victoria, Queensland, South Australia and the Australian Capital Territory. Two principals declined to participate from the outset, thus another two schools were identified with the same contextual characteristics. Principals of these schools were approached to replace those who declined. Four principals agreed to participate and gave email notification of their approval. One participant withdrew for health reasons, while another failed to respond to repeated requests to confirm willingness to participate in the study. Two further schools were identifed using the maximum variation sampling strategy and principals of those schools approached as replacements. One principal who indicated agreement to participate was subsequently forced to withdraw due to their state department of education declining to give ethical approval complementary to that provided by the University of Sydney’s Human Research Ethics Committee (see 4.7 Ethical considerations).

Another state department of education declined to approve the ethics application. This process took in excess of three months, during which time the other seven interviews were conducted. In consultation with the research supervisory team, consideration was given to proceeding with analysis of the seven interviews already concluded. Time constraints, and the view that limited new insights would likely emerge from further interviews, resulted in the

decision not to proceed with seeking an eighth participant. This was confirmed during initial coding where only 12 unique codes (from a total of 330 intial codes) were generated in coding the last interview transcript, and 23 from the second last. In total, these 35 codes represent 10.6%, thus 89.4% of codes were developed through initial coding of the first five participant transcripts, consistent with the experience of Guest et al. (2006).

Table 4.1 matches the maximum variation sampling strategy identified in the theoretical framework to participant schools, using pseudonyms; detailed descriptions of the schools and their pseudonyms are given in 4.3.5 Case schools and principal participant details. Only three Australian schools currently offer the CP (Table 2.5) and they are not included in this sample group due to their small proportion as a sub-population within the entire population of Australian IB schools. A comparison between the Phase One sample and the total Australian IB population by strata is also provided (Table 4.1).