Sampling strategy and sample size

One of the most important distinguishing features between a qualitative and quantitative inquiry is the type of sampling strategy both approaches are likely to employ (Pidgeon and Henwood, 2004, p.634). For example, in qualitative research, random sampling is rarely utilized for collecting data because findings from qualitative studies are not designed for hypothesis testing or for calculating probabilities from a population sample (Thomas, 2006). Thus, the major goal of a qualitative study is not to generalize across a population, but to provide a clearer and deeper understanding or explanation from interviewees’ perspectives (Pope et al. 2000; Taylor-Powell and Renner, 2003). In other words, as is the case with this study, qualitative research will, more often than not, attempt to answer questions such as what is unique about this individual, group, situation, or incident?

It is important to note that the sampling strategy that is employed in a particular study is closely related to both the internal and external validities, as well as to the transferability of the findings from such study. This therefore explains why the number of participants that is expected in qualitative studies is often lower than that expected in quantitative studies. In a qualitative study the sample size, although an important factor, may not necessarily influence the outcome of the study as much as

125 the type of participants selected and their process of selection would. These factors are important determinants of the quality of conclusions drawn from a qualitative study.

In this current study, a combination of purposeful sampling strategies were employed, as shown below. It was important, for the purpose of this study, to ensure that any sampling strategy that was to be considered was one that facilitated the knowledge elicitation process for obtaining qualitative information from experts and from the incident accounts being reported (Sandlewoski, 2000). Purposeful sampling is inclined towards the development of idiographic knowledge i.e. knowledge from and about specific cases, which is quite difficult to obtain through probabilistic sampling (Patton, 1990). Probabilistic or simple random sampling is typical of quantitative research as most quantitative studies are more inclined towards the development of nomothetic knowledge i.e. knowledge derived from investigations that are made from samples which are then generalized to the wider population (Burrell and Morgan, 1979; Patton, 2002). Since this current research is concerned with the development of idiographic knowledge (i.e. what expert firefighters know and do), it thus became logical to turn to purposeful sampling.

Patton (1990, p.182-183) identified sixteen different sampling sub-types within the purposeful sampling class — a list that proved quite useful as it allowed an appreciation of the variety of sampling strategies that exist in the qualitative research family to be possible. After a careful evaluation, four of the sampling strategies were found to be applicable to this current study:

Criterion sampling: This was an important purposive sampling strategy in this

study. Some criteria which served as the basis for screening the participants were set prior to data collection. To ensure that expertise was verified and not assumed, participants were carefully selected on the basis of their rank/position and also through peer nomination. All participants had to have personally been involved in managing real-life fire incidents for which they made critical decisions independently.

126 Also, they had to have at least operated as incident or operational commander i.e. managing at least one fire engine and leading certain number of fire crews out to a fire call.

Extreme or deviant case sampling: Searching for extreme and critical fire cases

that particularly challenged experts’ knowledge as opposed to typical or routine fire cases. This strategy fits well with the scope of the current study since it has been shown that experts typically utilize tacit knowledge when managing complex incidents than they would do when managing simple incidents (Wipawayangkool and Teng, 2014)

Stratified purposeful sampling: This illustrates the characteristics of particular

subgroups of interest, thereby facilitating comparisons. By collecting data about experts’ performances (both in the UK and Nigeria), this study aimed to compare and contrast the decision making strategies used by experts from both groups, with particular interest on the cultural differences that exist between the two groups.

Snowball or chain sampling: Snowball sampling is a non-probability sampling

technique that is used by investigators to identify potential subjects (Morgan, 2008). This method is common in studies where subjects with some characteristics of interest tend to be relatively difficult to track down. One of the benefits of the snowball sampling strategy lies in its ability to identify cases of interest from “someone who knows someone”, and the chain goes on and on till the investigator perceives that data saturation has been reached (Cohen and Arieli, 2011). It can therefore be inferred that the method is an effective way of identifying and gaining access to subjects, especially where a researcher anticipates difficulties in creating a representative sample of the study population. Although it is true that a number of officers referred the author to other officers during the interview process (either from the same fire station or from another), not all the participants emerged from the snowballing process. Thus, snowballing was used in this study as a complementary strategy rather than as an alternative sampling strategy.

127 Perhaps one of the most remarkable advantages of using the snowballing strategy was that it allowed past ties and communication with prior participants to enhance cooperation from, as well as trust with the potential participants. This was much evident from this study, particularly in Nigeria where referral seemed to be taken seriously. However, despite the benefits of the snowballing method, representativity has been shown to be its main limitation (Morgan, 2008). Being a convenient sampling strategy, selection bias and external and internal validity limitations tend to be prevalent with snowballing. This ultimately explains existing claim that most snowball samples are biased and cannot be generalized (Griffiths et al., 1993; Cohen and Arieli, 2011). Selection bias mainly stems from the fact that participants are often not sought randomly unlike other ‘pure’ random sampling strategies (Patton, 2002), but are rather dependent on the referrals of the respondents first accessed and on the willingness of the research subjects to participate. To overcome any possible selection bias from this method and as part of the quality assurance mechanism, it therefore became important to ensure that all potential participants met the criteria for inclusion discussed above

3.4.2. Sample Size

In the qualitative research community, the issue of sample size has generated much debate: it remains a lingering question as to what particular sample size is ideal for a qualitative study. For example, a wide range of studies that employed the critical decision method have used a varying number of sample size — ranging from 4 to 40 (Klein 1988,; Flin 1996; O’Hare et al. 1998; Calderwood et al. 1990; Wong, 2000; Hutchins et al. 2004; Horberry and Cooke, 2010). Some of these authors (e.g. Weitzenfeld, Freeman, Riedl and Klein, 1990) believe that conducting face-face CDM interviews with 3-4 experts will still generate a reasonable depth of expert knowledge that is both reliable and transferable as would a larger sample size.

Pope et al. (2000), Stake (1980) argue that, although it is quite difficult not to have a specific sample size in mind before commencing a study, qualitative researchers should be disciplined not to firmly hold onto their pre-selected sample size. These authors rather suggested that the most important factor to “weigh up” is whether the

128 point of data saturation has been reached, where new concepts or themes no longer seem to emerge from the data. Hence, in a theoretically sensitive sample i.e. a sample that is diverse in characteristics and experiences (as with CDM data), reaching the point of data saturation should be used as the criterion for determining whether or not to collect more data. If more themes are emerging from the analysis of the latest batch of data, then the chances are that more themes are still likely to emerge when additional data is collected.

The sample size for this current study comprises 31 firefighters (made up of 15 experts in the UK and 15 experts in Nigeria, plus 1 trainee commander in Nigeria). This sample size was chosen partly because it exceeds the usual range used in other CDM research, and partly because it has generally been suggested that data saturation starts to occur from a sample size of 8-10 for in-depth qualitative studies (Marshall, 1996). The demographic details of the participants are discussed in chapter 5.