Generalisability

Generally speaking, generalisation refers to whether the findings of research can be applied beyond the research and to the larger population (Saunder et al., 2012). In quantitative studies generalisation is explicitly claimed based on scientific reasoning such as statistics and random samples, while in qualitative studies claims for generalisation is less explicit and some researchers even deny the possibility of generalisation (Payne & Williams, 2005). This study largely driven by a qualitative approach has limited chance to provide such generalisability. In a quest for whether interpretivism studies can produce generalisation, Williams (2000) by raising three possible meaning of generalisation, total generalisation, statistic generalisation and moderatum generalisation, argues that interpretivism provides moderatum generalisation, which is an intermediate type of limited generalisation (Williams, 2000, Payne & Williams, 2005). While total generalisation refers to an identical instance of a general law, statistical generalisation is understood as probability of phenomena occurring in the larger population (Fairweather & Rinne, 2012).

Fairweather and Rinne (2012) believe that Q method provides a basis for moderatum generalisation, that is defined as “where aspects of S can be seen to be instances of a broader recognisable set of feature” (Williams 2000, p. 215). Moderatum generalisation has moderate claims about the social world that are not meant to hold true over long periods of time or across cultures. They are moderately held and therefore open to change (Payne & Williams, 2005, Fairweather & Rinne, 2012). Such that moderatum generalisation includes hypothetical

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characters which can be testable with further evidence to be confirmed or rejected (Payne & Williams, 2005). As pointed out, Williams (2000) acknowledges the limits of moderatum generalisation to be only moderate, meaning no statistical generalisation should be made. This means that the findings of this study cannot be generalised to a larger population of Overland Track walkers. In addition, the findings might not stay true for a long period of time, or applicable to other walking tracks in Tasmania. However, it provides hypothetical clues to understand Overland Track walkers, and it is a useful implication for national park authorities who manages wilderness multiday walkers.

While limits of generalisation in interpretive studies lie in sampling, which selects participants purposely, if it is acceptable to aim generalisation from interpretive studies as a legitimate goal, one of strategies in sampling is to reveal characters of the wider group to which researchers wish to generalise (Williams, 2000). A sampling technique that aims to reach a ‘wider universe’ through a range of units such as experiences, characteristics and categories is a way to achieve this (Williams, 2000). In fact, Gobo (2008) states that findings provided by Q method are reflexively generalisable even if probability sampling is used. This is because as supported by Hunter (2012, p. 337) “while individuals might update or revise their attitudes toward a discourse, the cluster of subjectivity will represent original and unique functional divisions within society”. In addition, collecting data until it reaches saturation point, that is the point when no further interview would provide new information, is another strategy to make generalisation claims stronger (Williams, 2000).

Validity concerns whether the scale used measures what it intended to do (Bryman & Bell, 2007). Incorporation of accurate operational measures for the concepts under investigation, is essential strategy to increase the validity (Shenton, 2004). In Q methodology, this is ensured by theoretically defined concourses and exhaustively extracted Q statements (Hunter, 2012). This study used the Spectrum (Weaver & Lawton, 2001) as a theoretical base, while intensive interviews during Phase One of the project allowed case specific interpretation of the theoretical concourse in the form of statements. In addition, Ekinci & Riley (2001) witnessed face validity and content validity both from Q sort procedures. Face validity is established when people with experience or expertise in a field determine that the measure seems to reflect the concept (Bryman & Bell, 2007). Content validity is

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the extent to which one can generalize from a particular collection of items to all possible items in a broader domain of items, the intention is to obtain as representative a collection of item material and relevant content as possible (Nunnally & Berstein, 1994, p. 104).

These have been ensured in the process of developing statements from the Phase One interviews and sorting by the actual Overland Track walkers in Phase Two. Internal validity, ‘how congruent are the findings with reality?’ promotes confidence that they have accurately recorded the phenomena under scrutiny:

Reliability is quantitatively understood as whether the results can be replicated (Bryman & Bell, 2007). It concerns if the work can provide similar results again under the same context such as the same methods and the same participants (Shenton, 2004). Qualitatively speaking, however, the changing nature of the phenomena makes these tasks problematic (Shenton, 2004). As acknowledged in this section earlier, findings of Q method might not stay true for a long period of time due to the nature of moderatum generalisation. Thus qualitative researchers identify the reliability as dependability, and this can be addressed by reporting the processes within the study in detail, in order to allow researchers to repeat the study, not to achieve the same results (Shenton, 2004). In Q study, this has been ensured by the fit between sampling and framework (Echtner & Ritchie, 1991), the link between researcher and respondent and the additional observations, interviews and case-specific interpretation of results (McKeown & Thomas, 1988). This study has identified what was planned and implemented by providing the research framework in Section 3.3 and its implementation in Section 3.4. Operational detail of data gathering is minutely given for each of four steps involved in Q method in Section 3.5, to demonstrate what was done in the field.

The comparable concern to objectivity is confirmability in the mind of qualitative researchers. Strategies need to be undertaken to ensure that findings of the work present the experiences and perspectives of the participants, instead of that of researchers (Shenton, 2004). In order to achieve this, this study conducted the intensive preliminary interviews (Phase One interviews), instead of secondary data. Phase Two interviews also ensured that each participants provided their reasons for selecting each statement in the particular order. By combining these voices of participants with the statistical analysis, this study aimed to provide the voices of participants. As discussed later in Section 5.3.1., the researcher’s

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perspective came within the process of deciding the number of factors to extract. However, this is the nature of Q method, since there is no correct number of factors (Watts & Stenner, 2012), but the decision is driven by a variety of statistical analyses as well as theoretical considerations (McKeown & Thomas, 1988).