In psychological and social science research, the two major approaches to the gathering and analysing of data are qualitative and quantitative. Historically, the dominance of hypothetico-deductive methodologies has resulted in a heavy emphasis on quantitative methods (Henwood & Pidgeon, 1992). As discussed by Henwood and Pidgeon (1992) this is because quantification states the concepts embedded in theoretical schemes or hypotheses as precise mathematical formulas that are readily observable, manipulable and testable. This has been viewed as a necessary, if not sufficient, condition for the findings of research to be replicable and generalisable and for predictions upon the basis of observed regularities to be made. When the aim of science is the prediction and control of phenomena, the formulaic precision attained through the use of quantitative methods has considerable value. It’s not surprising therefore, that quantification has traditionally been seen as the scientific method (Guba & Lincoln, 1994; Henwood &Pidgeon, 1992).
Recently, however, a cogent critique of quantification has gained considerable traction in the literature (Guba & Lincoln, 1994). This critique is not new. It was first put forth by Dilthy as part of the Verstehen
movement, albeit with limited impact (Henwood & Pigeon, 1992; see section 3.4). It was also present in the nomothetic-idiographic debate of the 1950s and 1960s when Allport (1962) argued that an
his expositions of symbolic interactionism, Blumer (1969) also questioned the validity of quantitative methods such as experiments and questionnaires because they do not involve a direct examination of the empirical world: that is, they do not focus directly on the actor’s contextually rooted meanings, definitions, and interpretations as these emerge in ongoing, naturally occurring action and interaction (Stryker &Vryan, 2003). Blumer (1937, p.194) explained his objection to the quantitative approach as follows:
The items on a questionnaire…may be clear and precise and the individual may answer in the categorical and definite way that is needed for the quantitative treatment of responses. But the point made is that the responses to these items do not tell what is the meaning of these items to the individual; hence, the investigator is not in a position to state what are the individuals’ attitudes or to know what would be his likely behaviour if he were actually to act toward the objects to which the items refer.
Symbolic interactionists do not categorically deny the usefulness of quantitative research, but for the research questions they want to ask, quantitative experimental or survey-based approaches are inadequate (Blumer, 1969): the symbolic interactionist emphasis on meaning is simply not conducive to quantitative methods (Wallace & Wolf, 2006).
In more recent times, the critique of quantitative methods, and the broader hypothetico-deductive approach from which they derive, has been more fully developed and now poses a significant challenge to the conventional wisdom that has sustained the hegemony of quantification. The five major elements of this critique, as identified by Guba & Lincoln (1994) are as follows:
i. Context stripping. Quantitative research design, with its focus on experimental controls and randomization, ‘strips’ the research context of other variables that could potentially affect outcomes if they were allowed to exert their influence. Exclusionary quantitative designs that delimit the influence of contextual factors have limited applicability and generalisability because their outcomes can only be applied in circumstances similarly devoid of context.
ii. Exclusion of meaning and purpose. Unlike the physical world, human behaviour cannot be understood without reference to the meanings and purposes attached by human actors to their activities. There are also issues concerning the overwriting of internally structured ‘subjectivities’ by externally imposed ‘objective’ systems of meaning. This reflects the earlier sentiments of Blumer (1969).
iii. Disjunction of grand theories with local contexts: the etic/emic dilemma. The etic
(outsider) theory or hypothesis imposed on an inquiry by an investigator may have little or no meaning within the emic view of the individuals, groups, societies or cultures under inquiry. In quantitative research, there is the potential for
inappropriately fixing meanings where these are variable and renegotiable in relation to their context of use. This is particularly important in research with children where the items on a questionnaire may not have the same meaning for children as they do for the adults who developed them (James & Prout, 2004).
iv. Inapplicability of general data to individual cases: the nomothetic/idiographic dilemma. Generalisations based on nomothetic data derived from quantitative inquiry may be statistically meaningful but do not necessarily apply to individual cases because they neglect the uniqueness and particularity of human experience. This reflects Allport’s (1962) critique, as outlined earlier.
v. Exclusion of the discovery dimension in inquiry. An emphasis on the verification of specific a priori hypotheses undermines the origins of those hypotheses, which have usually been generated by what is commonly termed the discovery process. Consequently, quantitative normative methods are privileged over the insights gained from innovative research practices, thereby denying opportunities for the emergence of new or divergent theory.
As several authors note (Denzin & Lincoln, 2005; Guba & Lincoln, 1994; Henwood & Pidgeon, 1992), each of these problems can be remedied by increase in qualitative inputs that a) study phenomena in all of their contextual complexity, b) give priority to the meanings and purpose of participants, and c) permit the unique perspectives of different groups to be articulated. Such approaches enable new or divergent theory to emerge from the research process. Given that the aim of this research is to develop a theory that increases understanding of children’s knowledge of bushfire hazards as conceptualised by the children themselves, qualitative methods of data collection and analysis are required.