Quantification and realist methodologies

ABSTRACT

The use of quantitative methods within realist methodologies are fairly rare. This is perhaps because a realist understanding of the social word as complex and dynamic (messy but not chaotic) does not sit well with traditional variable-based causal analysis which test specific theoretical assumptions, yet cannot account for interaction, moderation and emergence. In this paper, we explore the ontological challenges and epistemological issues which underpin the development of our complex realist approach to quantitative data analysis. We provide an example of its application to a large case-based time-ordered dataset and the resultant discovery of the deep patterns that underlie what happened to similar and different mentally disordered offenders as a consequence of the implementation of a new policy of custody diversion.

KEYWORDS:

• Realist methodologies
• quantification
• complex realist
• quantitative data

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. These are its natural allies, because realism is a naturalistic doctrine and realist social research is (or therefore should be a scientific enterprise).

2. The GLM can ‘cope’ with a relatively small number of interaction terms, but if many are added in the number of possible covariates and thus possible models rapidly escalates (Elliot, 2005, p. 102)

3. Ragin and Fiss (2016), in a book which challenges simplistic understandings of intelligence test scores, propose not simply an intersectional approach (which incorporates – for example, ethnicity, class, gender, family background etc.), but an intersectional methodological approach, that utilises Qualitative Comparative Analysis (QCA). Similarly, Brian Castellani and his colleagues (Castellani and Hafferty (2009) have devised a toolkit (the SACS Toolkit) that is case-based, mixed-method, system-clustering, data-compressing, theoretically-driven toolkit for modelling complex social systems. An empirical example of this can be found in Castellani et al. (2018). Though each of these approaches is methodologically different to the one we outline here (particularly QCA), they share the same methodological adherence to complexity, realism and intersectionality.

4. The critical realist economist, Tony Lawson (Lawson, 1997), has been very sceptical about the success of aggregate models. His argument is that all statistical models must contain an error term, and the amount of variability in social life renders such models useless. He believes quantitative analysis should limit itself to the use of descriptive statistics. In principle, we agree with this view (and we have argued as much elsewhere (reference removed for anonymity), but in practice even empiricist causal models, interpreted as inference to the best explanation, can provide valuable clues to mechanisms (reference removed for anonymity).

Additional information

Notes on contributors

Wendy Dyer

Wendy Dyer is a Lecturer in the Department of Social Sciences at Northumbria University, UK

Malcolm Williams

Malcolm Williams is a Professor in the School of Social Sciences at the Cardiff University, UK.