Research Methods Lecture 4: Critical Realism

Introduction Previous lecture presented criticisms of positivism and an alternative based on interpretivism Recently, Critical Realism has offered more criticisms of positivism but also of interpretivism Critical Realism has some radical implications for economic methodology

Critical Realism CR based on philosophy of Roy Bhaskar Bhaskar (1978) presented a realist theory of science and critique of other approaches Bhaskar (1979) presented the case for a social science based on modified naturalism Lawson (1997, 2003) is the principal exponent of CR in economics

Importance of ontology Bhaskar identified two dimensions of knowledge: Intransitive dimension of real objects existing independent of our knowledge of it Transitive dimension of knowledge: science as a social activity ID ≠ TD: conflation is guilty of committing the epistemic fallacy (e.g. in positivism)

Analysis of experiment Experiment: isolate action of one mechanism from external influences Scientist triggers mechanism by intervention Can produce a regularity ‘if X then Y’ Experiment leads to the discovery of powers (ways of acting) of entities, expressed through mechanisms

Analysis of experiment Transcendental deduction of reality Powers exist whether or not exercised Mechanisms work transfactually; i.e., whether or not regularity is found outside the experiment Experimental set up shows that in the outside world, conditions for event regularity do not exist: world comprises open systems

Critical Realist ontology World is structured: ordered and layered Object of science is to find causal mechanisms lying below level of events World comprises open systems: event regularities will in general not occur This applies to natural and social sciences; except social sciences rarely have access to experiment

Some Methodological Implications No problem of induction: not seeking regularities Deduction becomes problematic Quantitative methods become problematic Need to seek out alternative methods for investigating/explaining society Triangulation legitimised

Deduction Conclusions/predictions Assumptions

Deduction in open systems Conclusions/predictions Assumptions A/A’

Quantitative Methods Principal target for CR criticism Regression equation: Y = a + b 1 X 1 + b 2 X 2 + e Attempt to mimic experimental situation But regression equation imposes closure on open reality: not equivalent to experiment Assumptions of constant b i and random e are unjustified

Quantitative Methods Conditions for quantification require qualitative invariance Quantitative methods only capture the empirical level, not the causal level Uses of quantitative methods for prediction and predictivism (Friedman) problematic in open systems

Alternative Methods for Economics Logic of retroduction (underdeveloped) “Contrast explanation” Abstraction Hermeneutic moment Complex reality requires multiple methods of analysis  triangulation of qualitative and quantitative methods

Triangulation Use quantitative to analyse event level; use qualitative to investigate causal mechanisms All objects have qualitative element All methods presuppose closed systems to some degree Combine methods and their inferences Problem of operationalisation We’re working on it!

Conclusions CR tries to transcend positivism and interpretivism Reality is “open” and has “depth” Looking for the deep causal level Conventional methods problematic or misguided CR legitimises a strategy of triangulation