exam revision: deductive versus inductive research strategies
This belongs to the revision of social research strategies, I am going to summarise the key differences between inductive and deductive research approaches – but first what they’ve got in common. Both strategies are rooted in a positivist assumption in terms of epistemology and ontology. The underlying empiricism, i.e. the notion that only knowledge gained through experiences and senses is acceptable, is implemented by rigorous testing. Enlarging the number of instances observed (samples) increases plausibility and the number of regularities being identified. The accumulated ‘facts’ provide basis for general laws of cause and effects. Those are depicted in models as dependent (predictor) and independent (outcome) variables.
Inductive theory is being derived from the observations made. This approach cannot test hypotheses but generates them. In contrast, deduction is theory-driven, it’s based on preconceptions and aims to overcome the limitations of induction. It puts theories to the test, that means hypotheses can be falsified and disproved. The aim is to move closer to the truth, hence the gradual elimination of false theories implies that theories tested and not disproved can only be considered provisional.
Ideally, a deductive approach starts with a theoretical framework (for instance based on Erving Goffman’s ‘stigma’ or Pierre Bourdieu’s ‘social capital’) and the formulation of hypotheses. Usually, this includes an alternative hypothesis (also called experimental H., which states the effect assumed) and the null hypothesis (which states the effect is absent). What follows is the data collection which delivers findings that either result in confirmation or rejection of the null hypothesis and a subsequent revision of the theory.
In practice, though, deduction often entails an element of induction and vice versa. This is rooted in theoretical reflection once the data has been collected or the desire to establish conditions which allow the theory to hold (or not). This continuous weaving back and forth between data and theory and is called an iterative strategy, particularly evident in qualitative research which takes a grounded theory approach and a way to add to the validity of research. In quantitative research, it is advisable to carefully distinguish between the more complex development of theory and the generalisation of empirical findings.