Using Bayesian statistical modelling as a bridge between quantitative and qualitative analyses: illustrated via analysis of an online teaching tool
Embargoed until: 2019-05-01
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Bayesian methods provide a more general approach to statistical analysis that mathematically includes Null Hypothesis Significance Testing (NHST) and classical statistical modelling as special cases. This expanded, Bayesian, approach provides several benefits, which we illustrate using a case study about decision-making by teachers. We focus on a relatively unexplored topic: the way in which a Bayesian approach provides a “bridge” between qual/quant methods. We highlight five bridges, illustrated using the case study: (1) visualization of the conceptual framework, (2) generalization via randomization and alternatives, (3) stories for interpretation, (4) computation that is flexible, and (5) continual learning, through priors. This work illustrates these bridges using a case study on a digital tool that wove together: a behavioural study to investigate decision-making, with an inbuilt perceptual component to probe rationale for specific decisions, and an interview component. A mixed method was therefore a natural choice for integrating learnings across these data sources, collected using a single online tool. Thus, the digital learning sphere provides a context for raising awareness of the potential that the Bayesian statistical paradigm offers researchers who wish to connect qual/quant methods. In conclusion, mixing-in Bayesian with qualitative not only innovates on methodology. It also reshapes the ontology, epistemiology and axiology: providing a common ground for qual/quant methods, as a basis for better communication; redefining what quantitative method is, what it can achieve, and how it is done – particularly within a mixed method framework.
Educational Media International
© 2017 Taylor & Francis. This is an Accepted Manuscript of an article published by Taylor & Francis in Educational Media International on 23 Nov 2017, available online: 10.1080/09523987.2017.1397404
Specialist Studies in Education not elsewhere classified