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Summary SPS5034 mixed-methods-research-design
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lOMoARcPSD|6106013Mixed Methods Research Design 13.11.17
Research Design (University of Glasgow)
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Research Design. 13.11.17: Mixed Methods Approaches.
Mixed Methods:
It is a combination of quantitative and qualitative research methods, a form of
‘triangulation’ of the evidence that they produce.
There are several prominent approaches: nested; content; network.
Who should use the different types?
Sequential – this comes close to research cycle we discussed at the beginning of the course.
Almost everybody should use this approach.
*The boundaries between quant and qual are fluid anyway (as in comparative case
studies).*
Concurrent: instead of just following quantitative and qualitative methods along the
research cycle, you explicitly combine them at the same time. Useful if you have a lot of
data but it is fuzzy and requires interpretation.
Causal Complexity:
Say your research considers civil war. Some research forms with assume ‘additive causality’ -
the causes will be ‘additive’, suggesting that all of the causes independently affect the
dependent variable. But we might want to argue that the independent variables are
interacting in complex ways. For instance, foreign intervention and autocracy might have a
relationship, or cause one another, and so influence the dependent variable in a complex
way.
Regression analysis formula usually assumes additive causation.
Complex causation can be taken into account by multiplying the variables when calculating
regression.
*See slides for visualisations of examples: enjoyment of food and reliability of our
perceptions*.
In qualitative research it works a little differently.
You could use (in small-N comparative studies) Boolean logic terms (AND, OR, NOT).
Qualitative comparative analysis (QCA) a more principled way of doing this: similar to
regression modelling but for when you have fewer cases.
Bayesian statistics gives you a different perspective on regression modelling, and can also be
used with small data sets, to tell you how certain your results are. Requires knowing what
the variables are. It would solve small-N problem if you know what variables to look for.
Allows for specifying a “prior” – an expectation about the results. This is where the two
paradigms meet (quant and qual).
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