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Summary SPS5034 research-planning-and-design-161017
- Module
- SPS5034 Research Design
- Institution
- University Of Glasgow
SPS5034 research-planning-and-design-
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lOMoARcPSD|6106013Notes Research Planning and Design 16.10.17
Research Design (University of Glasgow)
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Research Planning and Design. Case Selection and Small-N Research Design. 16.10.17
Number of Cases and Indeterminate Research Designs:
Indeterminate – ‘virtually nothing can be learned from about the causal hypothesis.’
This can happen as a consequence of:
More inferences than cases: you are trying to make more causal inferences than you have
cases to draw data from.
Multi-collinearity: several independent variables are related to a very large extent.
Example: Does geographic distance to the workplace explain job satisfaction?
Control variables: nature of area (rural or urban); travel time; salary.
Random sample of 1,000 people drawn from the Scottish population.
1,000 cases – 4 variables = 996 degrees of freedom. (Subtract variables form the cases). This
is first step of calculating indeterminacy. If it’s really low that can be a problem (you get an
indeterminate research design if your degree of freedom is close to zero or negative).
Multi-collinearity. Travel time and distance to work are almost perfectly correlated. This
could be a problem: if we have a lot of cases which are very similar, we cannot compare
them to each other, and so cannot learn anything from the data.
One needs a sufficient number of cases in which the variables differ.
When multi-collinearity is present the degrees of freedom are reduced.
What can we do? Leave out a variable. You have to decide what is conceptually interesting
to you. Travel time or distance? You can’t put in both because they are highly correlated.
Example Two: (few cases).
Does the size of an ethnic minority lead to genocide?
Control variable (you could have more): economic deprivation.
Case selection:
Khmer Rouge in Cambodia.
Contemporary Switzerland.
2 Cases – 3 Variables = 1 degree of freedom. (note: the slides and lecturer said 1 degree of
freedom, but you might want to check this – if the formula is to subtract variables from the
cases then presumably it is -1).
We cannot distinguish between the causal effects of the two independent variables.
We have fewer cases to test than we have variables that we want to examine: this is why we
get negative degrees of freedom here.
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