Comparative case studies offer detailed insight into the causal mechanisms, processes, policies, motivations, decisions, beliefs and constraints facing actors – which statistics, large-scale surveys and cultural historiographies often struggle to explain.
As we discussed in week 1, case-oriented approaches place the integrity of the case, not variables, center-stage.
The language of variables, not the case, dominate the research process of variable-oriented comparative work. In case-oriented research the configuration of explanatory factors within-the-case is what matters, in terms of explaining the “outcome” of interest.
It is “Y” centred research.
What distinguishes the “case study approach” from “analytic narratives” is that the researcher operates from the assumption that their “case” reveals something about a broader population of cases. It shines a light on a bigger argument.
For example, generally, few people will care about your case on Ireland, Switzerland or Belgium, what they care about is it’s broader theoretical relevance.
Since the case is often constructed on the basis of a specific outcome or theory of interest, case selection is purposive i.e. it is not based on random sampling. It is theory driven.
In case studies, researchers want to explain a given outcome such as the re-emergence of far-right politics in Europe, and therefore they must violate the statistical rule of “choosing cases on the dependent variable”.
But actively selecting cases (the dependent variable) can lead to accusations of selection bias. How can purposive case selection be justified?
Political scientists require methodological justification for their case selection. It is not sufficient to say you are studying Irish politics because you speak the language and know the country. Nor is it sufficient to pick a case in order to ‘prove’ your theoretical claim.
What is your case study a case of?
The central question facing any case study researcher is “what is my case a case study of?”. Small N qualitative case studies inform the scholarly community about something larger than the case itself, even if the case cannot result in a complete generalization.
Case studies make a powerful contribution toward theory testing and theory building, something we will discuss in more detail in week 7.
Usually it is assumed that case studies are “countries”. But they can be anything from a person, a time period, a company, an event, a decision or a public policy.
What matters is how you construct the case study.
But what is a case? Is it an observation?
Methodologically, case studies should be bounded in time and space, related to a wider population of cases, and theoretically relevant.
Depending on the research question you are asking, or the puzzle that interests you, cases can be:
- Identified and established by the researcher (networks of elite influence)
- Objects that exist independently of the researcher (nation-states)
- Theoretically constructed by the researcher (benevolent tyranny)
- Theoretically accepted conventions (post-industrial societies)
Hancké (2009) uses the example of the Law and Justice Party in Poland, from 1995-2005, as a case study of rising populism in Eastern Europe. The case study is an in-depth analysis of the causal mechanisms that enabled populism to emerge in Poland, but it is framed against a broader universe of cases: the rise of populism in central and eastern Europe.
Single case studies
The weakest case studies are perhaps those selected to illustrate a theory.
A case study that challenges a scholarly community to think differently about the relevant dimensions of an existing theory is a much better contribution to social science debate.
These type of cases are often called “critical” or “crucial” case studies.
In terms of single case studies, casual process-tracing is the most widely used methodological strategy in political science. Causal process-tracing (which we will spend an entire seminar in week 9 on) attempts to unpack the precise causal chain or intermediate steps, or set of functional relationships, leading x to cause y.
They actively select their dependent variable in to trace the causal process leading x-y.
This is why we describe small N case study research as ‘purposive’. Researchers purposively select their case in order to explain a given “outcome” of interest.
For example, if we say that “democratic countries are wealthier”, we could unpack the causal mechanism into the following steps (with distinct empirical observations):
- Step 1: the median voter in a market economy has an income below the median
- Step 2: these voters support and elect parties that redistribute income
- Step 3: this redistribution leads to higher spending among the low-income majority
- Step 4: this results in higher consumption and aggregate demand
- Step 5: higher aggregate demand leads to higher employment and economic growth
This is not designed to be an empirical statement of fact. It is a reconstruction of a purported causal mechanism. Most importantly, each step can be empirically tested, against other proposed theories on why democracies are wealthier.
This is an essential point. In case study research, one needs a counter-factual, and an engagement with an alternative hypotheses/explanations for the same outcome. It’s not simply a matter of “telling a story” or a “I told you so argument”.
Critical case studies
Critical or crucial case studies challenge an existing theory.
Imagine you find a case where all existing theories suggest that given conditions X1, X2, X3, X4, we should expect to find a specified outcome Y1. Instead, we find a case with the opposite outcome.
Centralized wage-setting in a liberal market economy: the case of Ireland.
The researcher engages an existing theory, stacks the cards against herself, and then explains why the existing theory cannot explain the aberration observed.
It is not designed to generalize but to problematize.
Consider another example, almost all OECD countries experienced the common shock of declining interest rates and the expansion of cheap credit, but not every country experienced the emergence of an asset-price or housing bubble.
The same pressure in different institutional settings lead to different outcomes. Why?
Most different/most similar
Case studies are hard work and require a lot of careful reasoning by the researcher to ensure they are making valid comparisons that meaningfully speak to a wider population of cases, and which are of theoretical interest to a broader scientific community.
The most powerful techniques of comparison in the qualitative case study approach are those that make the dimensions of their case studies explicit.
The basic idea behind this approach originates in John Stuart Mills “A System of Logic“, and it’s usually referred to as the “Method of Difference” and “Method of Agreement” approach.
Alternatively, it is often referred to as a “most different or most similar” research design.
In the method of difference you select cases that are similar in every relevant characteristic expect for two: the outcome you are trying to explain (y – dependent variable), and what you think explains this outcome (x – independent variable).
Table 1 illustrates the logical structure of this comparative approach.
Examine the table. In this analysis, what explains the variation in house price inflation between case A (Ireland) and case B (Netherlands)?
Table 1: Method of Difference
|Case A (Netherlands)||Case B (Ireland)|
|Explanation 1 (credit)||Present||Present|
|Explanation 2 (LTV)||High||High|
|Explanation 3 (interest rate)||Low||Low|
|Explanation 4 (EMU)||Present||Present|
|Explanation 5 (growth)||High||High|
|Explanation N (IV – Income)*||Absent (low)||Present (high)|
|Outcome (DV – housing inflation)||Absent||Present|
The method of agreement works the other way around: everything between the two cases is different except for the explanation (x) and the outcome (y).
Table 2 illustrates the logical structure in this type of comparative analysis.
What explains the collapse of social partnership in Ireland and Italy in this example?
Table 2: Method of Agreement
|Case A (Italy)||Case B (Ireland)|
|Explanation 1 (size of economy)||Large||Small|
|Explanation 2 (type of market economy)||Coordinated||Liberal|
|Explanation 3 (problem-load)||Pensions||Wages|
|Explanation 4 (partisanship)||Technocratic||Centrist|
|Explanation N (union power)*||Weak-insiders||Weak-insiders|
|Outcome (DV – collapse of social partnership)||Present||Present|
The essential point to remember – and the main takeaway of this seminar – is that you need to defend your case selection, and think systematically about your comparisons.
Causal process tracing is a technique that will enable you to do this (week 8/9).
Gerring & Seawright (2008) suggest 7 case selection procedures, each of which facilities a different strategy for within-case analysis. These case selection procedures are:
- Typical (cases that confirm a given theory)
- Diverse (cases that illuminate the full range of variation on X, Y or X/Y)
- Extreme (cases with an extremely unusual values on X or Y)
- Deviant (cases that deviate from an established cross-case population)
- Influential (cases with established and influential configurations of X’s)
- Most similar (cases are similar on all variables except X1 and Y)
- Most different (cases are different on all variables except X1 and Y)
I would add “crucial or critical” cases to this list (cases that problematise a theory).
Discuss these case selection procedures and their methodological justification, and identify which is most appropriate to your research design.