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Bps managing dissertation
- 1. BPS Dissertation Workshop, 2011 Research Design and Methods Data Collection and Analysis Martin Ganco
- 3. Empirical design Paths to take: Secondary data & large sample analysis Primary data & large sample analysis Primary data & small sample - qualitative analysis Combination of the above
- 4. Common student traps: Developing many new pieces at the same time (theory, data, method, measures). Having beautiful and polished theory and starting to collect data very late. ….and end up with theory which cannot be tested or cannot be tested well. Be “too” data/context-driven. Hand-collected and novel/interesting but poor dataset. Suffering from mistakes made early. Expending all energy in one area (e.g. identification at the expense of theory).
- 5. How to avoid the traps? Limit the number of novel components Start data collection and analysis early (year 2-3) but be open to restart the process. Build/obtain a strong dataset! Theory building and empirical testing is an iterative process The data will limit your degrees of freedom in the theorizing Match theory to data – measures need to reflect the mechanisms
- 6. How to avoid the traps? Measure twice cut once Very path dependent process – choices that you make early in the process, when you lack skills, may haunt you later. Be careful and spend a lot of time in the design stage. Obtain feedback from different people. Stay flexible. Be open to revising theory/design/data/method after feedback (welcome to the review process). “Hourglass” approach Start broadly (focus on a high level theoretical relationship) Narrow down to 1-2 key variables Extensively analyze and obtain a set of well identified baseline results
- 7. Methods: endogeneity and the “identification revolution” Do not despair! Even though the identification is critically important, it should not be at the expense of a theoretical contribution. Be explicit about the endogeneity concerns and alternative explanations. Understand the patterns in the data and be creative. Good identification doesn’t necessarily mean an instrument or a natural experiment.
- 8. …and feel free to violate any of these rules if you feel that it is the right thing to do. Because…
- 9. Q&A
- 10. Which theory to test? Tradeoffs in theory building Verbal theorizing Ease of empirical implementation (Agent-based) simulations Analytical (math) equilibrium- based models Formalization
- 11. Which theory to test? Tradeoffs in theory building Ease of empirical Methodological implementation progress Formalization
- 12. Which theory to test? Tradeoffs in theory building Ease of empirical implementation Level of abstraction Formalization
Notes de l'éditeur
- What I am going to tell you is not all based on large sample evidence (evidence based science). Some of these are conjectures based on observation.
- Over focus on the context and data.Superb method but testing trivial relationships.
- Sample size and selection, quality measures, all relevant controls, etc. Good measures.Very costly investments – want to leverage them across projects. Need to be done right!
- Sample size and selection, quality measures, all relevant controls, etc. Good measures.Very costly investments – want to leverage them across projects. Need to be done right!
- I focus on X because there is a good instrument. This will lead to finding a well identified trivial relationships.Supplement the empirics with ruling out alternative explanations with logic.I.e. there is endogeneity.
- Sometimes referred to as rigor vs. relevance tradeoff.This is essentially a flexibility-constraints tradeoff.Less flexibility means better internal consistency that comes with formalization.Another tradeoff is in terms of specificity/generality (determines relevance).
- ABM More degrees of freedom than formal models and fewer degrees of freedom than verbal theorizing. In turn, you gain rigor and internal consistency.
- ABM More degrees of freedom than formal models and fewer degrees of freedom than verbal theorizing. In turn, you gain rigor and internal consistency.More distant from the phenomena – Higher abstraction – greater potential for theoretical contribution. And better generalizability.