1. Research Design and Methods

2. Causal Inference  What is causal inference “…learning about CAUSAL effects from the data observed.” (KKV, 8) -why treaty/policy A is more effective than treaty/policy B (rather than what is treaty A, is it effective)?; why is country A more sustainable than country B?; does strong civil society cause greater policy sustainability; are richer countries more sustainable than poorer counts?  Different from descriptive, philosophical, normative, prescriptive approaches  Causal inference is uncertain What evidence would increase confidence in causal argument

3. Selecting a Research Question  Question that interests you  Question that lends itself to causal analysis  Question that is of policy and/or theoretical significance  Think whether and how you could measure your dependent and explanatory/independent variables

4. What Is a Variable?  Variable is a measure of something that we are interested in. It varies in certain, measurable ways. Something which can have at least two values.  Dependent variable = effect  Independent variable = cause  Not always clear which direction causality runs. Sometimes issues of simultaneity or endogeneity.

5. Hypotheses  Probabilistic proposition about how variables relate, keeping everything else constant  Countries that have stronger environmental movements are more likely to adopt sustainable development policies, ceteris paribus  Environmental treaties with stronger monitoring clauses are more likely to be implemented  Higher levels of economic development is likely to lead to greater concern about the environment and more rigorous environmental policy. The different sustainable development ranking of a country is likely to be positively affected by the level of economic development  Sources of hypotheses  Theory  Existing literature, case studies  Logic

6. Methods  Experimental methods – not widely used in social science  Statistical methods  Comparative (case study) method  Case study method

7. Research Design  Identify a puzzle, research question  Identify theories that are relevant to that question.  Often achieved through literature review.  Have some preliminary hypotheses about how the variables of interest could correlate  Identify observable implications of your hypotheses  Select research and analysis method

8. Improving Case Selection  Increase number of cases or observations (disaggregate across regions, across time, etc.)  Control for as many variable as possible. Focus on comparable cases Select cases that similar in a lot of aspects, but vary along the variable of interest  Be explicit on why this particular set of cases were selected and make a good comparison.  Focus analysis on key variables, avoid “laundry list” type of explanations

9. Increasing Confidence in Causal Argument  Observable implications -generate as many as possible, check against evidence  Counterfactuals  Could the causal relationship be spurious (both the d.v. and i.v are caused by a third variable affecting each simultaneously and are therefore unrelated)  Distinguish correlation from causation.

10. Use of Data  Make sure you have a “measure” of your dependent and explanatory variables  Data and methods should be public, transparent, well documented  Make use or at least show awareness of the full range of data sources on a particular topic  Ask whether the particular measure of the variable is driving the results. Show awareness of a possible problem  Be cautious when using Internet sources, do not rely only on internet sources.