- Normative (vs. empirical analytical), descriptive, causal and predictive research questions
- Q: What is the difference?
- Q: What does the “inference” in statistical inference stand for?
- Normative vs. empirical analytical (positive)
- Should men and women be paid equally? Are men and women paid equally (and why?)?
- Q: Which one is empirical-analytical, which one normative? Can we derive hypotheses for normative questions?
- What? vs. Why? (Gerring 2012, 722-723)
- Describe aspect of the world vs. causal arguments that hold that one or more phenomena generate change in some outcome (imply a counterfactual)
- My personal preference: descriptive vs. causal questions
Measure:‘Would you say that most people can be trusted or that you can’t be too careful in dealing with people, if 0 means “Can’t be too careful” and 10 means “Most people can be trusted”?’
How are observations distributed across values of trust (Y)? (univariate)
- We can add as many variables/dimensions as we like → multivariate (e.g. gender, time)
- Q: What would the table above look like when we add gender as a second dimension?
- Descriptive questions (multivariate)
- Do females have more trust than males?
- Did trust rise across time?
- Mean Non-victims: 6.2; Mean Victims: 5.48
- Descriptive questions: Do victims have a different/lower level of trust from/than non-victims?
- Why?-questions start with difference(s) and, then, seek to explain why those difference(s) occured
- Why does this group of people have a higher level of trust?
- Causal questions: Is there a causal effect of victimization on trust? (We’ll define causal effect later)
- Data underlying descriptive & causal questions is the same
- Causal questions already concern one (or more) explanatory causal factors