Lecture 2: Research Logic and Research Design Flashcards
Normative research
- questions that inquire what ought to be
- deals with ethics, values, and value judgements (aim = prescriptive)
- require the application of philosophy rather than of data
Positive research
- questions that inquire what is
- deals with empirical phenomena and theoretical concepts plus the link between them
- capable of being researched through data collection
Theoretical research
- theory elaboration, conceptualizations
- analytical (truth = based on logical deductive reasoning)
Empirical research
- theory generation, testing and application, conceptualization
- synthetic (truth result from a confrontation between theory and empirical content)
Descriptive research
- collection of facts that can be used as evidence
- good descriptive research can lead to a ‘light bulb’ moment
Explanatory research
- can focus on causes of events, causal effects or causal mechanisms
- why-questions: causal effects
- how-questions: causal mechanisms
What is research design?
- logical plan
- ‘how are you going to conduct the research?’
- method needs to be consistent and logical, not logistical
Units of variation
= units of analysis (count of N / number of cases)
Case study
empirical analysis of a small sample of cases
X-centered
Focuses on a cause
- has it a specific effect on a given outcome
- gauging the contribution of X in explaining part of the variation in Y
Y-centered
Focuses on the outcome
- discern the relevant causes
- explaining the variation in Y as best as you can (backward looking)
Mechanism-centered
Focuses on tracing a causal mechanism/causal process
- uncovering the sequence of intervening factors that link an X to an Y
What is a case?
Bounded empirical phenomenon that is an instance of a population of similar empirical phenomena
- boundaries: spatial / temporal / substantive
- causal homogeneity: causal effects and mechanisms are expected to hold true for other cases in the population
Population
universe of cases, studied and unstudied
Sample
Studied cases
Analytical level
- macro level
- meso level
- micro level
Macro level
Societies, economies, states
Meso level
Groups, territorial subunits
Micro level
Individuals
Level of analysis
- cross-case level
- within-case level
Cross-case level
Causal effects
Within-case level
Causal mechanisms
Types of observations
- data set observations
- causal process observations
Causation
A type of co-variation where one phenomenon contributes to or produces another
Criteria for causal inference
- temporal sequence ( X –> Y)
- proximity (in time and space)
- constant coexistence (of X and Y)
- necessary connection (between X and Y)
Views towards causation
- probabilistic view
- deterministic view
Probabilistic view on causation
- when the values of an independent variable increase or decrease, this usually results in the values of the dependent variable increasing or decreasing
- cause as a probability raiser
= X sometimes effects Y
Deterministic view on causation
- when the values of an independent variable increase or decrease, this always results in the values of the dependent variable increasing or decreasing
- explanatory factors are (potentially) necessary and/or sufficient conditions for an outcome
= X always effects Y
Predictive research
Predictions about the future through identifying future patterns. Oriented towards elections. Not focused on in the course.
What is research design?
A logical plan for getting from the research question to the answer.
Not just about how you do the research, but also why and what the purpose of doing this is.
Answers the question of how are you going to conduct the research
Choosing a research design
Make decisions based on the theory out there
In explanatory research, you have to think about the variation you want to explain
The level of analysis
The type of data you have to collect to answer your research question
If there’s a probabilistic or deterministic causational perspective
Think about the choice of methods
Theory testing
- Start with describing/analysing the theory.
- Hypotheses/propositions
- Measurement/sampling etc.
- Data collection
- Data analysis
- Either the data analysis makes it so there’s implications for the hypothesis, so you confirm/reject the theory. When you reject the theory, a new theory is needed.
Deductive (when theory testing and theory building, abductive)
Theory building
When there is not much or no theory to build on.
1. Start with data collection
2. Data analysis
3. Implications for hypothesis or new theory
4. When implications for hypothesis, it results in a theory
5. When new theory is needed, a new hypothesis is needed and then you go through the process of measurement, data collection, data analysis and implications for hypothesis again and making a hypothesis in the end.
Inductive (when theory testing and theory building, abductive)
Type of research design
Single case, short time and short space (one case in time)
Comparative case study (different case studies at a specific time point)
Periodisation study (one case across different points in time)
Necessary condition in deterministic perspective towards causation
Something which must be present for something else to be possible.
