Lecture 4 - Research Design

Aim of research = to expand boundaries of existing knowledge (whether foundational or contextual or interpretative) - to advance the state of the art (state of existing knowledge, scientific literature) Getting valid answers to research questions in a reliable and efficient way applied (operational) epistemology - how do we know

mainly focuses on positivist research design no mention of normative theory little attention to interpretivist theory (because this is much more difficult: there are more varieties than in the precise considerations of positivist research design)

level • ontological, epistemological, broad theoretical outlook (is often implicit, but we should try to be explicit about the nature of the world and what we can know about it) • research goal and question, relation with theory, conceptualization, operationalization, class of research methodology (qualitative, quantitative, both) *this step is always explicit • cases, variables, evidence with level 2 we can deduct the ontological epistemological approach

• puzzles and lacunae • substantive problems • practical problems (government can ask questions to political scientists, to figure out the reason for a problem)

• scientific description • causal explanation and prediction (= aka positivist) • understanding/interpretation (=aka interpretative) combinations is possible, but remember that epistemology and ontology are skins, not sweaters

often not considered to be scientifically significant • analytical dimensions (concepts) often theoretically informed (every researcher, whatever approach, must conceptualize) • implications for both empirical and normative theory Two different types of description - comprehensive representation of one/few case(s) (many variables, often unique(ish) phenomena) - classification of many cases

description goes with classification ‘the value of scientific description is in selecting a set of important analytical dimensions on which to project the empirical world’ (LMS) classification is an important first step in theory classifications are imposed on the social/political world classifications can’t be neutral, they are embedded in the way we understand the world, into our own understanding and education, this element of constructivism, is correct for all approaches: classifications and categories don’t exist in the world difference between foundationalist and anti-foundationalist approaches to classifications don’t have to do with linguistic classifications, the difference is the things they classify: for anti-foundationalists the things they classify themselves are unstable, for foundationalism the things exist independently in the world foundationalism: no matter what you call it, you mean the same thing

associated with foundationalist ontologies (behaviouralist, rational choice, Marxist and institutionalist approacches) two types: - retrospective - prospective

often phrased in probablistic language instead of deterministic namely: if the cause had been different, the outcome would have been different they claim to be able to say … leads to it being more/less likely that …. requires large statistical research

associated with anti-foundationalist ontologies reject the scientific concept of causation political actions are the product of subjective reasons, meanings and beliefs -> we should : • focus on understanding rather than explanation (Weber) • focus on constitutive rather than causal arguments (Wendt)

theory comes to play in lots of different moments (e.g. research questions, choosing methodologies, making conclusions, conceptualisation) - text doesn’t highlight this enough according to Theuns 6 types of engagement of research projects - mere application of theory - abductive explanations (back and forth between theoretical standardizations and observations, kind of middle ground between induction and deduction) - theory development (deductive from premises to propositions) - theory generation (inductive: patterns in empirical data) - hypothesis testing

IT IS NOT ONE STEP IN THE RESEARCH PROCES formulating a research question is prior to choosing a research methodology, but the choice is not holy shaped by the question (also by e.g. epistemological and ontological position)

we can’t always mimic laboraty experiments in PS - phenomena are to complex to mimic in a lab (e.g. Trumpism) even if we could, we probably shouldn’t create experiments of creating societies because of ethical reasons (e.g. you can’t just pick to groups and expose them to dire conditions) we often try to work with observational designs (no choice of treatment group and control group)o

researchers in social sciences can’t always assign treatment and control groups researchers can control: • which cases to study • how may cases to observe • which features of the cases to observe • what (sort of) data to collect (qualitative or quantitative)

• concepts are abstract and unobservable (until you have operationalized it and found indicators) • they are contested • concepts must be judged by the usefulness and coherence • once we have a concept we try to operationalize it • concepts can have an inductive element (how the terms are used in politics/by people) but this has limits how to conceptualize is also contested

how many cases? large-N or small-N - can be applied to all approaches to some degree, generally speaking the discussion is mostly about positivist design positivists: the more cases, the better

population = individuals, countries, years, written content - population determines how many cases are possible to study ideal = randomly selected sample of cases (to be as representative as possible) practice = often convenience samples, purposive sampling (choosing for a specific feature of a group) research doesn’t control ‘treatment’ variation - try to measure, and thus rule out, all potentially confounding variables (which to select = based on theory) - try to measure, and thus observe, causal mechanisms

comparative politics = not the same as the comparative method comparative method tends to focus on small-N case studies it is the go-to method for much empirical political science when: - experimental methods aren’t possible (no control) - statistical methods are not possible (not enough data/cases)

we try to find different cases (small-N) that share a feature that we try to understand or explain, but are for the rest really different

cases that are very similar, but have a different outcome we want to explain how similar cases differ in one variable

• there are a small number of cases (not really confident in hypotheses) • methods are sensitive to case selection / variable specification (possible selecting cases to fit theory, instead of to test theory) • difficult to analyse the interactions between variables

not enough cases, inadequate prior knowledge of relevant variables -> single case studies comparison still important: compare to concepts, compare to wider universe of cases and concepts case studies are often descriptive, but: • contribute to general knowledge • contribute to theory generating and theory testing (new phenomena can lead to new knowledge and ideas of theories) • important steps in general approach to further understanding and explanation