Research Design Midterm Summary, including 24 mock exam questions and the required additional articles!
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Course
Research Design (6454753BY)
Institution
Universiteit Leiden (UL)
Book
Research Design in Political Science
This document is a summary of the book that follows the reading guide provided by the lecturers. Add the end there are 24 mock exam questions based on the previous exams given. It also includes a summary of the three additional articles we need to know for the midterm exam.
conceptualization and operationalization of resear
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Chapter 1: Introduction
What is research?
→ Research is about providing answers to questions.
● Research design ensures that the answers we provide are as valid as possible and are discovered as efficiently as
possible.
● Research design is an evolving set of rules, recommendations, theoretical starting points, and practical
considerations.
What is science?
→ The definition of science needs two crucial aspects: publicness and adherence to the scientific method
● Publicness:
○ Research is public and open to scrutiny
○ Funded by the public for the common good
● Scientific method:
○ Agreed upon points:
1. Empirical puzzles and substantive problems are as common starting points for research work
as theories
2. Theory testing is not the only or most important goal of science
3. Ideas are not tossed at the first sign of empirical inadequacy
○ Normative ideal: scientists start with a theoretically motivated hypothesis with data, and proceed to
conclusions rejecting the hypothesis that fails the empirical tests.
○ Positive ideal:
Major disagreements on the scientific method
1. Between subjectivists and positivists
● Subjectivists: the essential, unique characteristic of human behavior is its subjective meaningfulness. So, social
science cannot be construed as a value-free pursuit of objective truth
○ Research should be considered with interpreting the meaning of and reflecting on the reasons for human
action.
○ Social science can only function as a radical social critique
● Positivists: social reality is not set in stone, but constructed through a variety of social processes. Social reality is
to a large degree inter-subjective.
○ Difference with radical subjectivism: social science remains possible here.
○ Social science is conceived as a quest for the discovery and explanation of the causes and mechanisms of
social phenomena.
○ Subjectivists would object that one cannot entirely separate the observation of social facts from values
and theoretical notions. However, we can still limit the influence of our values in doing research.
2. Between empiricists and scientific realists
● While subjectivists and positivists disagree on whether social reality can be studied scientifically in the first place,
empiricists and scientific realists (who would both be positioned within the positivist camp) disagree about the
ways to conduct the study.
● Empiricists reject the reality of things that can't be directly observed, like theoretical concepts and causal
structures, and often view theoretical assumptions as useful tools rather than true representations of reality.
○ Interested in successful prediction and intervention and care little about understanding the underlying
structure of the world that generates the outcomes we observe.
○ A strong line of theoretical work that develops models of political processes based on extremely
simplified assumptions about human rationality and the nature of social interactions.
○ Operates at a level close to empirical reality
● Scientific realists: strive for a deeper understanding that goes beyond the instrumental uses of scientific results.
3. Between qualitative and quantitative researchers
They are both subject to the same rules and challenges of inference, so, there is a place for and value in both. But there are
important differences in what they can achieve.
,Chapter 2: Types of Research and Research Questions
Normative and Positive Research
- What is each type
- how do the types differ
- How are the different types connected
Normative questions deal with what ought to be
● Examines the moral order of the world
● Seeks o find answers to what is good, are about ethics,
values and value judgements
● Prescriptive
Positive questions deal with that is, used to be, or will be
● Focus on describing the reality as it is.
● Results from positive research bear on normative issues.
● Descriptive
→ In summary, positive research is usually motivated by normative
concerns and its results contribute to ethical discussions, but in itself
it is (or at least should be) free from subjective, value-ridden
judgements and biases.
Difference
● The fundamental distinction between normative and positive social science is rooted in the value/fact paradox.
● Normative and positive theoretical research share a lot, but the difference in their goals renders them distinct.
While all normative research is necessarily theoretical, the reverse is not true: theory can attempt to reconstruct the
world as it is, not as it ought to be.
Theoretical and Empirical Research
Positive theoretical research
● Positive political theory deals with the analysis of the properties of abstract concepts.
● Unlike normative political theory it does not attach any moral values to these abstract concepts.
Explanatory empirical research
● Theory development sits between the
inductive generation of initial theoretical
ideas from empirical patterns and the
deployment of theoretical propositions to
test the theory against new data and to
apply the theory to explain individual
cases and discover novel patterns in the
empirical world
● Theoretical analysis pursues the
implications of a number of premises to
their logical end.
● The logic is deductive.
● The aim is to derive a set of statements (also called propositions) that are internally consistent and logically related
with each other.
● The truth of theoretical statements is of a different nature from the truth of statements with empirical content.
● If the premises are true, the conclusions are true, provided that the rules of logic are respected when deriving the
conclusions from the premises.
Descriptive, Predictive, and Explanatory Research
Description
● Attempts to describe the world as it is, but goes beyond simple fact collecting.
● It lists the attributes of an item and assigns facts into classes or categories.
● Relies on inference, making valid statements about the whole from observing only a part.
● Descriptive research aims to describe cases or populations in various ways, using methods like historical analysis,
participant observation, or surveys.
, ● It can focus on detailed descriptions of one case or analyze multiple
cases by sorting, ranking, or exploring a few specific traits.
● Scientific description looks at patterns and connections within data,
compares traits across groups, and finds links between social events
and phenomena.
● No causality is implied.
● Good descriptive research sparks ideas, inspiring theories and deeper
explanations.
● Exploration is an advanced form of description that playfully seeks
possible explanations, less structured than theory-building. This
creative data exploration is often underrated in research methods.
Prediction
● What description does for the present, prediction does for the future.
● Involves projections about empirical phenomena and processes in the future.
● Causal explanations usually include predictions, but predictions don’t always rely on causal models, they can be
purely data-driven.
● All causal explanations imply predictions.
● Even with strong explanatory theories, prediction can still be difficult or impossible due to various factors.
○ Fundamental indeterminacy limits precise predictions. Even if we understand the reasons for actions,
predictions may still be only uncertain.
○ Sometimes, even with a good understanding of causal mechanisms, prediction remains challenging.
○ Even with a solid explanatory model, prediction may fail if key factors are unpredictable or
unmeasurable.
● However, in general, causal theories entail predictions, otherwise it is difficult to see how they differ from
description.
● The rise of big data in politics and governance makes the distinction between prediction and explanation crucial.
With more data, we must clarify our goals: are we aiming for accurate predictions or seeking to understand deep
causal structures? These goals may require different research designs and choices of variables.
Explanation
● Points to the cause of events, identifies general causal effects, and reveals the causal mechanisms that produce
them.
● Goal is to explain a particular case, to build a comprehensive causal account of a general phenomenon, or to
establish a general causal relationship that holds for a population of cases.
● Complete explanatory theories attempt to provide a complete account of how and why certain events happen.
○ Questions about impact and effects are always explanatory, even when they refer to the future and even
when the cause being evaluated is not under human control.
○ Interpretation can be regarded as a specific form of explanation; one that deals with the application of
theory and careful observation to particular cases with the aim of understanding
○ The basic rules of inference are an extension of general logic and apply across all social-scientific fields,
not just one specific area.
● Partial explanations focus on the relationships between two phenomena without the ambition to explain in full
any of them.
● Difference between explanation and description/prediction
○ Differs because it offers a deep understanding of causal structures, enabling manipulation and control.
By identifying causal mechanisms, we gain the ability to change outcomes, which is what makes
explanatory research more powerful than simply describing or predicting events.
Theory Generation, Theory Testing, and Theory Application
Theory generation
● Theory generation often starts with descriptive research, where detailed documentation of events leads to ideas
about how they are causally related.
● This process is inductive, building theories from empirical data.
● Theory generation can use various methods, like case studies or statistical analysis.
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