9798913 - RESEARCH DESIGN IN POLITICAL SCIENCE

General Objectives.
1. Knowledge and understanding. The course provides the background necessary to design original, rigorous research aimed at addressing questions related to political and social science. It pays special attention to the opportunities provided by innovative data sources and the recent turn towards computational social science.

2. Making judgements: in addition to learning to design rigorous research, students will also learn how to critically evaluate existing research and causal claims in general.

More in general, this course aims to foster interest in research, as well as do stimulate the development of a ‘research mindset’ in participants.

Synthetic General Description.

This course focuses on the methods for designing political (and social) science research, with a special emphasis on innovative data and data science methods. We will discuss how to design a political science research project, from asking good research questions to selecting data sources, formulating and testing hypotheses, and evaluating findings.

Throughout the course, you will be expected to pick up your topic and develop your own research project. Rather than on analytical techniques or statistical modelling, the emphasis will be on developing the ability to design, carry out and evaluate research that exploits the potential of big data and data science to understand political and social problems.

Expected Learning Results

- Knowledge and understanding. By the end of the course, students will be able to identify theories, hypotheses, and methods used in empirical political science research. They will understand how big data and data science can contribute to the understanding of political and social problems and dynamics;

- Applying knowledge and understanding. By the end of the course, students will apply different methods to political science research questions. They will be able to design and carry out a research project that uses innovative data for understanding, describing, real-time monitoring and/or forecasting of political and social behaviour;

- Making judgments. By the end of the course, students will be able to analyze data to measure concepts, make comparisons, and draw inferences. They will be able to understand suitable and appropriate methodologies and designs for political and social science research.

- Communication skills. By the end of the course, students will learn how to communicate political science concepts, theories, and methods in writing. They will also be able to present their research projects, findings and implications in front of an audience. 

- Learning skills. By the end of the course, students will learn how to recognise the most suitable method(s) for addressing research questions with the use of big data and data science methods. 

1. Background (2 CFU)

Introduction to the scientific study of politics (and to the course). What does political (or social) science mean. Approaching politics scientifically. Contents and structure of the course. 

Readings:

·        Kellstedt and Whitten 2018 chapter 1

·        Toshkov 2016 chapter 1

   1.1. Research questions and theory

Asking good research questions. Why bother with research questions. Types of research questions. Why good research questions are so important to good science, and how to formulate good research questions. 

Readings:

·        Toshkov 2016 chapter 2. 

·        Huntington-Klein 2022 chapter 2

Further readings:

·        Roberts Clark, W. (2020) “Asking interesting questions”, in Curini and Franzese, eds. The Sage Handbook of Research Methods in Political Science and International Relations. Thousand Oaks: Sage Pubns Ltd.

·        McCauley A and Ruggeri A (2020) “From Questions and Puzzles to Research Projects” , in Curini and Franzese, eds. The Sage Handbook of Research Methods in Political Science and International Relations. Thousand Oaks: Sage Pubns Ltd.

Optional topic: Literature review. Finding, selecting, assessing, organising and presenting science – ‘without getting buried in it’! 

Readings:

·        Knopf, J.W. (2006) ‘Doing a Literature Review’, PS: Political Science & Politics, 39(1), pp. 127–132. doi:10.1017/S1049096506060264.

·        Slides + selective readings to be analysed and discussed in class. 

Theory. The function of theory in social science – and the difference with theories in natural science. Paradigms, frameworks, theories and models. Developing theories. Assessing theories.  

Readings:

·        Toshkov 2016 chapter 3

·        Kellstedt and Whitten 2018 chapter 2

Further readings:

·        Roberts Clark, W. (2020) “Asking interesting questions”, in Curini and Franzese, eds. The Sage Handbook of Research Methods in Political Science and International Relations. Thousand Oaks: Sage Pubns Ltd.

·        Kellstedt and Whitten 2018 chapter 2

   1.2. Concepts, measurement, description

Concepts and operationalisation. The role of concepts in social science, and the challenge of concept definition. Operationalising concepts to make them measurable. 

Readings:

·        Toshkov 2016 chapter 4. 

Further readings:

·        Gerring 2012 chapter 5

Measuring and describing variables. Measurement strategies and descriptive inference. 

Readings:

·        Toshkov 2016 chapter 5

·        Kellstedt and Whitten 2018 chapter 5

Further readings:

·        Gerring 2012 chapter 7

   1.3. Explanations and causality

Explanation and causal relations. Types of explanation: laws, probabilistic, functional, intentional and mechanistic explations. Notions of causality and causal inference. 

Readings:

·        Toshkov 2016 chapter 6

·        Huntington-Klein 2022 chapters 6, 7

Further readings:

·        Cunningham, chapter 3

·        Huntington-Klein 2022 chapters 8, 9

·        King, Keohane and Verba, selected chapters

2. Designs (2 CFU)
   2.1. Experimental designs

Experimental research designs. The basics, goals and logic of experimental research; the design of experimental research; randomised controlled trials and quasi-experiments; analysis and limitations; experiments in political science and public policy research.

Readings:

·        Toshkov 2016 chapter 7

·        Imai ch2

   2.2. Large-n designs

Large-N research designs: logic and pitfalls. Conditions and strategies for causal inference: naturals experiments, instrumental variables, mediation analysis, conditioning. Common designs for causal inference: time series, cross-sectional, panel, multilevel designs. Estimating causal effects: varieties and size of association; uncertainty and statistical significance; linearity and beyond; limited outcomes. Design: variable and case selection; levels of analysis and observation: measurement error and missing data. Use and limitations. 

Readings:

·        Toshkov 2016 chapter 8

Further readings:

·        Huntington-Klein chapters 14-20

·        Cunningham chapters 4-10

   2.3. Comparative designs, case studies, mixed methods

Comparative designs: logic and types of small-n comparative research, most similar/most different designs; qualitative comparative analysis; use and limitations. 

Readings:

·        Toshkov 2016 chapter 9, 10

Further readings:

·        King, Keohane and Verba, selected chapters

Case studies: selecting evidence to observe; conducting case studies research; use and limitations. Mixed and nested designs: selecting and using cases in mixed and nested designs; use and limitations.

Readings:

·        Toshkov 2016 chapter 10, 11

·        King Keohane and Verba?

3. Practical applications (2 CFU)
   2.1. Student workshops.

Presentation of research topics/questions; presentation of data sources and preliminary design for research design proposal.

   2.2. Research design lab.

Exercises with practical applications of research design techniques.

Toshkov, Dimiter. 2016. Research Design in Political Science. 1st ed. 2016 edizione. London New York, NY: Palgrave.

Supplementary texts

Cunningham, Scott. 2021. Causal Inference: The Mixtape. New Haven ; London: Yale University Press.

Curini, Luigi, and Robert Franzese, eds. 2020. The Sage Handbook of Research Methods in Political Science and International Relations. 1° edizione. Thousand Oaks: Sage Pubns Ltd.

Huntington-Klein, Nick. 2021. The Effect. An Introduction to Research Design and Causality. https://nickchk.com/causalitybook.html; https://github.com/NickCH-K/causalbook; https://github.com/NickCH-K/causaldata; https://nickchk.com/videos.html.

Imai, Kosuke. 2017. Quantitative Social Science: An Introduction. Princeton: Princeton Univ Pr.

Kellstedt, Paul M., and Guy D. Whitten. 2018. ‘The Fundamentals of Political Science Research’. Cam

ASSESSMENT

The assessment is based on class participation and presentations (30%), final paper (40%), and final exam (30%). 

The final paper is a 3000-5000 word original research design proposal carried out by applying course materials. The research design proposal should include the basic elements of an original research project, namely a research question, theoretical contribution, testable hypotheses, and a description of the proposed data collection and analysis. This paper should focus narrowly on a topic of the student’s choice and display a greater depth of understanding of a smaller set of ideas raised in the course. More information in a separate document (see “Student deliverables”).

Students should work on the research design proposal over the full term. Indeed two seminars – the student workshops – will include students’ presentations of 1) possible research topics/questions, literature review and hypotheses (around the tenth seminar); 3) data and preliminary design (around the end of the course). 

The final exam focuses largely on an oral discussion of the research design proposal, that will (also) be used as a basis for discussing the key topics of the course. 

Grading scale:

•    Failed: the student does not know the basic concept of the course and has completed less than 40% of the required assignemnts

•    18-20: the student has a basic knowledge of the topics of the course but he has great difficulties in applying them to practical exercises and problem solving pipelines.

•    21-24: the student has a basic knowledge of the topics of the course and he is able to solve simple problems and exercises with some guidance from the teacher.

•    25-27: the student has a good knowledge of the topics of the course and can complete the assignment in autonomy with minor errors

•    28-30 e lode: The student has full knowledge of the topics of the course and is able to complete in autonomy assignments making connections and with only very minimal occasional mistakes.

Illustrate the distinctive features of your research design proposal.

Motivate the choice of your research design.

Explain how you operationalise the concepts in proposals.

If you are going to propose and test a theory, discuss it. Otherwise explain why you will not design your own theory of the problem at hand.

Explain the logic behind case selection in your proposal