Case study methodology – small-n research designs

Prof. Derek Beach, University of Aarhus, Denmark

The aim of this course is to provide students with a set of methodological tools that enable the use of case study methods in your own research. A constant theme throughout the course will be on debating the strengths and limitations of different small-n methods, illustrating the types and scopes of inferences that are possible, and whether and how they can be nested into mixed-methods research designs.

The course starts by introducing the debate on whether there is a divide between quantitative, large-n and qualitative, small-n methods. This is followed by a session on working with concepts, case selection principles in different case study methods, and discussions about causal inferences and causal relationships in small-n methods. The course then turns to individual case study methods, including cross-case, comparative research designs (Mill’s methods, structured-focused comparisons and typological theorization) and within-case methods (congruence/pattern-matching, and process tracing). Particular emphasis will be given to process tracing methods. In the final part of the course, we debate when and how different small-n methods can be nested into mixed-methods designs.

The course is designed for students in the early to mid-stages of the dissertation who have already defined their research question. The final exercise will be the production of a 5-7 page research design paper.

Course prerequisites: each student should arrive at the summer school with a 2-3 page description of their research question and tentative research design which will be presented and discussed during the course.

Comparative research designs and comparative methods

Prof. Dirk Berg-Schlosser, Philipps University Marburg.

Emile Durkheim, one of the founders of modern empirical social science once stated that the comparative method is the only one which suits the social sciences. But Descartes already had reminded us that “comparaison n’est pas raison”, i. e. comparison is not reason (or theory) by itself. This course provides an introduction and overview of systematic comparative analyses in the social sciences and shows how to employ this method for constructive explanation and theory-building. It begins with comparisons of very few cases and specific “most similar” and “most different” research designs. A major part is then devoted to the often occurring situation of dealing with a small number of highly complex cases, e.g., when comparing Latin American political systems or particular policy areas. In response to this complexity, new approaches and software have been developed in recent years (“Qualitative Comparative Analysis”, QCA, and related methods). These procedures are able to reduce complexity and to arrive at “configurational” solutions based on set theory and Boolean algebra, which are more meaningful in this context than the usual broad-based statistical methods. In a last section more common statistical comparative methods at the macro-level of states or societies are presented and the respective strengths and weaknesses discussed. Participants are strongly encouraged to present their own research problems and data, if available. Some basic quantitative or qualitative methodological training is probably useful to get more out of the course, but participants with little methodological training should find no major obstacles to follow.

Game Theory and Applications for Political Science

Prof. Peter Rosendorff, New York University

Game theory is used to study strategic interactions. Whenever the choices made by two or more individuals have an effect on each other’s gains or losses, and hence their actions, the interaction between them is game-theoretic in nature. Game theory can be applied in a wide variety of political settings. For instance, in elections, the policy platforms selected by political candidates are a strategic choice that can bear heavily on the candidates’ outcomes—winning or losing. In international relations, the decision to join an international agreement or alliance is a strategic choice that can have an impact on matters of war and peace, trade and investment, and many other cross-border interactions. Because much of politics is about the allocation of scarce goods, such as power and wealth, and the competition for these goods, much of politics would seem to be a natural fit for the language of game theory. The basic objectives of this course are twofold. First, it introduces the basic concepts of elementary non-cooperative game theory in a way that allows them to be used in solving simple problems. Second, it gives a flavor of how game theory can be used in the study of political science by presenting a wide array of example applications.

Knowing and the Known: Philosophical Foundations of the Social Sciences

Prof. Patrick Jackson, American University

[Please note that this is a one week course. It will run from January 28 to February 1st]

The social sciences have long been concerned with the epistemic status of their empirical claims. Unlike in the natural sciences, where an evident record of practical success tends to make the exploration of such philosophical issues a narrowly specialized endeavour, in the social sciences, differences between the philosophies of science underpinning the empirical work of varied researchers produces important and evident differences in the kind of social-scientific work that they do. Philosophy of science issues are, in this way, closer to the surface of social-scientific research, and part of being a competent social scientist involves coming to terms with and developing a position on those issues. This course will provide a survey of important authors and themes in the philosophy of the social sciences, concentrating in particular on the relationship of the mind to the social world and on the relationship between knowledge and experience; students will have ample opportunities to draw out the implications of different stances on these issues for their concrete empirical research.

Refresher Course in Mathematics and Statistics

Prof. Lorena G. Barberia, University of São Paulo; Prof. Glauco Peres da Silva, University of São Paulo and FECAP

[Please note that this is a one week course. It will run from January 21 to 24]

This course is designed for students who require basic training in mathematical concepts and statistics, which are essential for understanding formal and quantitative analysis in political science research. It prepares students for the courses offered in the IPSA Summer School 2013.  The course will take place in the week preceding the commencement of all other courses in the Summer School. It will cover topics including calculus, linear algebra, and probability theory. To complement lectures, students will apply the concepts taught in lectures to analyze problems using the mathematical and software packages commonly used in quantitative social science research including Stata and R, as well as complete exercises.   The course presumes students have basic notions of arithmetic and algebra operations.

Quantitative Methods for Public Policy Analysis

Prof. Bruno Cautrès, Sciences Po, Paris.

This course presents the major multivariate statistical methods that are fundamental to read advanced journals and to use statistics in the field of public policy analysis and public administration. Many specialists of public policy and administration, government/governance analysis need quantitative methods for an evaluation of their actions and choices, and policy implementations. A key problem is to show the net (positive) impact of such policy changes and implementations. A policy expert that cannot prove this is in a bad position…The course aims to provide participants with a toolbox that may save their career…! The aim is to make participants effective users of modern statistical tools in analyzing public policy problems and to make diagnostics of policy implementations. This interaction between statistics, policy analysis and decision making will be highlighted. The course has two major objectives: a) to make the participants experts of basic multivariate statistical methods (like regression analysis, factor analysis); b) from there to cover recent extensions especially designed for public policy analysis. These extensions are very important for policy evaluations which aim at establishing causal links between interventions and outcomes: randomized evaluations, natural experiments, the regression discontinuity design, instrumental variables, difference-in-differences regression. These methods permit the analysts and practioners of public policies to assess the net impact of policy choices and implementations in a typical before/after quasi-experimental framework. The temporal change perspective will be privileged even if the methods can be applied to cross-national analysis as well. The course presents these methods in simple terms, the level of mathematical presentation is very basic since the use and practical understanding is the fundamental objective. Case studies will be available through a selection of papers and applications. The first week will cover the basic tools of multivariate statistics; the second week will cover the specialized multivariate tools for public policy analysis. The course is introductory, some basic previous  knowledge of descriptive statistics and statistical inference (up to hypothesis testing) may be helpful.

Quantitative textual analysis

Pror. Iñaki Sagarzazu, Nuffield College, University of Oxford

The information age has made readily available large quantities of politically relevant texts (such as politicians' speeches, laws, news, campaign ads and material). Given all this information political scientists have looked for ways to use and analyze these documents in our attempt to understand and explain political behavior. This course is intended to review, understand, and apply methods which systematically extract information from texts by turning this information into data for analytical purposes. It starts with traditional methods of content analysis -such as hand coding documents- but quickly moves to the most recent advances in quantitative content analysis. The course will also discuss issues associated with textual analysis, such as research design, reliability, validity, generalizability, and subjectivity. Lessons will consist of a mixture of theoretical grounding in content analysis approaches and techniques where we will discuss the different topics at hand and learn different analysis techniques. This theoretical component will also be accompanied by hands-on analysis of real texts using content analytic and statistical software. We will work from day one in understanding how the quantitative text analysis process works from getting texts, cleaning them, and turning them into measures of interest. For, both theory and practice, classes students are encouraged to bring their data and to discuss their research ideas and problems

Techniques for analyzing public opinion for political and economic decision makers

Prof. Clifford Young, IPSOS-Washington [Ph.D., University of Chicago]

[Please note that this is a one-week course. It will run from February 4th to 8th]

Public opinion is sacrosanct in any democracy, but is public opinion a leading or lagging indicator? Indeed, it both determines who will govern as well as helps determine which policies will be more and less likely to succeed. At a political level, pundits and politicians often see public opinion as highly ephemeral—easily influenced when the right messages are pushed. At a public policy level, policy makers often focus on the most 'technically sweet' policy solution, without considering what public opinion thinks. Contrary to these beliefs, however, both practice and scientific evidence show that public opinion is a stable, measurable, and, ultimately, predictable phenomenon. The course will explore this issue both conceptually and in practice. First, it will include a review and discussion of relevant literature on the subject: what is public opinion? Second, the course will include concrete case-studies exploring the uses and misuse of public opinion and polling by political and policy stakeholders. Cases will include, for example, the 2005 Brazilian vote buying scandal, the Obama Election 2008, and the US healthcare reform 2009. The final objective is to develop a critical eye when analyzing public policy and political problems. There no special statistical or mathematical requisites for this course, previous knowledge of survey research techniques in general is helpful.

Time Series and Pooled Time Series Analyses"

Prof. Guy Whitten, Texas A&M University

The course is an applied course focusing on the techniques for testing theories with time series and pooled time series data. Students will learn the theory and practical application of a wide range of techniques for making theoretical inferences about data with dynamic and pooled dynamic structures.  While both types of data are pervasive and highly informative, they present a unique set of challenges to applied researchers.  This course is designed to present clear explanations of these challenges and a series of strategies for how to overcome them.  A strong emphasis will also be placed on strategies for presenting the results from such models through simulation-based graphics, tables, and effective writing.

Voting in Legislatures: From Theory to Data Analysis

Prof. Simon Jackman, Stanford University

For over fifty years, the spatial model of voting has supported scholarship on voting and decision making in legislative and judicial institutions.  More recently, political scientists have used the spatial voting model in analyses of data from surveys of mass publics. This course is about the connections between the spatial voting model and data.   How does one take voting data from a legislative body and estimate ideal points? How does one take a battery of issue questions on a survey and estimate respondents' locations in a low-dimensional, latent issue space? Given estimates of ideal points in such a space, how does one use them in other models?  How can one operationalize extensions and elaborations of the spatial voting model?  Students will learn how to (a) operationalize the spatial model in their own research; (b) operationalize extensions of the spatial model in analyses of legislative behavior and surveys of mass publics; and (c) assess model performance and fit. Please note that the first week of lectures and laboratory sessions will be taught by Prof. Ricardo Ceneviva.