IPSA-USP 2017 Summer School Course Descriptions

Basics of Causal Case Study Methods
Derek Beach, University of Aarhus

COURSE DESCRIPTION
The aim of this course is to provide students with a good understanding of case-based methods and how they can be applied in your own research. This introductory course is designed for students in the early to mid-stages of a research project, where you have already defined your research question and are interested in learning about what case-based methods can offer. Some knowledge of basic social science methodology is helpful, although not required.
The focus will be on methods like small-n comparisons and in-depth case studies using Process-tracing. The core of the readings will be a forthcoming book on causal case studies co-authored by the instructor.
For those of you considering enrolling in this course, watch the video below to find out more!
DATES
This course runs January 23-27, 2017.
TEACHING FELLOW: Natália Calfat
COURSE OUTLINE
The course covers the following topics:
DAY 1 - WHAT ARE CASE BASED METHODS, AND HOW DO THEY DIFFER FROM VARIANCE-BASED METHODS?
King, Gary, Robert O. Keohane & Sidney Verba (1994), Designing Social Inquiry. Scientific Inference in Qualitative Research, Princeton, New Jersey: Princeton University press, Chapter 3, pp. 75-114
Mahoney, James and Gary Goertz (2006), “A Tale of Two Cultures”, Political Analysis, vol. 14, no. 3, p. 227-249.
Beach and Pedersen (forthcoming) Causal Case Studies. Under contract at the University of Michigan Press, Chapter 1.
DAY 2 - WORKING WITH CONCEPTS AND THEORIES IN CASE-BASED RESEARCH
Goertz and Mahoney (2012) A Tale of Two Cultures. Princeton: Princeton University Press, Chapter 11, 12, 13, pp. 139-160.
Beach and Pedersen (forthcoming) Causal Case Studies. Under contract at the University of Michigan Press, Chapter 2, 3.
DAY 3 - COMPARATIVE METHOD
Beach and Pedersen (forthcoming) Causal Case Studies. Under contract at the University of Michigan Press, Chapter 7.
Risse-Kappen, Thomas (1991) ‘Public Opinion, Domestic Structure, and Foreign Policy in Liberal Democracies.’, World Politics, Vol. 43, No. 4, pp. 479-512.
DAY 4 - CASE STUDIES
Beach and Pedersen (forthcoming) Causal Case Studies. Under contract at the University of Michigan Press, Chapter 8, 9.
DAY 5 - MAKING INFERENCES IN CASE-BASED RESEARCH
Beach and Pedersen (forthcoming) Causal Case Studies. Under contract at the University of Michigan Press, Chapter 5, 6 (on causal inference and Bayesian framework)
Doyle, Arthur Connan (1894) Silver Blaze can be downloaded free at:  http://www.wesjones.com/doyle1.htm
A constant theme throughout the course will be on debating the strengths and limitations of different case-based methods, illustrating the types and scopes of inferences that are possible, and how they differ from what variance-based methods enable.
The course will contain lectures and discussions in the morning sessions, followed by group exercises in the afternoons. Many of the exercises will utilize aspects of your own research projects.
PREREQUISITES
Some familiarity with the basics of research design and methods at the BA/MA level is preferable (though not strictly required). If no prior courses have been taken, please familiarize yourself with the basics using a book like ‘Research Methods in Politics’ (Burnham et al) (Palgrave).

Building Parametric Statistical Models
Glauco Peres da Silva, University of São Paulo
COURSE DESCRIPTION
In this week long course, we will learn how to build a statistical model that captures the essential features of the process that may have generated one's observed data. Building such a model is the first step in any data analysis that will rely on parametric modelling (including for example, likelihood or Bayesian models). We will integrate a large number of common models (e.g., duration models, count models, dichotomous DV models, regression models, models of ordered data) into a single conceptual framework that will allow students to build their own new models if necessary to capture important features of the process that generated their data. We will also include a brief introduction to estimating the parameters of these models via maximum likelihood, so the week will be (somewhat) self-contained.
DATES
This course runs January 23-27, 2017.
TEACHING FELLOW: Matheus Hardt, University of São Paulo
COURSE OUTLINE
1. Overview of building a statistical model and probability distribution
2. Basics mathematical notation and functions
3. Basics of Calculus - Derivatives
4. Basics of probability distribution
5. Brief Introduction to Maximum Likelihood Estimation

Designing Feasible Research Projects in Political Science
COURSE DESCRIPTION
This course introduces students to basic principles in the design of political science research.  The goal is to help guide students in the design research projects that can be feasibly completed in BA, MA, or PhD theses or in an article manuscript.  The course covers three basic topics.  It begins with how to choose a research question or topic and how to set this topic within the relevant scholarly literature.  It then covers the construction of causal arguments and the identification of these arguments’ testable expectations and implications that can be verified empirically.  Included in this section is a discussion of the importance of identifying null hypotheses and alternative arguments.  The course finishes by discussing empirical strategies for testing arguments, including case selection and the different types of qualitative and quantitative data available to researchers.  Included in this section is a discussion of the identification of variables and their appropriate measures, and concept clarification and measurement.  This course does not delve into the analysis of qualitative or quantitative empirical evidence, as this is covered in other courses in the summer program.
DATES: This course runs January 23-27, 2017.
TEACHING FELLOW: Hellen Guicheney
COURSE OUTLINE: 
1.      Research Questions: Identifying Research Questions; Indentifying (and Organizing) the Relevant Scholarly Literature
2.      Constructing Arguments: Causality and Causal Mechanisms; Hypotheses and Testable Expectations/Implications of the Argument
3.      Empirical Strategy: Concept Clarification and Measurement; The Use of Counterfactuals; Case Selection
4.      Data Types: Qualitative (Interviews & Archives); Qualitative/Quantitative Observational; Quantitative Experimental (Natural Expermiments/Scientific Expermiments)
PREREQUISITES:
Students should be prepared either to develop a new or improve an existing research proposal or to discuss a current research project (usually, article length) that is underway.  Each day, students should be prepared to discuss progress on their own projects in light of the particular topic of the class.  In addition to course readings, students will be expected to read and discuss at least one award-winning conference paper and one award-winning PhD dissertation (a list of possible papers and theses will be given to the students).  To ensure progress on their projects, students will be required to present written and oral advances each day. 

Essentials of Applied Data Analysis
Leonardo S. Barone
COURSE DESCRIPTION
This course is designed for students who are interested in reviewing their training in statistics. It prepares students for courses offered in the IPSA-USP Summer School that require statistical training.  It reviews basic probability; random variables and their distributions; confidence intervals and tests of hypotheses for means, variances, and proportions from one or two populations. To complement lectures, students apply the concepts taught in lectures to analyze problems using Excel and Stata.
DATES
This course runs January 23-27, 2017.
TEACHING FELLOW: Victor Araújo, University of São Paulo
COURSE OUTLINE
This course departs from the premise that the most effective way to learn statistics is by actively engaging in doing the statistical analysis. For each topic, we will have lectures that will be followed by sessions in which students will use data to answer questions that are important to political scientists. Although the goals of the class are primarily conceptual rather than narrowly mathematical, students should feel comfortable with engaging with mathematics, formulas, and data analysis.

By the end of the intensive one-week course, students should be able to apply basic concepts in probability theory to social science research questions, to make inferences about the distribution of populations based on a sample and to correctly conduct and interpret hypothesis tests. For those students who will be studying multivariate regression analysis in the Summer School, the course will provide an intuitive and basic review of linear regression in theory and practice.
PREREQUISITES
The course presumes students have some basic training in mathematics including arithmetic and algebra operations.

Methods and Problems in Political Philosophy
Herlinde Pauer-Studer, Department of Philosophy, University of Vienna
OUTLINE
This course discusses different methodological paradigms of political philosophy and their normative consequences with respect to some crucial issues in political philosophy. The main objectives of the course are: to provide an overview of normative foundations of political philosophy; to make you acquainted with different methodological approaches to central problems of political philosophy; to explore the relevance and strength of philosophical analysis of central issues in political theory and to make you familiar with current debates in political philosophy.
We will discuss the following problems: the justification of political authority; the normative foundations of ‘a rightful’ political order; conceptions of justice; freedom and autonomy; the metric of equality and distributive justice; public reason and democracy; civic responsibility under authoritarian and totalitarian political conditions. We will explore those questions by discussing relevant selections of readings, including texts by classical and contemporary philosophers.
DATES
January 23-27,2017.
TEACHING FELLOW: Lucas Petroni, University of São Paulo
DETAILED DESCRIPTION
This course discusses different methodological paradigms of political philosophy and their normative consequences. We will compare three basic models: a Hobbesian justification of political authority, a Kantian account of the normative foundations of ‘a rightful’ political order, and a Humean convention-based approach to social systems. We will then analyze and discuss conceptions of justice, freedom and equality, public reason and democracy. A further topic is global justice. Finally, we will look at civic responsibility under authoritarian and totalitarian political conditions.
Course Plan
1.  Philosophical Foundations of Society and State Authority
2. Self-Interest, Impartiality, and Rational Agreement: Conceptions of Justice
3. Freedom and Autonomy
4. The Metric of Equality and Distributive Justice
5. Equality and Freedom
6. Global Justice
7. Public Reason and Deliberative Democracy
8. Procedural Democracy and Epistemic Justice
9. Authoritarianism and Totalitarianism
10. Law, Morality, and Politics
PREREQUISITES
There are no prerequisites except the willingness to work hard reading some quite challenging philosophical material.

Modeling Dynamics
Lorena Barberia, University of São Paulo and Andrew Philips, Texas A & M
COURSE DESCRIPTION
Time series variables (e.g. presidential approval, public mood liberalism, GDP, inflation, education level) are extremely common in the social sciences. However, due to certain properties, these series cannot always be handled using standard regression approaches. This course serves as an introduction to the world of time series analysis. We will explore the properties of time series (e.g. non-independence of observations, moving averages, unit-roots), and introduce strategies to test and model these data.
During the first three days, the course will involve about three hours of lecture time with breaks, then lunch, then three to four hours of hands on instruction in analysis that takes place in smaller groups using STATA. On the fourth day, students will work on a specific project assignment that applies the concepts introduced in the course. On the final day, we will have a wrap-up lecture and discuss group presentations.  
DATES
This course runs January 23-27, 2017.
TEACHING FELLOW: Natalia Moreira, University of São Paulo
COURSE OUTLINE
Topics
1. Regression Essentials
2. Fundamental Concepts of Time Series Analysis – Non-Stationarity and Unit Roots
3. Univariate ARIMA
4.  ARIMA with RHS variables
5. Group Presentations and Closing Lecture
PREREQUISITES
A full-semester graduate-level course in multiple regression analysis. 

Advances in Modeling Dynamics
Lorena Barberia, University of São Paulo, Andrew Phillips and Guy Whitten, Texas A & M
COURSE DESCRIPTION
Time series variables (e.g. presidential approval, public mood liberalism, GDP, inflation, education level) are extremely common in the social sciences. However, due to certain properties, these series cannot always be handled using standard regression approaches.
​This five-day course is an applied intermediate-level course focusing on the techniques for testing theories with time series data. Each day the course will involve about three hours of lecture time with breaks, then lunch, then three to four hours of hands on instruction in analysis that takes place in smaller groups using STATA.
DATES
This course runs January 30-February 3, 2017.
TEACHING FELLOW: Natália Moreira, University of São Paulo  
COURSE OUTLINE
Topics
1. Time Series Regression Models
2. Cointegration and Error Correction Models
3. Vector Auto Regression and Vector Error Correction Models
4. Fractional Integration
5. Dynamic Pie Models
PREREQUISITES
A full-semester graduate-level course in multiple regression analysis and Modeling Dynamics (offered in the IPSA-USP 2017 Summer School) or the equivalent background in time series analysis.

Basics of Multi-Method Research: Integrating Case Studies and Regression
Jason Seawright, Northwestern University
COURSE DESCRIPTION
This course explores the most common family of multi-method research designs, involving combinations of regression analysis with qualitative methods. We will review the mechanics and assumptions involved in regression, as well as in common qualitative techniques including process tracing, comparative analysis, focus groups, in-depth interviews, and participant observation. We will then discuss efficient designs that use either regression or case studies to test assumptions and fill inferential gaps in a causal inference justified by the other method. Topics will include case selection, process-tracing designs for testing key regression assumptions, and designs that use regression as a step in a process-tracing argument. We will carry out practical exercises reviewing each method, and will discuss optimal designs for a variety of real-life inferential problems.
DATES
This course runs January 30-February 3, 2017.

TEACHING FELLOW: Francisco Urdinez, University of São Paulo
COURSE OUTLINE
PREREQUISITES
A solid understanding of regression analysis and basic qualitative methods. Familiarity with statistical packages such as R and/or Stata.

Basics in Quantitative Methods for Public Policy Analysis
Bruno Cautrès, Sciences Po
OUTLINE
Whether they should or they shouldn’t, numbers, data and quantitative methods matter in policy and in policy analysis. Policy and political analysts use numbers treated with quantitative methods in evidence-­based research about whether policy interventions are successful. Policymakers use numbers to support (sometimes normative) arguments about whether government should (or should not) provide particular services or engage in policy change and reforms. This course is the first course in a two­course (Module 2 and Module 3) sequence designed to teach you the quantitative methods that you need for a career in public policy and also to be able to read publications using these methods.
DATES
This course runs January 30-February 3, 2017.
TEACHING FELLOW: Garrett Vande Kamp, Texas A&M University  
DETAILED DESCRIPTION
This first module introduces participants to major basic concepts and tools for quantitative evaluation of public policies. Among the many concepts used by specialists of causal reasoning and policy evaluation, the following will be clarified : counterfactual, potential outcomes, quasi-experiment, treatment effect, before-after effect. The course will then show how statistical methods can be useful to face these problems and to create a formal framework for quasi-experimental reasoning. The basics of regression models will be presented in relationship to testing the effect of « treatment » (like a policy change or like to be exposed to political reforms). On this aspect, the course will cover regression group comparisons (treated versus control groups) through different techniques (dummies, interactions, Chow test).  At the end of the course, participants will have a clear view on the relationship between comparing treated versus control groups and the regression methods framework. 
PREREQUISITES
Basic knowledge about descriptive statistics and introduction to statistical inference tests. As the course will use Stata as the software, a background in using this software is helpful, but not required. Lab sessions will include replication of some published papers that will permit participant to acquire practical skills for working with empirical data.

Basics of Set-Theoretic Methods and Qualitative Comparative Analysis (QCA)
Carsten Schneider, Central European University
COURSE DESCRIPTION
The module starts out by familiarising students with the basic concepts of set-theoretic methods. We discuss the two fundamental subset relations of necessity and sufficiency, and introduce the basics of formal logic, set theory, and Boolean algebra. From there, we move to the logic and analysis of truth tables and discuss the most important problems that emerge when this analytic tool is used for analysing social science data. All topics will be introduced using crisp sets and later expanded to fuzzy sets. Right from the beginning, students will be exposed to performing set-theoretic analyses using the relevant R software packages. When discussing set-theoretic methods, in-class debates will further engage on broad, general comparative social research issues, such as case selection principles, concept formation, questions of data aggregation and the treatment of causally relevant notions of time. Real-life published applications are used throughout the course. Participants are encouraged to bring their own data for in-class exercises and assignments, if available.
DATES
This course runs January 30-February 3, 2017.
TEACHING FELLOW: Marcos Campos, University of São Paulo
INSTRUCTOR: Carsten Schneider, Central European University
COURSE OUTLINE
Topic 1: Set Theory
- Methodological foundations: set theory, Boolean and fuzzy algebra, formal logic
- Set operations and set relations
Topic 2. Calibration
- Measurement and calibration
- Calibration techniques
- Differences in calibration and their consequences
Topic 3. Truth Table Analysis
- From data matrix to truth table
- Analyzing truth tables
- Quine-McCluskey Algorithm
Topic 4. Parameters of Fit
- Consistency and coverage measures for necessary and sufficient conditions
- Issues related to the parameters of fit
Topic 5. Limited Diversity
- Origins of remainders
- Types of remainders
- Types of assumptions on remainders
- The Standard Analysis

Basics of Spatial Interdepence in Theory and Practice
Laron Williams, University of Missouri
COURSE DESCRIPTION
Spatial econometric models have grown in popularity in the social sciences over the last two decades, especially as scholars grapple with estimating models that match the spatial complexity of their theories. This five-day course is designed to provide students with an introduction to the use of spatial econometric models.  It prepares students to carefully theorize about these spatial processes and more effectively test theoretical expectations about patterns of spatial interdependence.
We begin the course by exploring how prominent theories of social science (e.g., policy diffusion, party competition, civil war spillovers, etc) argue that the processes of nearby or similar units are related.  In the second and third days we focus on issues of model specification that are unique to these models; this includes correctly specifying the manner in which the units (i.e., individuals, states, countries, etc) are spatially related, and whether the spatial interdependence occurs among variables, the errors, or the outcome itself.  The fourth day emphasizes how to estimate a variety of spatial econometric models.  In the final day, we explore graphical and tabular techniques to provide meaningful quantities of interest from these models.  Students are encouraged to develop their own research questions about spatial processes and to bring their own data sets.  In the afternoon sessions, students will use Stata and R to apply the concepts discussed in the lectures to their own research questions.
DATES
This course runs January 30-February 3, 2017.
LAB COORDINATOR: Daniela Schettini
COURSE OUTLINE
Day 1: Spatial interdependence in theory and practice
This introductory seminar provides an overview of the uses of spatial econometric models in the social sciences, and discusses how these models get us closer to our causal models of politically interdependent outcomes.
Day 2: Identifying neighbors
The work horse of spatial econometric models is the W matrix, which specifies the manner in which all the observations are connected.  This lecture explores the variety of options for specifying the weights matrices.  We will discuss strategies for determining the appropriate patterns of interconnectedness, whether scholars begin with a strong theoretical foundation or the desire to maximize model fit.  While most scholars focus on geographic patterns of interdependence, we discuss a range of other means of identifying how neighboring units are socially, politically and economically interdependent. 
Day 3: Specification of the spatial interdependence
There are a variety of models in the broad family of spatial econometrics, and a useful categorization divides the spatially interdependent processes based on whether the spatial interdependence occurs in the observables, unobservables and/or outcomes.  Most theories provide some insight that guides this decision; a range of tests can assert whether the theory is supported.  This lecture introduces a series of tests that will detect different patterns of spatial interdependence.  Moreover, we will explore how to connect the causal relationships in one’s theory to some basic spatial econometric models including the spatial lag, spatial error and spatial-X models.
Day 4: Estimating spatial econometric models
Even after scholars have identified spatial neighbors, detected patterns of spatial interdependence, and decided the appropriate model, they face the often daunting task of estimating the model.  We explore a variety of techniques used to estimate spatial econometric models including OLS, MLE and two-stage least squares.
Day 5: Visualizing and depicting spatial interdependence
A recent trend in the social sciences centers on providing meaningful and easy-to-interpret quantities of interest from one’s model.  This is particularly important in spatial econometric models because outcomes are often simultaneously determined with significant feedback, and/or are spatially dependent on factors occurring in nearby units.  Due to these factors, simply providing the coefficients in a table provides little insight to readers beyond the sign and statistical significance.  This lecture demonstrates how to calculate and provide visual depictions of substantive effects from a variety of spatial econometric models.
PREREQUISITES
A full-semester graduate-level course in multiple regression analysis and a background in simple mathematics and statistics (including probability distributions, random variables, and hypothesis tests).  A strong familiarity with Stata and R are helpful, though not required. 

The Philosophy of Science: Positivism and Beyond
Patrick Thaddeus Jackson, American University
COURSE DESCRIPTION
The “science question” rests more heavily on the social sciences than it does on the natural sciences, for the simple reason that the evident successes of the natural sciences in enhancing the human ability to control and manipulate the physical world stands as a powerful rejoinder to any skepticism about the scientific status of fields of inquiry like physics and biology. This course is a broad survey of epistemological, ontological, and methodological issues relevant to the production of knowledge in the social sciences, and in particular, to its “scientific” status. The course has three overlapping and interrelated objectives: to provide you with a grounding in these issues as they are conceptualized and debated by philosophers, social theorists, and intellectuals more generally; to act as an introduction to the ways in which these issues have been incorporated (sometimes— often!—inaccurately) into different branches of the social sciences; and to serve as a forum for reflection on the relationship between these issues and the concrete conduct of research, both your own and that of others. 
DATES
This course runs January 30-February 3, 2017.
TEACHING FELLOW: Gabriela Rosa, University of São Paulo

TOPICS
Seminar 1: Who needs philosophy of science, anyway?
Lecture 1: Cartesian Anxiety and the Positivist Project: the road to the Vienna Circle.
Lecture 2: Neopositivism
Seminar 2: Hypothesis-testing and cross-case comparison
Lecture 3: Critical Realism
Seminar 3: Causal powers and dispositional causation
Lecture 4: Analyticism
Seminar 4: Ideal-types and singular causal analysis
Lecture 5: Reflexivity and Critical Theory
Seminar 5: Theorizing from a point of view

Using Case-Based Methods in Practice
Derek Beach, University of Aarhus
COURSE DESCRIPTION
The aim of this course is to provide participants with a set of methodological tools that enable the use of case study methods in your own research. This course illustrates how case-based methods can be used in practice, focusing on applying methodological principles on your own research. As with the first module, the focus is on small-n comparisons and in-depth case studies using Process-tracing. The core of the readings will be a forthcoming book on causal case studies co-authored by the instructor.
After the course you should be able to use case-based methods in your own research project, and be able to evaluate the relative strengths/weaknesses and tradeoffs of using different methods in relation to your own research question.
For those of you considering enrolling in this course, watch the video below to find out more!
DATES
This course runs January 30- February 3, 2017.
TEACHING FELLOW: Natália Calfat
DETAILED OUTLINE
The course covers the following topics:
DAY 1 - WORKING WITH EVIDENCE IN CASE-BASED METHODS
Beach and Pedersen (2013) Process Tracing: Foundations and Guidelines. Ann Arbor: University of Michigan Press. Chapter 7.
Lustick (1996) ’History, Historiography and Political Science.’, APSR, 90(3), pp. 605-618.
Case material on Cuban Missile Crisis. Will be provided
DAY 2 - USING COMPARATIVE DESINGS IN PRACTICE
Schnyder (2011) 'Revisiting the Party Paradox of Finance Capitalism: Social Democratic Preferences and Corporate Governance Reforms in Switzerland, Sweden, and the Netherlands.', Comparative Political Studies, 44(2) 184–210.
DAY 3 - USING CASE STUDIES (PROCESS-TRACING) IN PRACTICE
Brast, Benjamin (2015) 'The Regional Dimension of Statebuilding Interventions.' International Peacekeeping, 22(1).
DAY 4 - CASE SELECTION AND MIXED METHODS
Geddes, Barbara (1990), “How the cases you choose affect the answers you get: selection bias in comparative politics”, Political Analysis, vol. 2, no. 1, pp. 131-150.
Collier and Mahoney (1996) ‘Insights and Pitfalls: Selection Bias in Qualitative Research’, World Politics, Vol. 49, pp. 56-91.
Lieberman (2005) ‘Nested Analysis as a Mixed-Method Strategy for Comparative Research.’, American Political Science Review, Vol. 99, No. 3, pp. 435-451.
Beach and Pedersen (forthcoming) Paper on case selection and nesting.
DAY 5 - DESIGNING CASE-BASED RESEARCH IN PRACTICE
No readings - presentations of designs
The course will contain lectures and discussions in the morning sessions, followed by group exercises in the afternoons. Many of the exercises will utilize aspects of your own research projects.
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. Module 3 is for participants who have either follow module 2 (basics of causal case studies) or have extensive experience with using case studies and/or knowledge of recent developments in case-based methods.

Advanced Issues in Multi-Method Research: Integrating Case Studies and Contemporary Methods for Causal Inference
Jason Seawright, Northwestern University
COURSE DESCRIPTION
This course extends the discussion of multi-method research designs to include contemporary tools for causal inference, involving combinations of matching methods, natural experiments, and randomized experiments with qualitative methods. We will review the mechanics and assumptions involved in each statistical tool. We will then discuss efficient designs that use case studies to test assumptions and fill inferential gaps in a causal inference justified by these methods for causal inference. For each quantitative method, we will analyze optimal case-selection strategies, discuss special process-tracing designs for testing assumptions distinctive to that method, and explore special considerations that arise in embedding that method as a step in a process-tracing argument. We will carry out practical exercises reviewing each method, and will discuss optimal designs for a variety of real-life inferential problems.
DATES
This course runs February 6-10, 2017.
TEACHING FELLOW: Francisco Urdinez, University of São Paulo
COURSE OUTLINE
PREREQUISITES
A solid understanding of regression analysis and basic qualitative methods. Students should also have familiarity with natural experiments, instrumental variables, matching and randomized experiments, as well as familiarity with statistical packages such as R and/or Stata.

Advanced Issues in Set-Theoretic Methods and Qualitative Comparative Analysis (QCA)
Carsten Schneider, Central European University
COURSE DESCRIPTION
The purpose of the advanced module is fourfold: (1) to re-visit and further practice the software implementation of the core points of QCA addressed in module 1 (calibration, tests of necessity and sufficiency, truth tables, parameters of fit); (2) to elaborate on further issues that arise when neat formal logical tools and concepts, in particular the issues of limited diversity and the challenge to make good counterfactuals on so-called logical remainders; (3) to get better acquainted with the standards of good practice, both in its fundamental aspects and in using the relevant software programmes; and (4) to spell out from a set-theoretic point of view general methodological issues, such as multi-method research, robustness tests, theory evaluation, and panel data analysis.
DATES
This course runs February 6-10, 2017.
TEACHING FELLOW: Marcos Campos, University of São Paulo
INSTRUCTOR: Carsten Schneider, Central European University
COURSE OUTLINE
Topic 1. Enhanced Standard Analysis
- How to avoid untenable assumptions on logical remainders 
Topic 2. Set-Theoretic Multi-Method Research
- How to select cases after a QCA
- How to make use of the insights gained from these (comparative) within-case analyses
Topic 3. Set-Theoretic Theory Evaluation
- How to evaluate existing theories in light of results generated with QCA
Topic 4. Robustness
-How to assess robustness in set-theoretic methods
-Robustness against what
-Simulations as one tool for robustness tests
Topic 5. Inclusion of Time Dimension
- The inclusion of time into set-theoretic analyses (panel data, causal chains etc.)

Advanced Issues in Quantitative Methods for Public Policy Analysis
Bruno Cautrès, Sciences Po
COURSE DESCRIPTION
This is the second course in a two-course sequence (Module 2 and Module 3) designed to teach you the quantitative methods that you need for a career in public policy and also to be able to read publications using these methods. By this we mean the application of statistical methods to problems in political science and public policy. 
DATES:
This course runs February 6-10, 2017.
TEACHING FELLOW: Thiago Silva, Texas A&M University
INSTRUCTOR: Bruno Cautrès, Sciences Po
COURSE OUTLINE
Building on the first course which covered basic concepts, notions and introduction to regression based reasonings, this second module provides a survey of more advanced empirical tools for political science and public policy research. The focus is on statistical methods for causal inference, i.e. methods designed to address research questions that concern the impact of some potential cause (an intervention, a change in institutions, economic conditions, or policies) on some outcome ( vote choice, income, election results, crime rates, etc).
We cover a variety of causal inference designs, including quasi-experiments, advanced regression, panel methods (fixed and random effects), difference-in-differences, instrumental variable estimation, regression discontinuity designs, quantile regression. We will analyze the strengths and weaknesses of these methods. Applications are drawn from various fields including political science, public policy, economics, and sociology.
We begin by discussing the strengths and limitations of multiple regression analysis and the relationship between regression and causal modeling.  We then develop a sequence of extensions and alternatives, including : regression discontinuity,  difference-in-differences, panel data, instrumental variables. The course will conclude with an introduction to some limited dependent variables techniques that are now common in political and policy analysis due to the categorical nature of many phenomens treated by political and policy analysis (binary and ordinal logit analysis).  
We will learn both the techniques and how to apply them using data sets. Skills students will acquire in this course include: the capacity to reason causally and empirically, the ability critically to assess empirical work, knowledge of advanced quantitative tools, and experience in working with data sets.  
PREREQUISITES
Background knowledge of multiple regression models, such as the Basics of Quantitative Methods for Public Policy Analysis course offered in Module 2, or the equivalent.  As the course will use Stata as the software, a background in using this software is helpful, but not required. Lab sessions will include replication of some published papers that will permit participant to acquire practical skills for working with empirical data. 

Analyzing Grouped Data: Multi-level Models
Randy Stevenson, Rice University 
COURSE DESCRIPTION
This course will introduce multi-level models for analyzing data with some, potentially complex, group structure (students in classrooms, schools, and states; countries and years; electoral districts in regions and years).
Such structures offer both challenges to statistical models -- they can cause complex patterns of dependence between the observations in the data -- and opportunities: by modelling this grouping structure, we can sometimes control (in a way) for variables that operate at the level of the group but that we could not measure (e.g., when one can not measure teacher quality -- which operates at the classroom level -- in a study of student performance).
Models that exploit this grouping structure are variously called multi-level models, hierarchical models, random intercept models, random coefficient models, and error-component models. They can be estimated on data for which normal-linear specifications are appropriate as well as data that require non-linear and non-additive specifications (e.g., Bernoulli-logistic models, Poisson-exponential models, etc.)
In this course, we will learn how to estimate and interpret these models using Stata. 
DATES
This course runs February 6-10, 2017.
TEACHING FELLOW: Matheus Hardt 
COURSE OUTLINE
Topics
1. Review of Building a Statistical Model when one's observations are independent
2. Building Group Structure into the Normal-Linear Model: Fixed Effects and Random Intercepts
3. Building Group Structure into the Normal-Linear Model: Random Slopes
4. Building Group Structure into non-linear models: Random slopes and intercepts
5. Applying the model to solve other problems: Micro-segmenting, small-area estimation, Cohort Analysis
PREREQUISITES
A full-semester graduate-level course in multiple regression analysis and Building Parameteric Models (offered in the IPSA-USP 2017 Summer School) and Advances in Modeling Dynamics (offered in the IPSA-USP 2017 Summer School) or the equivalent background. 

Essentials of Multiple Regression Analysis
Glauco Peres da Silva, University of São Paulo
COURSE DESCRIPTION
This five-day course is designed for students who are interested in reviewing their training in multiple regression analysis. It prepares students for courses offered in the IPSA-USP Summer School that require a background in multiple regression analysis.  The intensive course starts with a discussion of the logic of the multivariate regression model and the central assumptions underlying the ordinary least squares approach. Particular emphasis will be given to multicollinearity, heteroskedasticity, and autocorrelation. To complement lectures, students apply the concepts taught in lectures to analyze problems using Stata.
For those of you considering enrolling in this course, watch the video below to find out more!
DATES
This course runs Feburary 6-10, 2017.
TEACHING FELLOW:  Mauricio Izumi, University of São Paulo
COURSE OUTLINE
This course departs from the premise that the most effective way to learn multivariate statistics is by actively using the concepts discussed in class to solve problems. For each topic, we will have lectures that will be followed by sessions in which students will use empirical data to answer questions that are important to political scientists. For those students who will be studying multivariate regression analysis in the IPSA-USP Summer School, the course will provide an intuitive and basic review of linear regression in theory and practice.  In the five-day course, our classes will be focused on the following topics:
Topics
Day 1
An Introduction to the Multiple Regression Model
The Linear Regression Model with a Single Regressor
OLS Assumptions
Day 2
The Linear Regression Model with Multiple Regressors
Hypothesis Tests and Confidence Intervals
Assessing Goodness of Fit
Day 3
Multicollinearity
Day 4
Heteroskedasticity
Day 5
Autocorrelation
PREREQUISITES
The course presumes students have some basic training in mathematics including arithmetic and algebra operations. It also assumes that students have a background in statistics including basic probability; random variables and their distributions; confidence intervals and tests of hypotheses for means, variances, and proportions from one or two populations

Modeling Dynamics in Space and Time
Guy Whitten, Texas A & M and Lorena Barberia, University of São Paulo
COURSE DESCRIPTION
Data collected over both units (e.g. municipalities, states, countries) and time (e.g. days, months, years)---known as pooled time series---are common in social science. By gaining leverage both across units and over time, this data structure helps us answer important questions that would be difficult if we only looked at a single year (i.e. cross section) or single country (i.e. time series): the relationship between growth and democracy, whether or not the resource curse exists, and how institutions shape political and economic outcomes. However, pooled time series often show types of heterogeneity that make standard regression approaches inappropriate. In this course we will explore why we might want to use pooled time series data, what issues might arise when using data in time and space, and how we can test for violations of assumptions and account for them.
This five-day course is an applied intermediate-level course focusing on the techniques for testing theories with pooled time series data. During the first three days, the course will involve about three hours of lecture time with breaks, then lunch, then three to four hours of hands on instruction in analysis that takes place in smaller groups using STATA. On the fourth day, students will work on a specific project assignment that applies the concepts introduced in the course. On the final day, we will have a wrap-up lecture and discuss group presentations.
DATES
This course runs February 6-10, 2017.
TEACHING FELLOW: Andrew Philips, Texas A & M
COURSE OUTLINE
Topics
1. Matrix Algebra
2. The Basics of Pooled Time Series
3. Models of Pooled Time Series
4. Modeling Spatial Components of Time Series Cross Sectional data
5. Group Project Day
6. Group Presentations and Closing Remarks 
PREREQUISITES
A full-semester graduate-level course in multiple regression analysis, Modeling Dynamics (offered in the IPSA-USP 2017 Summer School) and Advances in Modeling Dynamics (offered in the IPSA-USP 2017 Summer School) or the equivalent background in time series analysis.

Predicting Elections:  Analytical Techniques and Illustrative Case Studies
Clifford Young, IPSOS-Washington 
COURSE DESCRIPTION
Election forecasting is an activity which attempts to predict the outcome of an election. Such prediction can either be very far out from the election (a year or more) or very close to election-day (a few days before).  Election forecasting can also employ the most simple of analytical frameworks to highly-sophisticated multivariate statistical models. So how is election forecasting employed in practice today?  What methods are employed? Are some better under certain conditions? And what are different methods’ relative strengths and weaknesses? The course will attempt to answer these questions through the use of both an examination of the extant literature on the subject on election forecasting as well as using illustrative case studies. The primary objective is to provide the student with a general understanding of election forecasting and a simple tool box to work from. It is important to stress that the course will focus on national elections (in both presidential and parliamentary systems) not on downstream races at the legislative, state, and local levels.
For those of you considering enrolling in this course, watch the video below to find out more!
DATES
This course runs February 6-10, 2017.
TEACHING FELLOWRodrigo Nakahara, University of São Paulo
INSTRUCTOR: Clifford Young is Managing Director Ipsos’ Public Sector and Polling in the US and leads its global elections and polling risk practice.  His specialties include social and public opinion trends, crisis management, communications research, and election polling. He also currently oversees Ipsos’ US presidential election polling for Thomson Reuters, and is the spokesperson for Ipsos Public Affairs in the US. Cliff is considered an expert on polling in emerging markets, as well as polling in adverse and hostile conditions. Before coming to Ipsos Public Affairs North America, he was Managing Director of Ipsos Public Affairs Brazil where he started the practice for Ipsos. Cliff has polled on over 80 elections around the world. He is a frequent writer, analyst, and commentator on elections, electoral polling, and public opinion.
COURSE OUTLINE
Election forecasting is increasingly finding its way into the national media coverage of elections. The best example of this is the 2012 US Presidential election which pitted Obama versus Romney. During this election cycle, Nate Sliver as well as other election forecasters became important stakeholders in the national narrative about the elections and ultimately predicted the election spot on. Many are talking about the rise of the “forecaster-pundit” who will replace traditional journalist and politics in election analysis.
Election prediction has many applications in practice.  Indeed, reducing uncertainty about which candidate will win on election-day can be an important strategic advantage for decision-makers in the private sector as we all in politics. The examples are many but include financial services firms making bets on currency or equities, ‘brick and motor’ companies making long-term capital investments, and political parties choosing the most viable candidate.  Ultimately, election- forecasting is a central tool in assessing the political risk associated with any specific decision.
The course includes four components:        
First, the course will review the existing literature on forecasting as well as best-industry practices.  In particular, we will explore definitions of prediction and forecasting; will survey theories behind behavioral change and behavioral prediction; will examination of the median voter model and related concepts; and will review of different types of analyst biases and how they can hinder optimal prediction.
Second, the course will review the different forecasting models used today by election forecasters, including the most naïve ‘rule of thumb’ frameworks to highly sophistical multivariate statistical models.  We will explore a number of different statistical models including those which include purely economic variables to those which employ public opinion variables as well.  Finally, we will also examine statistical models using both classical and Bayesian frameworks.  In essence, we want to compare and contrast different methods, assessing their accuracy and determining under what conditions they perform more optimally and under which conditions they don’t.   Ultimately, we will find that ‘one size does not fit all’ and that the forecaster needs to have a broad tool box of methods in order to be effective across contexts and countries.
Third, we will use the US 2012 presidential election and the Brazilian 2010 election to assess the different forecasting methods.  I choose these two elections because of their unique characteristics: the US is what I consider a data-intensive election environment, while Brazil is a data-scarce one.  Such relative differences in the volume of information require different forecasting solutions.   When appropriate, we will also examine other elections including the 2013 Kenyan, Italian, and Venezuelan elections; the 1992 British elections; 2011 Nigerian Elections as well as the upcoming Brazilian 2014 election.
Fourth, the course will also include lab sessions which will be used to run different models and methods.  Specifically, we will analyze data both from the 2012 US presidential and 2010 Brazilian elections. The lab is meant to give ‘hands on’ data experience and practical tools.
PREREQUISITES
Some familiarity with SPSS, STATA, R, or SAS.  We will be using a combination of SPSS, Excel, and R.
At least one course in multivariate statistics.