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IPSA-USP Summer School – 2019

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10th Annual IPSA-USP Summer School in Concepts, Methods and Techniques in Political Science and International Relations

January 14 – February 1, 2019

The 10th Annual IPSA-USP Summer School in Concepts, Methods and Techniques in Political Science and International Relations was held at the University of São Paulo, Brazil from January 14 to February 1, 2019. Jointly organized by the University of São Paulo’s Department of Political Science and the International Political Science Association (IPSA), the IPSA-USP Summer School made major progress in raising the international profile of our teaching activities, USP and research in São Paulo. Some 173 participants from 23 countries attended, with women accounting for 49% of enrollment. A total of 328 student-modules were completed in 23 one-week courses.

To celebrate the tenth anniversary of the IPSA-USP Summer School, two special events were held with generous support from IPSOS. The first, a Keynote Lecture by Professor Gary King of Harvard University attracted a large public audience from beyond the School’s participants to focus on the topic “How the news media activate public expression and influence national agendas”. The second, a Roundtable discussion, brought together diverse voices from across continents and research traditions to ask “What will Political Science look like in Ten Years’ Time?”.

At the Summer School poster session participants presented 26 posters. The winner of the 2019 Poster Competition in political methodology was Natalia de Paula Moreira (Department of Political Science, University of São Paulo). Thiago de Oliveira Meireles(Department of Political Science, University of São Paulo) received an honoruable mention for the poster in applied methodology.

Institutional Partners

The 2019 IPSA-USP Summer School was made possible with generous financial support from the Department of Political Science, the Provost’s Office for Research at the University of São Paulo, and the philosophy, literature and human sciences department (FFLCH) at USP. We are grateful for the valuable support provided by FAPESP, CAPES, CNPq and IPSOS. The Summer School was also supported by the Center for Metropolitan Studies (CEM) and the Center for Comparative and International Studies (NECI) at the University of São Paulo. Stata and Nvivo provided software licensing for the computer laboratories.

To encourage participation by as diverse a range of students as possible, the School secured funding from the Ford Foundation to finance the participation of one student from Cuba, and provided two IPSA travel grants in the amount of US$500 each. In addition, the School continued to offer registration fee waivers to the two political science departments who have partnerships with USP´s political science department –CIDE in Mexico and Texas A&M in the United States, and to a third political science department currently reviewing a cooperation agreement with USP, the University of Missouri. Two students from CIDE, one student from Texas A&M and the University of Missouri attended with registration fee waivers. The School also awarded registration fee waivers to a member of the Brazilian Political Science Association, a member of ALACIP (Asociación Latinoamericana de Ciencia Política) and one student from the Methods Winter School organized by the Catholic University of Uruguay.

Module I (January 14-18, 2019)Basics of Quantitative Methods for Public Policy Analysis – Bruno Cautrès, Sciences PoDesigning Feasible Research Projects in Political Science – Allyson Lucinda Benton, Centro de Investigación y Docencia Económicas (CIDE)Essentials of Data and Multiple Regression Analysis – Glauco Peres da Silva, University of São PauloEssentials of Time Series Analysis for Time Series Cross-Section Analyses – Lorena Barberia, University of São Paulo and Guy D. Whitten, Texas A&M UniversityInterviewing and Multi-Methods Research – Melani Cammett, Harvard UniversityIntroduction to Spatial Analysis – Jonathan Phillips, University of São PauloPredicting Elections: Analytical Techniques and Illustrative Case Studies – Clifford Young, IPSOS-WashingtonSurvey Research Design – Soledad Artiz Prillaman, Nuffield College at Oxford University 

Module II (January 21-25, 2019)The Philosophy of Science: Positivism and Beyond – Patrick Thaddeus Jackson, American UniversityAdvanced Issues in Quantitative Methods for Public Policy Analysis – Bruno Cautrès, Sciences PoAdvanced Research Design in Political Science: From Modeling to Manuscript – Allyson Lucinda Benton, Centro de Investigación y Docencia Económicas (CIDE)Basics of Causal Case Study Methods – Derek Beach, University of AarhusBasics of Multi-Method Research: Integrating Case Studies and Regression – Jason Seawright, Northwestern UniversityBasics of Spatial Interdependence in Theory and Practice – Laron Williams, University of MissouriFundamentals of Time Series Cross-Section Analyses – Lorena Barberia, University of São Paulo and Guy Whitten, Texas A & M UniversityMathematics for Social Scientists – Glauco Peres da Silva, University of São PauloSurvey Research Analysis – Soledad Artiz Prillaman, Nuffield College at Oxford University 

Module III (January 28 – February 1, 2019)Advanced Issues in Multi-Method Research: Integrating Case Studies and Contemporary Methods for Causal Inference – Jason Seawright, Northwestern UniversityAdvanced Time Series Cross-Section Analyses – Andrew Philips, University of Colorado at Boulder and Lorena Barberia, University of São PauloAn Introduction to Survey Experiments – Mark Pickup, Simon Fraser UniversityMaking Causal Critiques – Jonathan Phillips, University of São PauloMethods and Problems in Political Philosophy – Herlinde Pauer-Studer, Department of Philosophy, University of ViennaProcess Tracing Case Studies – Derek Beach, University of Aarhus

IPSA-USP 2019 Summer School Course Descriptions

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 14-18, 2019. 

TEACHING FELLOW: Flávio Souza, Texas A&M University 

DETAILED DESCRIPTIONThis 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. 

 PREREQUISITESBasic 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. 

Designing Feasible Research Projects in Political Science

Allyson Lucinda Benton, Centro de Investigación y Docencia Económicas (CIDE)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 14-18, 2019.

TEACHING FELLOW: Andreza Davidian

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 Data and Multiple Regression Analysis

Glauco Peres da Silva, University of São Paulo

COURSE DESCRIPTION

This course is designed for students who are interested in reviewing their training in data analysis and multiple regression analysis. It prepares students for courses offered in the IPSA-USP Summer School that require a background in statistics and in multiple regression analysis including the Time Series Analysis and Pooled Time Series Analyses, and Spatial Econometrics courses.  The course will take place in the week preceding the commencement of the Summer School. The intensive course starts with a discussion of the logic of the data analysis, based on including basic probability; random variables and their distributions; confidence intervals and tests of hypotheses. After that it covers the basic assumptions of multivariate regression model and the central assumptions underlying the ordinary least squares approach. Similar to other IPSA-USP courses, the Essentials of Data and Multiple Regressions Analysis takes a “hands on” approach. To complement lectures, students apply the concepts taught in lectures to analyze problems using software packages commonly used in quantitative social science research including Excel and Stata.

For those of you considering enrolling in this course, watch the video below to find out more!

DATES

This course runs January 14-18,2019.

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.  

 Topics
Monday, January 14thLecture 1. ProbabilityLecture 2. Distribution of random variables
Tuesday, January 15thLecture 3. Joint distributionsLecture 4. Confidence Intervals
Wednesday, January 16thLecture 5. An Introduction to the Multiple Regression ModelLecture 6. The Linear Regression Model with a Single Regressor
Thursday, January 17thLecture 7. Hypothesis Tests and Confidence IntervalsLecture 8. Assumptions of Ordinary Least Squares
Friday, January 18thLecture 9. The Linear Regression Model with Multiple RegressorsLecture 10. Assessing Goodness of Fit

In the afternoon, classes will be focused on labs activities.

PREREQUISITES

The course presumes students have some basic training in mathematics including arithmetic and algebra operations.

REQUIRED READINGS

Casella, George, and Roger Berger. 2008. Statistical Inference. 2nd ed. Duxbury Advanced Series. Cengage Learning.

Kellstedt, Paul M., and Guy D. Whitten. 2013. The Fundamentals of Political Science Research. 2nd ed. Cambridge ;New York: Cambridge University Press.

Stock, James H., and Mark W. Watson. 2011. Introduction to Econometrics. 3rd ed. Boston: Pearson/Addison Wesley.

FURTHER READINGS

Gelman, Andrew, and Hal Stern. 2006. The Difference Between “Significant” and “Not Significant” is not Itself Statistically Significant. The American Statistician 60 (4): 328-331.

Gujarati, Damodar N., and Dawn C. Porter. 2009. Basic econometrics. 5th ed. Boston: McGraw-Hill Irwin.

Greene, W. H. 2012. Econometric analysis. 7th ed. Upper Saddle River, NJ: Pearson Prentice Hall.

Wooldridge, Jeffrey M. 2009. Introductory Econometrics: A Modern Approach. Cincinnati, OH: South-Western College.

Essentials of Time Series for Time Series Cross-Section Analyses

Lorena Barberia, University of São Paulo and Guy D. Whitten, Texas A&M University

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. In this module, we will discuss the essentials of time series with a focus on preparing you for cross-sectional 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 four 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 fifth day, students will present a specific project that applies the concepts introduced in the course.

DATES

This course runs January 14-18, 2019.

Teaching Fellow: Ali Kagalwala

COURSE OUTLINE

Topic 1: We start with a review of standard regression assumptions. We then move into the basics of time series data.

Topic 2: We will cover how to write time series notation, how to analyze time series data, and begin to discuss threats to inference common in time series data, including autoregression and non-stationarity.

Topic 3: We introduce the basics of ARIMA models, which are used for univariate time series. Although ARIMA models are not used that frequently in applied political science research, they provide some important diagnostic procedures and a set of foundational concepts.

Topic 4: We will focus on regression models common in applied analysis (e.g.,, a dependent variable and multiple independent variables). We will discuss how autocorrelation is commonly treated in these models, lag structures, and interpreting the output from dynamic regression models.

Topic 5:  We will have student presentations of a research project developed over the week. Everyone will provide feedback. If needed, we will also finish up any lectures.
 PREREQUISITESA full-semester graduate-level course in multiple regression analysis.  

Interviewing and Multi-Methods Research

Melani Cammett, Harvard University

COURSE DESCRIPTION

This course focuses on the process of conducting field research from start to finish, with an emphasis on designing and carrying out mixed methods research that includes interviewing. The goal is to help guide researchers in preparing for and carrying out diverse forms of data collection in the field and to identify approaches to managing and analyzing data once collected. The bulk of the course centers on the design and execution of interview-based research and situates it within broader research projects. Key topics covered include strategies for preparing for and carrying out productive field research, which explicitly links research design to data collection in the field; varieties of interviews; the construction of different kinds of interview protocols; varied sampling strategies for interviewing; strategies for designing and conducting interviews on sensitive topics; organizing and analyzing interview-based data and aligning it with other forms of data collection; ethical considerations; and an overall assessment of the pros and cons of multi-methods research involving interviews.

DATES

This course runs January 14-18, 2019.

TEACHING FELLOW  Natalia Suzuki, University of São Paulo

COURSE OUTLINE

1.      Preparing for field research: Research designs to data collection in the field; designing and adapting “to-get lists”
2.      Interviewing in political science: Diverse types and roles of interviews in to research designs; elite and non-elite interviews;
3.      Designing interview protocols: Varieties of interviews and how to prepare for them
4.      Sampling strategies in interview-based research
5.      Strategies for conducting interviews on sensitive topics: Designing questionnaires on sensitive topics; carrying out interviews on sensitive topics; matched proxy interviewing
6.      Organizing and analyzing interview data: NVivo and other software options; intergrating data from interviews with other data collection approaches
7.      Ethical issues
8.      Back to research designs: The strengths and limits of interviews, multi-methods research

PREREQUISITES

There are no prerequisites for this course. However, some experience with field research is a plus. Participants should be prepared either to develop a new or improve an existing research proposal or to discuss a current research project that involves interview-based research and to describe the role of interviews in an actual or hypothetical multi-methods research design. Each day, participants should be prepared to assess and report progress on their own projects with respect to the particular topic of the session. To ensure progress, participants will be required to present written and oral developments of their research design each day. 

Introduction to Spatial Analysis

Jonathan Phillips, University of São Paulo

OUTLINE

Political interactions are often shaped by the geographical context in which they take place; by the shape of electoral boundaries, by the segregation of urban neighbourhoods, or by the distribution of natural resources. This course provides an introduction to thinking spatially.

By the end of the course students will be able to reframe any political or social science question as a question of political geography: Why are some countries democracies? Which people voted for the current government? Why is politics becoming more polarized? How long can I expect to live? Students will also have the tools to organize and create geographic datasets which can address these questions and interpret and produce maps displaying complex geographic relationships. Finally, they will be able to implement spatial statistics used in a wide range of academic studies and policy analyses, including measures of spatial clustering (for example, of violence), segregation (by ethnicity), discontinuity (along borders) and manipulation (in gerrymandered elections).

Throughout the course we will discuss a range of applied examples from the rapidly growing fields of political geography and spatial analysis. Half of each day will be spent discussing conceptual and statistical issues in the use of spatial data, and half in the lab practicing and experimenting with datasets. We will explore datasets from published papers, and also from the wealth of spatial data published in Brazil, including both vector and raster data formats. The labs will use open-source software to ensure students can continue learning on their own beyond the course, including QGIS as a Geographic Information System and Stata/R for spatial statistics. 

DATES

This course takes place between 14th – 18th January 2019.

TEACHING FELLOW  Tainá Pacheco, Fundação Getulio Vargas

DETAILED DESCRIPTION

Day 1: Introduction – We start the course by asking why spatial and geographic relationships matter for politics? Drawing on recent examples from the literature and policy debates, our goal is to create a typology of geographic relationships to guide the datasets and research questions we will use in the rest of the course.

Day 2: Spatial Datasets – We discuss how to access, organize and represent data spatially, including how to use interactive Geographic Information Systems. We will learn how to merge geographic shapefiles with non-spatial datasets, project data and perform basic spatial calculations and transformations.

Day 3: Visualizing Spatial Data – Continuing our analysis of spatial data, we discuss more advanced topics including the analysis of point patterns and raster data, including an introduction to processing satellite images. We will also discuss techniques of map visualization.

Day 4: Measuring Spatial Relationships – To characterize spatial interdependence and patterns, we derive basic statistical measures such as the Moran’s I measure of clustering and learn how to calculate these ourselves using standard statistical software.

Day 5: Applications to Political Geography – Drawing inspiration from recent political science papers, we replicate core spatial analyses of clustering, segregation, boundaries and gerrymandering.

PREREQUISITES

There are no prerequisites for this course, beyond an understanding of basic statistics. 

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 of 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 toolbox 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.

DATES

This course runs from  January 14 – January 18, 2019. 

*Please note the dates of this course have been altered for logistical reasons.

TEACHING FELLOW: Guilherme Russo, Fundação Getulio Vargas

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 toolbox 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 2018 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 US presidential and 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, such as the Essentials of Regression Analysis or the Basics of Public Policy Analysis offered in the IPSA-USP Summer School.

Survey Research Design

Soledad Artiz Prillaman, Nuffield College at Oxford University

COURSE DESCRIPTION

Surveys are one of the most important sources of data for researchers and their design and analysis is therefore a critical component of a researcher’s tool kit. This is the first course in a series on Survey Research aimed at providing these important tools. This course is designed to provide an introduction to methods of survey design and implementation. This course includes both lecture-based instruction as well as hands-on practice in survey design. By the end of the course, students will be able to design their own survey and assess the quality of survey questions, evaluate various sampling strategies and in-field protocols, and identify potential methodological challenges to data quality. The course will cover methodological issues that arise in survey research, including sources of bias, measurement theory, and non-response. Students will also have the opportunity to pilot their own original survey to learn first-hand the challenges with question design, sampling, and data quality. This course is well suited for students planning to collect survey or public opinion data as well as students that work with existing survey data but desire tools to evaluate and address the quality of the data.

DATES

This course runs January 14-18, 2019.

TEACHING FELLOW: Marina Merlo, University of São Paulo

COURSE OUTLINE

Day 1 – Introduction to Survey Methods, Ethics, and Research Design

Day 2 – Sample Frames

Day 3 – Instrument Development

Day 4 – Methods of Data Collection

Day 5 – Survey Interviewing and Quality Control

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 21-25, 2019.

TEACHING FELLOW: 

Gabriela Rosa, University of São PauloINSTRUCTOR: 

Patrick Thaddeus Jackson, American University

TOPICSSeminar 1: Who needs philosophy of science, anyway?Lecture 1: Cartesian Anxiety and the Positivist Project: the road to the Vienna Circle.Lecture 2: NeopositivismSeminar 2: Hypothesis-testing and cross-case comparisonLecture 3: Critical RealismSeminar 3: Causal powers and dispositional causationLecture 4: AnalyticismSeminar 4: Ideal-types and singular causal analysisLecture 5: Reflexivity and Critical TheorySeminar 5: Theorizing from a point of view 

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 January 21-25,2019.

TEACHING FELLOW: Guillermo Toral, MIT

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 phenomena 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.   PREREQUISITESBackground knowledge of multiple regression models, such as the Basics of Quantitative Methods for Public Policy Analysis course offered in Module 1, 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 participants to acquire practical skills for working with empirical data.    Advanced Research Design in Political Science: From Modeling to Manuscript

Allyson Lucinda Benton, Centro de Investigación y Docencia Económicas (CIDE)

OUTLINE

The aim of this course is to strengthen the methodological training of graduate students and junior faculty in comparative politics (including comparative political economy) or international relations (including international political economy) on how to take draft research papers and craft them into polished article manuscripts ready to be sent out for review in peer-reviewed academic journals. The course offers guidance on how to maximize the scholarly impact of each section found in an article-length manuscript, to raise its chances of surviving peer review. Morning sessions will include a short lecture on the main elements of and strategy for organizing the particular section under discussion that day. Specifically, the lectures will address different perspectives on how this can be achieved, with an eye to demonstrating how the component parts of an article-length research manuscript shape the dissemination of research findings in applied work and its contribution to knowledge. Students will receive feedback on the relevant sections in their manuscript to help them enhance the scholarly contribution of their research to the discipline. Afternoon sessions will function as a writing laboratory, where students will revise relevant sections of their manuscript, based on morning lectures and feedback, and present the results of their efforts.

DATES

This course takes place between January 21 – 25,2019.

OUTLINE OF THE COURSE

  1. Framing the Introduction and Literature Review
  2. Presenting the Argument and Testable Hypotheses
  3. Describing the Empirical Strategy, Cases, Variables, and Data Sources
  4. Organizing and Presenting the Empirical Findings
  5. Considering Alternative Arguments and Framing the Conclusion
  6. Identifying Venues for Publication

PREREQUISITES

Students should have completed an earlier methods course covering the equivalent of the topics discussed in the IPSA-USP Summer School course “Designing Feasible Research Projects in Political Science,” or should have taken the course itself in the current or a past session of the Summer School.

SPECIAL REQUIREMENTS:

Applicants must submit a draft of an article-length research manuscript in English. This manuscript might be a course research paper, a chapter in an MA or PhD thesis, or a conference paper that could be revised for publication as an article in an academic journal. Due to the structure of the course, manuscripts must address issues generally found within the comparative politics or international relations fields (that is, manuscripts covering topics within the field of political theory would not be appropriate for this course). Prospective students must send the draft manuscript as a part of the application, to be considered for admittance, by email to summeripsa@usp.br.

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. DATES

This course runs January 21-25, 2019.

TEACHING FELLOW: Natália Calfat, University of São Paulo

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. 75-114Mahoney, 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 RESEARCHGoertz 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 METHODBeach 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 STUDIESBeach and Pedersen (forthcoming) Causal Case Studies. Under contract at the University of Michigan Press, Chapter 8, 9. DAY 5 – MAKING INFERENCES IN CASE-BASED RESEARCHBeach 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).

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 21-25, 2019.

INSTRUCTOR: 

Jason Seawright, Northwestern University

TEACHING FELLOW: Janica Magat, Texas A&M University

COURSE OUTLINE

PREREQUISITES

A solid understanding of regression analysis and basic qualitative methods. Familiarity with statistical packages such as R and/or Stata.

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 the tools necessary to understand and use spatial econometric models.  It prepares students to carefully theorize about these spatial processes, specify the appropriate models 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 day 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 third day emphasizes how to estimate a variety of cross-sectional spatial econometric models.  The fourth day introduces a variety of spatial econometric models to deal with spatial heterogeneity and dependence across time and space.  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 21-25, 2019.

Teaching Fellow: Flávio Souza, Texas A&M University

COURSE OUTLINE

Day 1: Spatial dependence 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: Specifying and estimating spatial econometric models

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.  We explore a variety of techniques used to estimate a simple class of spatial econometric models including OLS, MLE and two-stage least squares.

Day 4: Spatial dependence in a time-series cross-section world

The simple class of spatial econometric models introduced in the previous day serves as a useful foundation for understanding basic spatial dependence across units.  Since these models are relatively simple, they cannot handle the additional obstacles that arise in time-series cross-sectional data.  This lecture introduces a variety of modifications to these simple models that incorporate both spatial heterogeneity and temporal dependence. 

Day 5: Visualizing and depicting spatial dependence

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. This is the second course in a two-week spatial module.  It is strongly encouraged that students also take the first week’s class, “Introduction to Spatial Data” with Jonathan Phillips.

Fundamentals of Time Series Cross Section Analyses

Lorena Barberia, University of São Paulo and Guy Whitten, Texas A & M University

COURSE DESCRIPTIONData 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 dicult if we only looked at a single year (e.g., cross section) or single country (e.g., 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 week, building on Module I’s introduction to time series, we move to modeling cross sectional time series data. 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. We discuss the fundamentals behind pooling your data, such as accounting for heterogeneity and temporal dependence. We also cover a variety of ways of presenting results from cross sectional time series models.During the first four days, the course will involve about three hours of lecture time with breaks, then lunch, and then three to four hours of hands on instruction in analysis that takes place in smaller groups using Stata. On the fifth day, students will present a speci c project that applies the concepts introduced in the course.

DATES

This course runs January 21-25, 2019. 

TEACHING FELLOW: Yoo Sun  Jung, Texas A&M University

COURSE OUTLINE

Topic 1: We will introduce the basics of pooling data across time and space. This involves the notation behind cross-sectional time series data, different types of data structures, and ways to visualize and examine these data (including panel unit root tests).

Topic 2: We will begin to cover how to think about and model unit heterogeneity. We will delve into models of pooled time series data used by applied researchers. These include models with random effects, fixed effects, and panel corrected standard errors.

Topic 3: We will discuss di erent strategies for moving between theories and model specifications with pooled time series data.

Topic 4: We will focus on how to present and interpret our results. This will involve discussing simulation-based approaches.

Topic 5: We will have student presentations of a research project developed over the week. Everyone will provide feedback. If needed, we will also nish up any lectures.

PREREQUISITES

A full-semester graduate-level course in multiple regression analysis and Essentials of TS for TSCS (Module 1) or the equivalent thereof.

Mathematics for Social Scientists

Glauco Peres da Silva, University of São Paulo 

OUTLINE

This course is designed for students who seek to acquire basic training in mathematics applied to quantitative analysis in the social sciences. It is directed toward students with limited training in mathematics. This course will present basic concepts commonly used in political science research, such as mathematical notation, basic set theory, various number systems, the algebra of numbers, the notion of a function, several important classes of functions, and solutions to systems of linear equations. The course will also provide an introduction to differential and integral calculus and applied matrix algebra. 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.

For those of you considering enrolling in this course, watch the video below to find out more!

DATES

This course runs January 21-25,2019.

DETAILED DESCRIPTION

This course will present fundamental mathematics principles focusing on students with limited backgrounds in mathematics. This course will adopt a hands-on approach in which morning lectures will be followed by afternoon laboratory sessions in which student will apply the concepts taught in the morning to solve problems. Classes will be divided in three steps: a) lectures; b) in-class exercises; c) applications based on social science research.

Teaching Fellow: Mauricio Izumi, University of São Paulo

OUTLINE OF THE COURSE

1.    Preliminaries

2.    Functions and limits

3.    Derivatives and Integrals

4.    Linear Algebra

5.    Optimization

6.    Maxima and Minima Global and Local

7.    Lagrange Multiplier

Required Readings

Gill, Jeff. 2006. Essential Mathematics for Political and Social Research. 1st Edition, Cambridge University Press.

Moore, Will, and David Siegel. 2013. A Mathematics Course for Political & Social Research. 1st edition, Princeton, N.J.: Princeton University Press.

Simon, Carl, and Lawrence Blume. 1994. Mathematics for Economists. 1st edition, W. W. Norton & Co. 

PREREQUISITES

A background in algebra is helpful. 

Survey Research Analysis

Soledad Artiz Prillaman, Nuffield College at Oxford University

COURSE DESCRIPTION

Surveys and public opinion polls are important sources of data, particularly for research on political behavior and preferences. This is the second course in a series on Survey Research aimed at providing these important tools. This course focuses on the analysis of existing survey data and reporting of results. The course will cover data cleaning and coding, descriptive statistics, hypothesis testing, data reduction techniques, multivariate regression of linear, binary, and categorical data, and measurement error and other sources of bias. The course will also cover the presentation of survey data through graphics and reports. By the end of the course, students will be able to take raw survey output through the process of creating presentation quality reports and graphics. This course is valuable for any researcher working with survey or public opinion data that wants an introduction to methods of survey analysis.  

DATES

This course runs January 21-25,2019.

TEACHING FELLOW: Marina Merlo, University of São Paulo

COURSE OUTLINE

Day 1 – Data Processing and Reduction

Day 2 – Multivariate models of survey data

Day 3 – Dealing with non-response

Day 4 – Measurement error and sources of bias

Day 5 – Data presentation and visualization

PREREQUISITES

A background in statistics and multiple regression is strongly suggested.

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 January 28 – February 1,2019.

INSTRUCTOR: 

Jason Seawright, Northwestern University

TEACHING FELLOW: Janica Magat, Texas A&M University

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 Time Series Cross-Section Analyses

Andrew Philips, University of Colorado at Boulder and Lorena Barberia, University of São Paulo

COURSE DESCRIPTIONData collected over both units (e.g., municipalities, states, countries) and time (e.g., days, months, years)—known as time series cross-sectional data—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 (e.g., cross section) or single country (e.g., 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 week, building off Module I’s Essentials of Time Series for Time Series Cross-Section Analyses (TSCS) and Module II’s Fundamentals of Time Series Cross-Section Analyses, we cover several advanced topics regarding these data. This includes a focus on establishing identification, model selection testing procedures, as well as more advanced estimation methods, such as GMM and SUR models. During the first four days, the course will involve about three hours of lecture time with breaks, then lunch, and then three to four hours of hands-on instruction in analysis that takes place in smaller groups using Stata. On the fifth day, students will work on a specific project assignment that applies the concepts introduced in the course. 

DATES

This course runs January 28 – February 1, 2019.

TEACHING FELLOW:  Hannah Paul, University of Colorado at Boulder

COURSE OUTLINE

Topic 1:  Modeling Heterogeneity: Slopes
For the first topic, we will focus on testing various pooling assumptions about our coefficients of interest. We will introduce SUR (Seemingly Unrelated Regressions) models, which have two or more equations (one for each cross-sectional units) whose errors are correlated. This modeling strategy is appropriate for testing the pooling assumptions that we make in models of TSCS data but does not work well for models that include variables that have little or no within-unit variation. We will also discuss models that incorporate random slopes, another way to relax the assumption of a fixed effect across units.

Topic 2: The Mundlak Transformation and Missing Data

For the second topic, we will discuss the Mundlak transformation, an additional model to explore heterogeneity in effects between units, as well as within units. We will also discuss how to impute missing data, a common issue when working with TSCS data.

Topic 3: Models for Dichotomous Dependent Variables in TSCSFor the third topic, we will explore how to model dichotomous dependent variables. These models require us to think differently about event dependence than models with a continuous dependent variable.
Topic 4:  Modeling Dynamics with GMM Estimators
For the fourth topic, we will introduce the one and two-step generalized method of moments (GMM) estimators for dynamic panels, which have become increasingly popular. We will show how these models handle the endogeneity of regressors and unit fixed effects, as well as discuss some of the potential pitfalls that should be avoided in estimation.

Topic 5:  Student Presentations
For the last topic, we will have student presentations of your research project you have developed over the week. Everyone will provide feedback. If needed, we will also finish up any lectures.

PREREQUISITES

A full-semester graduate-level course in multiple regression analysis and Essentials of TS for TSCS and Fundamentals of Time Series Cross-Section Analyses (offered in the IPSA-USP 2019 Summer School) or the equivalent background in time series and time series cross-section (TSCS) analysis.

An Introduction to Survey Experiments

Mark Pickup, Simon Fraser University

OUTLINE

Survey experiments combine the power of causal inference from an experimental design with the external validity of representative surveys. Further, online survey experiments provide a highly flexible platform for conducting the experiment. This course will cover the theory, design and analysis of survey experiments. In terms of theory, the course covers topics such as the potential outcomes model of causal inference, and the logic of experiments. In terms of design, the course covers topics such as: types of randomization; blocking; priming and framing experiments; list and conjoint experiments; and incentivized experiments. As for analysis, the course covers difference of means (and proportions) tests, difference-in-differences tests, rank statistics, and OLS regression and randomized inference for testing treatment effects. In addition to a discussion of each of these topics, the course will include instruction on how to set up an online survey experiment and analyse the data.

DATES

This course runs January 28-February 1,2019.

TEACHING FELLOW:  Guilherme Russo

OUTLINE OF THE COURSE

Day 1: Review and Background

During the first day, we will review some of the basics of conducting surveys, with a focus on conducting web-based surveys. The lecture will also provide an overview of the potential outcomes model of causal inference, which will allow us to discuss the logic of experiments, and survey experiments in particular. This will include a discussion of different types of treatments in survey experiments and forms of randomization. The lecture and lab will also provide a review of difference of means/proportions tests and simple OLS regression, and discuss methods of analysis that may be new to students, including difference-in-differences tests, rank statistics, and randomized inference for testing treatment effects.

Day 2: List and Conjoint Experiments

Day 2 explores experiments designed to elicit preferences that are not easily obtained through traditional survey methods. These include sensitive opinions that are subject to social desirability effects, and preferences that require difficult trade-offs. In the lecture and lab, we will examine: 1) how the list experiment can be employed to measure the prevalence of sensitive opinions (e.g., anti-immigrant sentiment) in a population; and 2) how conjoint experiments can be used to measure the utility that respondents attached to difference preferences that may be in conflict with each other (e.g., characteristics of candidates in a democratic election).

Day 3: Priming and Framing Experiments

Day 3 examines experiments designed to test how context can change the attitudes and opinions one expresses. The lecture and lab will examine: 1) how framing experiments test how the informational environment can change a respondent’s expressed attitudes and opinions (e.g., how the provision of different types of arguments changes a respondent’s expressed support for a policy); and 2) how priming experiments test how the salience of different considerations can change a respondents expressed political choices (e.g., how making a political identity salient changes the respondent’s choices). In the lab component of the day we will also look at how to set up a very simple online survey experiment.

Day 4: Incentivized Experiments

A common concern in surveys is “cheap talk”. There is very little cost to a respondent if they give a response that feels good to them, even if it is not entirely true. For example, respondents may give responses that they think those conducting the survey want to hear. Alternatively, they may give responses that are consistent with their identity, even if it is not truly what they believe. In the case of partisan identity, the latter is known as partisan cheerleading. A potential solution to this is giving respondents an incentive to reveal their true beliefs and preferences. Monetary incentives are commonly paired with tasks in which the amount that the respondent receives depends upon the decisions she makes and potentially also the decisions of other respondents. This approach is more common in lab experiments but is increasingly being applied in survey experiments. In Day 4, we will cover the application of incentivized experiments within surveys.

Day 5: Cautions and Challenges

Although survey experiments can be very powerful, this requires the researcher to carefully connect the causal mechanism they wish to test to the survey design. Even then, questions of internal versus external validity, ethics, and response bias remain. In the last lecture, we will discuss potential problems when conducting survey experiments and the best ways to mitigate them.

PREREQUISITES

Students are strongly recommended to take the Survey Research Design (offered in Module 1 of the the IPSA-USP 2019 Summer School and the Survey Analysis (offered in Module 2 the IPSA-USP 2019 Summer School) or the equivalent background in survey analyses.

Making Causal Critiques

Jonathan Phillips, University of São Paulo

OUTLINE

This module will give students the tools and confidence to understand, deconstruct and critique political science research papers. By encouraging participants to ground critiques of both quantitative and qualitative research in the framework and language of causation, the course hones vital skills for identifying hidden assumptions, weighing the strength of evidence and suggesting alternative explanations. The course also underlines the importance of making these critiques constructive by suggesting alternative research designs and a wide range of robustness checks. By the end of the course, participants will be confident contributing to peer review processes as colleagues, seminar participants or as journal referees, and will also gain new perspectives on how to design and execute their own research.

The course aims to systematize the types of critique we can make so that participants are able to provide multiple reasons why the account offered by an author might not be valid. While the course covers critiques of measurement, theory and modeling, we focus particularly on critiques of causation, including risks of selection, confounding and reverse causation, demystifying terms such as ‘counterfactual’, ‘complier’ and ‘external validity’. In turn, we consider how to make critiques constructive – first, in the way they are communicated, and, second, in identifying positive research strategies that can overcome or mitigate common critiques, for example alternative research designs and robustness tests.

We will use the afternoon lab sessions to practice formulating effective and constructive critiques. Building on examples drawn from a wide range of papers and review articles across the fields of political science and international relations, participants will develop and compare critiques. Participants will also have the option (no obligation or expectation) of sharing their own research ideas and papers to receive feedback from others. The lab sessions will include the replication of one or two published analyses to highlight the range of modelling options researchers are faced with and the breadth of potential critiques that this opens up. The replication exercises will be guided and can be completed in Stata or R.

DATES

This course takes place between January 28 – February 1, 2019.DETAILED DESCRIPTION

Day 1: Deconstructing an argument – First we discuss what constitutes a convincing argument and how a paper can contribute to learning in the discipline by reviewing the concept of causation and the framework of causal inference. Then we learn to systematically translate the text of a paper into the core elements of a research argument; the units of analysis, the comparisons, the concepts, the measures, the assumptions, the theory, the models and the conclusions.

Day 2: Fundamental Critiques – We consider basic critiques of whether the measures reflect the concepts, whether the model captures the theory, and whether the conclusions follow from the premises and evidence.

Day 3: Assessing Causal Evidence – We review a range of causal research designs, the assumptions on which they are based and their connection to specific statistical models. We practice repeatedly assessing if these assumptions have been met.

Day 4: How much are we learning? – We look beyond each argument’s own claims to critique the generalizability of the findings, the sensitivity of the findings to the research design, the match between theory and evidence, and support for specific causal mechanisms.

Day 5: Constructive Critiques – We consider various strategies and techniques for overcoming weaknesses in an argument. These include the use of alternative research designs, deriving multiple tests from theory, uncovering ‘hidden’ units, robustness tests, heterogeneity tests and placebo tests.

PREREQUISITES

Participants should have a basic understanding of research design and quantitative methods techniques. IPSA courses that meet this requirement are “Designing Feasible Research Projects in Political Science”, “Basics of Quantitative Methods for Public Policy Analysis”, “Advanced Issues in Quantitative Methods for Public Policy Analysis”, “Advanced Research Design in Political Science:  From Modelling to Manuscript” or “Basics of Multi-Method Research: Integrating Case Studies and Regression”.

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 28 – February 1,2019.

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.

Process Tracing Case Study Methods

Derek Beach, University of Aarhus

COURSE DESCRIPTION

 The aim of this course is to introduce process tracing case study methods, enabling participants to use them in for their own research. At the core of process tracing is the in-depth analysis of causal mechanisms (process) and how mechanisms function in real-world cases. Process tracing therefore attempts to unpack and study the arrow that links causes and outcomes together in causal models, shedding light on how things work. The course will discuss process tracing’s relative strengths and limitations, and how the method can be combined productively with other methods in multi-method designs.

The course develops the two core elements of process-tracing, focusing first on the theory-side by assessing what we are actually ‘tracing’ using process-tracing methods (causal mechanisms), and second, how we are able to make evidence-based causal inferences using within-case, ‘mechanistic’ evidence. The final session deals with how process-tracing can be combined with other case-based designs like small-n comparative methods.

DATES

This course runs January 28 – February 1,2019.

TEACHING FELLOW: 

Natália Calfat

COURSE OUTLINE

The course is intended for participants who have some knowledge of case-based methods. Ideally, participants should be mid-stage in a research project, enabling you to draw on theories and empirics from your own research to be refined during the course.

The core of the readings will be a 2016 book on causal case studies co-authored by the instructor.

The course covers the following topics:

– Day 1 – what is process tracing actually tracing?

– Day 2 – how can we make inferences in process tracing research?

– Day 3 – evaluating evidence

– Day 4 – case selection and process tracing

– Day 5 – designing process tracing research in practice

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 3-5 page description of their research question and tentative research design which will be presented and discussed during the course. The Process Tracing course is for participants who have either followed the Basics of causal case studies or have working experience with using case studies and knowledge of recent developments in case-based methods. 

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