IPSA-USP 2016 Summer School Course Descriptions

Essentials of Applied Data Analysis

Leonardo S. Barone, CEPESP-Fundação Getulio Vargas

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.   

TEACHING FELLOW: 

Patrick Cunha Silva, Center for Metropolitan Studies

INSTRUCTOR: 

Leonardo S. Barone, CEPESP-Fundação Getulio Vargas

DATES

This course runs January 18-22, 2016.

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.

  
 
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.

TEACHING FELLOW

Natália Moreira, University of São Paulo

INSTRUCTOR

Glauco Peres da Silva, University of São Paulo

DATES

This course runs January 18-22, 2016.

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:

 



Course Day

Topics

Day 1

An Introduction to the Multiple Regression Model

The Linear Regression Model with a Single Regressor

Least Square 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.

 

Essentials of Time Series Analysis

Lorena Barberia, University of São Paulo

COURSE DESCRIPTION

This five-day course is an applied introductory course focusing on introducing the techniques for testing theories with 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.  

TEACHING FELLOW:

Andrew Philips, Texas A & M

INSTRUCTOR

Lorena Barberia, University of São Paulo

DATES

This course runs January 18-22, 2016.

COURSE OUTLINE

Topics
     1. Regression Essentials 
     2. Fundamental Concepts of Time Series Analysis – Non-Stationarity and Unit Roots 
     3. Univariate ARIMA 
     4. Group Project 
     5. Group Presentations and Closing Lecture
 
PREREQUISITES
A full-semester graduate-level course in multiple regression analysis. 
 
  

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 18-22, 2016.

TEACHING FELLOW: 

Gabriela Rosa, University of São Paulo

 
INSTRUCTOR: 

Patrick Thaddeus Jackson, American University

Advanced Time Series Analysis

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

COURSE DESCRIPTION

This four-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.

DATE

This course runs January 26-29, 2016.

TEACHING FELLOW: 

Andrew Philips, Texas A & M

INSTRUCTOR: 

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

COURSE OUTLINE

Topics
     1. ARIMA with RHS variables 
     2. ARCH and GARCH Models  
     3. Time Series Regression Models 
     4. Error Correction Models 
     5. Fractional Integration 

PREREQUISITES

A full-semester graduate-level course in multiple regression analysis and Essentials of Time Series Analysis (offered in the IPSA-USP 2016 Summer School) or the equivalent background in time series analysis. 

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 26-29, 2016.

TEACHING FELLOW: 

Camila Rocha

INSTRUCTOR: 

Derek Beach, University of Aarhus

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).

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 26-29, 2016.

INSTRUCTOR: 

Jason Seawright, Northwestern University

TEACHING FELLOW: Danilo Barboza, University of São Paulo

COURSE OUTLINE

PREREQUISITES

Basic exposure to regression analysis and qualitative methods, although we will review the relevant details of both traditions.

 

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 26-29, 2016.
 
TEACHING FELLOW:
Leonardo S. Barone, CEPESP-FGV
 
INSTRUCTOR:
Bruno Cautrès, Sciences Po
 
Building Parametric Statistical Models

Randy Stevenson, Rice University

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 26-29, 2016.

TEACHING FELLOW: 

Patrick Cunha SilvaCenter for Metropolitan Studies

INSTRUCTOR: 

Randy Stevenson, Rice University

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 1-5, 2016.

INSTRUCTOR: 

Jason Seawright, Northwestern University

TEACHING FELLOW: Danilo Barboza, University of São Paulo

A solid understanding of regression analysis and basic qualitative methods. Some exposure to natural experiments, matching, and randomized experiments, although we will review the relevant details of these traditions.

 

  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 1-5, 2016.

TEACHING FELLOW:

Leonardo S. Barone, CEPESP-FGV

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.   
  

Maximum Likelihood Estimation

Randy Stevenson, Rice University

COURSE DESCRIPTION

In this week long class, we will assume one has specified a parametric statistical model that captures the essential features of the process that generated one's data.  Thus, we will learn how to estimate the unknown parameters of these models using Maximum Likelihood and also how to estimate confidence intervals around these estimates and do hypotheses tests. We will also learn how to present results to communicate the message of the data effectively.

DATES

This course runs February 1-5, 2016.

TEACHING FELLOW: 

Patrick Cunha SilvaCenter for Metropolitan Studies

INSTRUCTOR: 

Randy Stevenson, Rice University

Pooled Time Series Analyses

Guy Whitten, Texas A & M

COURSE DESCRIPTION

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 1-5, 2016.

TEACHING FELLOW: 

Andrew Philips, Texas A & M

INSTRUCTOR: 

Guy Whitten, 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, Essentials of Time Series Analysis (offered in the IPSA-USP 2016 Summer School) and Advanced Time Series Analysis (offered in the IPSA-USP 2016 Summer School) or the equivalent background in time series analysis.   

  
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 module 2, 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. 
 DATES

This course runs February 1-5, 2016.

TEACHING FELLOW:

Camila Rocha

INSTRUCTOR: 

Derek Beach, University of Aarhus

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. 
 

 

   

 

 
Online Applications will be received starting May 15, 2017.
Applications are due by October 9, 2017.
Notifications of acceptances will be sent by October 16, 2017.
Applicants must pay registration fee by November 3, 2017.