Course Descriptions

Refresher Course in Mathematics

Glauco Peres da Silva, University of São Paulo 

OUTLINE

This course is designed for students who require an intensive review in basic mathematical concepts, which are essential for complete understanding of quantitative analysis in political science research. The course will take place in the week preceding the commencement of all other courses in the IPSA-USP Summer School as a preparatory course. It will cover topics 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.

DATES

This course runs January 26-30, 2015.

TEACHING FELLOWSGuilherme Jardim Duarte

DETAILED DESCRIPTION

This course will aim to review fundamental mathematics principles and skills that are needed for students wishing to study game theory, network analysis, experimental methods, formal modeling and statistics.      

OUTLINE OF THE COURSE

1.    Preliminaries

2.    Functions and limits

3.    Derivatives and Integrals

4.    Optimization

5.    Sets and probabilities

REQUIRED TEXTBOOKS

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 and students should feel comfortable with engaging with mathematics, formulas, and data analysis. Students who will be taking two-week courses in the Summer School requiring background in mathematics should strongly consider enrolling in this course.

 

 

Refresher Course in Statistics 

Leonardo Sangali Barone, Researcher, CEPESP/Fundação Getulio Vargas (FGV)

OUTLINE

This course is designed for students who are interested in reviewing their training in statistics. It prepares students for courses offered in the IPSA Summer School that require statistical training.  The course will take place in the week preceding the commencement of the Summer School. It is a refresher course in statistics focusing on probability, and data analysis. 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. Similar to the IPSA-USP Refresher Course in Mathematics and the Refresher Course in Multiple Regression Analysis, the course 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.   

DATES

This course runs January 26-30, 2015.

TEACHING FELLOWFrancisco Urdinez

DETAILED DESCRIPTION

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.

 

Refresher Course in Multiple Regression Analysis

Lorena Barberia, University of São Paulo

OUTLINE

This 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 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 multivariate regression model and the central assumptions underlying the ordinary least squares approach. Particular emphasis will be given to multicollinearity, heteroskedasticity, and autocorrelation. Similar to the IPSA-USP Refresher Course in Mathematics and the Refresher Course in Statistics, the course 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.   

DATES

This course runs January 26-30, 2015.

TEACHING FELLOWPatrick Cunha Silva

INSTRUCTOR

Lorena G. Barberia joined the Department of Political Science at the University of São Paulo as a member of its faculty in 2011. Her primary fields of interest are comparative political economy, economic development and Latin American politics. Her work is aimed at analyzing redistributive politics in Latin America. Her current projects include: the analysis of the impact of democracy and electoral institutions in developing democracies in Latin America on social services; the application of political budget cycles models to understand the impact of electoral politics on state-level expenditures in Argentina and Brazil; and the exploration of the impact of local politics on corruption in health spending in Brazilian municipalities. At USP, Professor Barberia's undergraduate and graduate courses include: Quantitative Methods and Techniques in Political Science.

DETAILED DESCRIPTION

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.

 

 

Knowing and the Known: An Introduction to the Philosophy of Science

Patrick Thaddeus Jackson, American University

OUTLINE

This course is a broad survey of epistemological, ontological, and methodological issues relevant to the production of knowledge in the social sciences. 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 a sort of introduction to the ways in which these issues have been incorporated (sometimes—often—inaccurately) into different branches of the social sciences;
• 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.

That having been said, this is neither a technical “research design” nor a “proposal writing” class, but is pitched as a somewhat greater level of abstraction. As we proceed through the course, we will always keep firmly in mind that these philosophical discussions have profound consequences for practical research, particularly in the areas of research design and causal explanation. Treat this course as an opportunity to set aside some time to think critically, creatively, and expansively about the status of knowledge, particular about the kinds of knowledge you yourself will produce as a researcher in the future -- including while using some of the techniques covered in depth in courses offered in subsequent weeks of the IPSA-USP Summer School.

DATES

This course runs January 26-30, 2015.

PREREQUISITES

No prerequisites except the willingness to work hard reading some complicated conceptual material.

 

 

Predicting Elections:  Analytical Techniques and Illustrative Case Studies

Clifford Young, IPSOS-Washington 

OUTLINE

Election forecasting is an activity which attempts to predict the outcome of an election. Such prediction can either be very far out from the election (a year or more) or very close to election-day (a few days before).  Election forecasting can also employ the most simple of analytical frameworks to highly-sophisticated multivariate statistical models. So how is election forecasting employed in practice today?  What methods are employed? Are some better under certain conditions? And what are different methods’ relative strengths and weaknesses? The course will attempt to answer these questions through the use of both an examination of the extant literature on the subject on election forecasting as well as using illustrative case studies. The primary objective is to provide the student with a general understanding of election forecasting and a simple tool box to work from. It is important to stress that the course will focus on national elections (in both presidential and parliamentary systems) not on downstream races at the legislative, state, and local levels.

DATES

This is a one-week course and runs February 9-13, 2015.

TEACHING FELLOWDanilo Praxedes Barboza

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.

DETAILED DESCRIPTION

Election forecasting is increasingly finding its way into the national media coverage of elections. The best example of this is the 2012 US Presidential election which pitted Obama versus Romney. During this election cycle, Nate Sliver as well as other election forecasters became important stakeholders in the national narrative about the elections and ultimately predicted the election spot on. Many are talking about the rise of the “forecaster-pundit” who will replace traditional journalist and politics in election analysis.

Election prediction has many applications in practice.  Indeed, reducing uncertainty about which candidate will win on election-day can be an important strategic advantage for decision-makers in the private sector as we all in politics. The examples are many but include financial services firms making bets on currency or equities, ‘brick and motor’ companies making long-term capital investments, and political parties choosing the most viable candidate.  Ultimately, election- forecasting is a central tool in assessing the political risk associated with any specific decision. 

The course includes four components:        

First, the course will review the existing literature on forecasting as well as best-industry practices.  In particular, we will explore definitions of prediction and forecasting; will survey theories behind behavioral change and behavioral prediction; will examination of the median voter model and related concepts; and will review of different types of analyst biases and how they can hinder optimal prediction.

Second, the course will review the different forecasting models used today by election forecasters, including the most naïve ‘rule of thumb’ frameworks to highly sophistical multivariate statistical models.  We will explore a number of different statistical models including those which include purely economic variables to those which employ public opinion variables as well.  Finally, we will also examine statistical models using both classical and Bayesian frameworks.  In essence, we want to compare and contrast different methods, assessing their accuracy and determining under what conditions they perform more optimally and under which conditions they don’t.   Ultimately, we will find that ‘one size does not fit all’ and that the forecaster needs to have a broad tool box of methods in order to be effective across contexts and countries.

Third, we will use the US 2012 presidential election and the Brazilian 2010 election to assess the different forecasting methods.  I choose these two elections because of their unique characteristics: the US is what I consider a data-intensive election environment, while Brazil is a data-scarce one.  Such relative differences in the volume of information require different forecasting solutions.   When appropriate, we will also examine other elections including the 2013 Kenyan, Italian, and Venezuelan elections; the 1992 British elections; 2011 Nigerian Elections as well as the upcoming Brazilian 2014 election.

Fourth, the course will also include lab sessions which will be used to run different models and methods.  Specifically, we will analyze data both from the 2012 US presidential and 2010 Brazilian elections. The lab is meant to give ‘hands on’ data experience and practical tools.

PREREQUISITES

  • Some familiarity with SPSS, STATA R, or SAS.  We will be using a combination of SPSS, Excel, and R.

  • At least one course in multivariate statistics.

 

 

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.

DATES

This course runs February 2-13, 2015.

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.

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 TEXTBOOKS

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.

 

 

Causal Case Study Methods: Comparing, Matching and Tracing

Derek Beach, Universtity of Aarhus

OUTLINE

The aim of this course is to provide students with a set of methodological tools that enable the use of case study methods in your own research. The course is designed for students in the early to mid-stages of the dissertation who have already defined their research question. A constant theme throughout the course will be on debating the strengths and limitations of different case-based, small-n methods, illustrating the types and scopes of inferences that are possible, and whether and how they can be nested into mixed-methods research designs. The core text is a forthcoming book on causal case study methods co-authored by the instructor – the text will be distributed to participants prior to the course.

The course starts by introducing the debate on whether there is divide between quantitative, large-n, regression-based and qualitative case-based study methods. This is followed by sessions on working with concepts and theories and discussions about causal inferences and causal relationships in case-based methods. In particular, the Bayesian logic of inference is introduced, showing how they differ from the frequentist logic used in large-n research.

The course then turns to individual case study methods, including cross-case, comparative research designs (Mill’s methods, structured-focused comparisons and typological theorization) and within-case methods (congruence/pattern-matching, and process tracing).

In the third part of the course, we discuss different case selection strategies and debate and when and how small-n studies can be nested into mixed-methods designs. The final exercise will be the production of a 5-7 page research design paper.

DATES

This course runs February 2-13, 2015.

TEACHING FELLOWTerra Friedrich Budini

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. Students are expected to be have encountered basic qualitative, case study research methods in their graduate-level education (e.g. King, Keohane and Verba’s Designing Social Inquiry (1994) is a good starting point). 

 

 

Introduction to Network Analysis using Pajek

Vladimir Batagelj,  University of Ljubljana (Slovenia).

OUTLINE

The course aims to provide an introduction into the main topics and concepts of social network analysis. It focuses on the analysis and visualisation of complete networks. Participants will get an understanding of basic network analysis concepts like centrality, cohesion, blockmodeling, etc. Special attention will be given to the analysis of large networks. After the course participants should be able to examine data in ‘social networks way’ – they should be able to identify and formulate their own network analysis problems, solve them using network analysis software and interpret the obtained results. The course is supported by Pajek – a program for analysis and visualisation of large networks.

 

DATES

This course runs February 2-13, 2015.

TEACHING FELLOW: Ana Carolina Andrada

DETAILED DESCRIPTION

The course will start with an overview of the history of social network analysis, followed by a presentation of some typical and well-known real-life networks. In the main part fundamental concepts and methods of network analysis will be explained. Lab sessions will be performed using the software package Pajek. Then the course will cover the following topics:

1) Basic network concepts: network representations: matrix, graph; types of networks: undirected networks, directed networks, multi-relational networks, 2-mode networks, temporal networks; size and density; small, large and huge networks, sparse and dense networks;

2) Program Pajek and other network analysis software: description of networks in Pajek input file; network layouts: automatic and manual drawing; Unicode; connection with statistical packages (R); utility programs: Text2Pajek, GSView, SVG, King, Inkscape;

3) Paths in networks: walk, chain and path; closed walk, cycle, closed chain, loop; length and value of path; the shortest path, diameter; k-neighbours; acyclic networks;

4) Centrality: degree, closeness, betweenness; hubs and authorities, clustering coefficient; Hummon-Doreian’s weights in acyclic networks; small world and scale-free networks;

5) Weights and properties: line and vertex cuts, sub-networks; regression; visualisation in Pajek;

6) Connectivity: weakly, strongly and bi-connected components; global and local views; contraction; extraction; skeletons: minimal spanning trees, Pathfinder;

7) Cohesion: triads, cliques, rings, cores, islands; strong and weak ties; pattern search (motifs);

8) 2-mode networks: examples of 2-mode networks; direct analysis of 2-mode networks; multiplication of networks; transforming 2-mode to 1-mode networks; analysis of bibliometric (citation, collaboration, keywords/tags,...) networks;

9) Blockmodelling: direct and indirect approaches; structural, regular equivalence;

10) Generalised blockmodelling and blockmodelling of 2-mode networks;

11) Temporal, spatial and multirelational networks: macros and operations on sequences of networks.

After listening to the lectures, participants will work individually in computer labs. Several data sets will be prepared to challenge their knowledge.

PREREQUISITES

Participants need to have basic knowledge of mathematics (set theory notation, computation with matrices and vectors) and statistics. Basic familiarity with at least one statistical package (R or SPSS) can be helpful. Participants are expected to attend computer labs daily, where the software package Pajek will be used. During the lab hours, students will perform several network analyses on different small and large networks individually.

 

 

Quantitative Methods for Public Policy Analysis

Bruno Cautrès, Science Po

OUTLINE

This course presents the major multivariate statistical methods that are fundamental to read advanced journals and to use statistics in the field of public policy analysis and public administration. Many specialists of public policy and administration, government/governance analysis, need modern quantitative methodologies when they are involved in evaluation of their actions and choices or in policy implementation. A key problem is then to show the net (and positive) impact of these policy changes and implementations. A policy expert that cannot prove this is somewhat in a bad position…The course aims at providing to participants a toolbox that may save their career…!  The aim is making participants more effective users of modern statistical tools in analyzing public policies problems and to give diagnostics about policy solutions and implementations. The interaction between statistics, policy analysis and decision making will be highlighted.

DATES

This course runs February 2-13, 2015.

TEACHING FELLOW: Leonardo Barone

DETAILED DESCRIPTION

The course has two major objectives:

  • To make the participants good experts of basic multivariate statistical methods (in particular modern methods of regression analysis, factor analysis);

  • From there to cover recent extensions specially designed for public policy analysis.

Among these extensions some are very important for policy evaluation which aims at establishing a causal link between interventions and outcomes: randomized evaluations, natural experiments, the regression discontinuity design, instrumental variables, difference-in-differences regression, will be presented in details. These methods permit the experts and practionners of public policy analysis to study the net impact of their policy choices and implementations in a typical before/after quasi-experimental framework. The temporal change perspective will be privileged even if the methods could be applied to cross-national analysis. The course presents methods in simple terms, the level of mathematical presentation is basic since the use and practical understanding is the fundamental objective. Cases studies will be available through a selection of papers and applications. The first week will cover the basic tools of multivariate statistics (regression analysis, factor analysis); the second week will cover the specialized multivariate tools for public policy analysis (natural experiments, instrumental variables, difference-in-difference regression, the regression discontinuity design).

PREREQUISITES

The course is introductory, only some basic knowledge in descriptive statistics and statistical inference (up to hypothesis testing) may be helpful before to attend. 

 

 

Time Series and Pooled Time Series Analyses

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

OUTLINE

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

 

DATES

This course runs February 2-13, 2015.

TEACHING FELLOW: Patrick Cunha Silva and Andrew Philips

DETAILED DESCRIPTION

This is a hands-on course, meaning that a major goal is to have students learn about techniques by putting them to work with statistical software. To facilitate this, we will have lectures on each topic in the mornings followed by lab sessions in the afternoons with a teaching assistant. In these lab sessions students will have the option of working with their own data or working with data provided by the instructor. The main statistical software program that we will use for the labs is Stata. We will also look at some examples in EViews and WinRATS.

Although some knowledge of Stata or other statistical programs would be helpful, this is not required. Students are strongly encouraged to bring with them one or more time series or pooled time series data sets in which they have an interest. This suggestion inevitably leads to the question: "How many time points do I need in my data set to estimate the types of models covered in this course?" Although there is no definitive answer to this question, it is usually difficult to get useful estimates from data sets with less than 30 time points. The instructor will be able to convert most types of data sets into formats compatible with the programs used in the course lab exercises. If you have any questions about data that you might bring to the course, send them to the instructor.

PREREQUISITES

Students should have a basic knowledge of multiple regression and social science research design. There are many ways to obtain this, but one self-serving suggestion from the instructor is to read Kellstedt and Whitten's The Fundamentals of Political Science Research, 2nd edition, with a focus on the materials covered in Chapters 8 through 10.

 

Game Theory and Applications for Political Science

Rebecca Morton, New York University

OUTLINE

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

This course introduces the basic concepts of elementary game theory in a way that allows students to use them in solving simple problems. Topics include: the basics of cooperative and non-cooperative game theory; basic solution concepts such as Nash equilibrium; and the extensions of these solutions to dynamic games and situations of incomplete information. Students will be exposed to a variety of simple games with varied and useful applications: zero-sum games; the Prisoner's Dilemma; coordination games; the Battle of the Sexes; repeated games; and elementary signaling games. The course will rely on a wide array of example applications of game theory in the social sciences.

DATES

This course runs February 2-13, 2015.

TEACHING FELLOWAllexandro Mori Coelho

DETAILED DESCRIPTION

There will be one required text for the class:  Martin Osborne, An Introduction to Game Theory

Outline:

1.  Introduction to Game Theory and Choice Theory

2.  Games with Perfect Information

3.  Illustrations of Games with Perfect Information Osborne, Chapters 2 & 3 Continued

4.  Probabilities & Expected Utility Theory

5.   Mixed Strategy Equilibrium

6.  Extensive Games with Perfect Information

7.  Games with Imperfect Information

8.  Extensive Games with Imperfect Information

9. (If time allows) Repeated Games

PREREQUISITES

The course will use significant amount of mathematics.  High school level algebra is required; knowledge of calculus is useful, but not required.

 

 

Spatial Data Analysis with Spatial Econometrics

Robert Haining, University of Cambridge

OUTLINE

This course introduces students to data mapping and spatial data analysis including exploratory analysis and spatial data modelling, both frequentist (GeoDa) and Bayesian (WinBUGS), for continuous valued and discrete valued dependent variables.  The course combines theory and applications (in the areas of socio-spatial criminology, spatial epidemiology, human geography, spatial economics) with hands-on experience of some of the methods through practical classes.

DATES

This course runs February 2-13, 2015.

DETAILED DESCRIPTION

This course will provide a two week introduction to the theoretical and practical challenges associated with analysing spatial data in the social sciences with a particular focus in the second week on spatial econometric modelling.  The course will consist of lectures, practical classes and consolidation/review sessions.  A wide range of application areas in the social sciences will be considered.

Some lectures will cover the theory and methods of spatial data analysis and spatial econometrics whilst others will demonstrate the methods in specific subject areas including criminology, geography, spatial economics and political science.  Practical classes will give students the opportunity to develop their skills in the application of these methods.  A range of software will be used including GeoDa for use by beginners to the field, but for those with experience of R and WinBUGS there will be the opportunity to fit models using these software.  For those with no prior experience but who wish to gain some initial experience of modelling with R and WinBUGS there will be examples to work on.  A question and answer/review and consolidation session at the end of each day will give students the opportunity to discuss and clarify topics covered during the day.

Week 1.  Spatial data analysis: theory and practice.

The first week will engage broadly with the theory, methods and application of spatial data analysis in the social sciences.  The week will be structured as follows:

Day 1 students will acquire an understanding of what it means to “think spatially and think statistically” as well as acquire some historical perspective by learning about the development of spatial statistics.  The day will then move on to look at types and sources of spatial data, including the theory of spatial sampling, and how the quality of spatial data is evaluated.

Day 2 pays particular attention to the question “what is special about spatial data?” looking at the twin properties of spatial autocorrelation and spatial heterogeneity.  Practical classes will enable students to study these statistics using real data sets.

Day 3 will concentrate on exploratory spatial data analysis (ESDA) and cluster detection.  The theory and  methods behind ESDA will be presented in lectures with practical classes giving students the opportunity to apply the methods taught. 

Day 4 will look at two important special topics in spatial data analysis: spatial interpolation (estimating missing values) and areal interpolation (non-parametric methods for translating data from one spatial framework to another).

Day 5 will provide a bridge into the second week by reviewing the normal regression model and the special issues that arise when fitting the normal model to spatial data.  Lectures will be followed by practical classes.

Week 2.  Spatial econometrics: theory and practice.

The second week will explore spatial econometric modelling. 

Day 1 will start with a short overview of the history of spatial econometrics followed by a close inspection of several statistical “workhorses” in the field: spatial lag models and spatial error models.   Some applications of these models will be presented and discussed.

Day 2 students will gain experience fitting these models using GeoDa and R.  It is anticipated that there will be two routes for this practical work – one for students relatively inexperienced in this field which involves using GeoDa, the other for more experienced students, familiar with (or willing to try using) R.

Days 3 will examine spatial models for discrete valued variables and discuss various applications of the models to real data.  The focus here is on Bayesian Hierarchical Models with students having the opportunity to learn about how to fit these models using WinBUGS.  These models are placed in the larger context of how data analysts cope with different forms of scientific uncertainty. 

Day 4 will give students experience of using WinBUGS through an extended practical class.

Day 5 will extend learning to space-time data modelling.  The emphasis on this final day will be on the special challenges presented by space-time data together with examples of applications.  In the afternoon there will be an opportunity to review the course and discuss directions for further learning.

Students intending to take this course should have an introductory level awareness of the principles of statistical inference and some experience of the regression model is highly desirable.  The aim is to offer a course that will be accessible to students who have only a limited background in statistics but who have a strong commitment to the use of statistical methods applied to social science data.  Whilst parts of the course may be challenging for those with only limited experience of statistical theory and methods there will be alternative pathways through most of the course that it is hoped will enable students from a variety of backgrounds to develop their skills in this area.

TEACHING FELLOWThiago Bananeira Castro e Silva

PREREQUISITES

The course presumes students have take a full semester of multiple regression analysis at the graduate level.

 

 

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

This course runs February 2-13, 2015.

TEACHING FELLOW: Tiago Cerqueira Lazier

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.