SA.100 (Core Courses)

Provides comprehensive introduction to econometrics. Develops tools for estimating functional relationships and critically reading empirical studies that use different econometric techniques; presents assumptions of multivariate regression and discusses the most common econometric problems and the potential consequences and remedies; and discusses omitted variables, sample selection, heteroscedasticity, autocorrelation, multicollinearity and use of discrete variables. Introduces instrumental variable technique. Uses statistical software in applied exercises.

Prerequisite(s): Statistics for Data Analysis (SA.340.710) is a pre-requisite for this course (SA.100.401);Students may not register for Econometrics if they have already received credit for SA.340.710[C];;SA.100.501[C] OR SA.340.709[C] OR SA.999.702[C]

Focuses on recent empirical issues in international economics and development. Examines both econometric methods and findings. Emphasizes the econometrics models used, estimation problems encountered and techniques developed to address these problems. Aims to evaluate evidence from existing research and extend understanding of econometric methods to carry out independent research. Prerequisite: Econometrics.

Prerequisite(s): Students may not register for this class if they have already received credit for SA.340.731[C];SA.100.401[C] OR SA.340.710[C]

Macroeconometrics develops the basic tools of time series to design dynamic models that explain the data, discriminate among alternative theories, and are used to forecast. The focus is on the following questions: Why is time a big deal for Macroeconometrics? How can one model the dynamics of the response of one variable to changes in its determinants? Can I treat the parameters as constant? What is forecast accuracy? Should the focus be on a single equation or on a system of equations? What are the assumptions, weaknesses, and strengths? Are there alternative methods of addressing a given issue? The course develops and applies this modeling strategy to understand the interactions between the price of oil and the value of the dollar. I will provide the data for these applications. Students write three “papers” that constitute the three sections of an integrated paper. The first three papers focus on (1) data properties; (2) assessing the inferential quality of the estimating equations; and (3) assessing forecast accuracy relative to alternative models. The fourth paper integrates the three papers into a reliable econometric model to explain and forecast the price of oil and the value of the U.S. dollar.

Prerequisite(s): SA.100.401[C] OR SA.340.710[C];Students may not register for this class if they have already received credit for SA.340.735[C]

This course provides a general overview of common quantitative and qualitative research methods used in practical applications in policy analysis. The first half of this course focuses on equipping students with understandings of different elements of research, diverse types of tools available for collecting (including interviews and surveys) and analyzing data, as well as heuristic values of research. It examines how to formulate research questions and hypotheses, the role of theory, how to construct a conceptual framework and write a literature review. The second half of the course engages students with in-depth discussions on case study methods and comparative methods. The goal of this course is to provide practical tips on designing and conducting graduate-level policy relevant research.

Prerequisite(s): Students may not register for this class if they have already received credit for SA.400.700[C];Cannot take if you have already received credit for SA.100.416[C]

The material in this course covers assumptions of multivariate regression and discusses the most common econometric problems and the potential consequences and remedies. Moreover, it discusses heteroscedasticity, time series, autocorrelation, multicollinearity, outliers, logistic regression analysis, pooled cross sections, and the use of dummy variables. The purpose of this course is to provide students with a conceptual understanding of the tools of quantitative analysis used in economics and social sciences research. The course has a strong practical approach, and the emphasis is on how to model, use, and interpret data to perform research analysis. Indeed, the topics will be heavily tilted towards interpreting pre-estimated quantitative output and identifying econometrics issues with estimation or testing procedure that might affect the interpretation of the output. In addition, class examples together with TA sessions will make students familiar with the main software used to perform quantitative research: STATA. This course will enable students to comfortably attend furthermore advanced courses in Economics and Finance. Prerequisite: Statistics for Data Analysis, Elements of Statistics, or Introduction to Statistics.

Prerequisite(s): SA.100.501[C] OR SA.100.502[C] OR SA.630.724[C] OR SA.999.702[C]

This course introduces students to research methods that are commonly employed today in the field of international relations. The focus of the course is on research designs aimed at establishing causal inferences. There are four main sections to the class, each of which covers one major type of research design. The first section focuses on qualitative research methods. The second section turns to quantitative methods, particularly methods used for analyzing observational data (i.e. non-experimental data). In section III, we study experimental methods. The final section provides an overview of various quasi-experimental methods.

Prerequisite(s): SA.100.401[C] OR SA.340.710[C];Students may not register for this class if they have already received credit for SA.100.423[C]

Research in the social sciences calls upon different methods for gathering information, interpreting data, drawing inferences, and advancing arguments. Qualitative methods rely on direct observation, narrative forms, and tools such as interviews, archival sources, media, participant observation, ethnographic analysis and historical documentation. Qualitative approaches may draw upon inductive techniques (assessing evidence directly) and deductive reasoning (drawing on stylized or logical relationships) when identifying patterns and crafting propositions. The purposes of qualitative methods are broadly similar to those of quantitative methods: drawing inferences (resilient generalizations) from evidence, developing causal arguments about the sources and mechanisms of events, and testing propositions about political behavior. Qualitative methods are also frequently partnered with quantitative methods in "mixed-method" research designs. However, the assumptions and procedures of qualitative methodology are distinct.Initial registration for this course is restricted to PhD students. Interested non-PhD students can register via SIS and request “Pending Approval”. Separately, students must submit a maximum one-page statement of interest and relevant experience to Professor Lewis at plewis18@jhu.edu; please copy jyoungs1@jhu.edu. Prof. Lewis will review and decide whether or not to approve.

Prerequisite(s): Students may not register for this class if they have already received credit for SA.600.774[C]

This class introduces students to different research designs in comparative and international analysis. The discussion will be based mainly on examples of high-impact studies. Methods covered include cross-national large-N survey, ethnography, interview, small-N comparison, paired comparison, and process tracing. Through intensive engagement with the readings, which are composed of classics and cutting-edge research, students are expected to develop critical appreciation of the diverse methodological strategies underlying different empirical studies and the capability of employing some of the methods in their research.

Today's world relies a lot on the accumulation, presentation, and interpretation of large quantities of information. Statistics and Econometrics are tools that enable us to organize our data in an efficient manner and provide us with methods that help to understand the relationships that occur in our data and our increasingly complex world. In this course, we will draw examples from multiple disciplines, such as political science and economics, to demonstrate how to search for and evaluate patterns in large amounts of data, as well as to interpret what these patterns tell us about the world. The course develops tools for estimating functional relationships and critically reading empirical studies that use different econometric techniques.

Prerequisite(s): SA.100.501[C] OR SA.340.709[C] OR SA.999.702[C] OR SA.630.724[C]

forthcoming...

Relationships between individuals and the social networks those relationships form can be crucial to explaining important sociopolitical outcomes, such as a group's power dynamics, the spread of information, or governmental decision-making processes. This course introduces students to the foundations of social network analysis, an increasingly popular approach for social scientists interested in understanding interdependence between actors and the properties of social network structures. Starting with an introduction to the conceptual foundations of social network analysis methods, the course then moves to consideration of their contemporary applications to understanding phenomena in international affairs as diverse as wartime mobilization or state status.

The course is a critical review of human-centered quantitative and qualitative research applications in international affairs, with a particular focus on learning the tools used by social scientists to evaluate the quality and feasibility of different research approaches in developing country contexts. The first half of the course delves into the practical uses of validity theory as the gold standard to assess the victories and pitfalls of experiments, quasi-experiments and non-experiments in real-world cases around the world. The second half of the course is devoted to understanding qualitative research methods (in-depth interviews, focus groups, and a variety of observational tools such as photography and graphic novel techniques), and learning to identify the strengths and weaknesses of qualitative work in hard-to-access contexts across Africa, Asia and Latin America. The course provides a practical knowledge foundation for professionals who aspire to critically assess the real value of quantitative and qualitative evidence presented to them, while it helps them to determine the right opportunities to generate new knowledge for more informed and effective policy and program design.

Prerequisite(s): Cannot take if you have already received credit for SA.100.406[C];Cannot take if you have already received credit for SA.100.414[C]

This course teaches students how to do field research. We will explore various field methods, including participant-observation, formal and informal interviews, network mapping, and ethnographic photography and video. In addition to weekly readings, students will immerse themselves in the actual doing of fieldwork by carrying out a mini field project during the semester in DC. Examples of field sites students can explore for their projects can include: courts, community organizations, religious institutions, activist networks, newsrooms, political institutions.

This course will focus on the history of modern international relations in order to train students in the use of historical, qualitative, and empirical methods. Students will work with primary sources and learn how to use these methods with those sources to build their own historical, qualitative arguments.

Nowadays datasets that have relevance for managerial decisions are accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, bar-code readers, and intelligent machines. Such datasets are often stored in data warehouses specifically intended for management decision support. Data mining is a rapidly growing field that is concerned with developing techniques to assist managers to make intelligent use of these repositories. Many successful applications have been reported in areas such as credit rating, fraud detection, database marketing, customer relationship management, stock market investments, and so on. The field of data mining has evolved from the disciplines of statistics and artificial intelligence. This course will examine methods and Machine Learning algorithms that have emerged from both fields and proven to be of value in recognizing patterns and making predictions. We will survey applications and provide an opportunity for hands-on experimentation with Machine Learning algorithms for data mining using Python. Prerequisite: Statistics for Data Analysis.

This course prepares students to conduct field research. Students will acquire methodological skills and data collection strategies that have broad application to research in international development. The course covers a range of field research methods, including ethnography, interviews, focus groups, surveys, and field experiments, as well as the logistical and ethical aspects of field research design.

Prerequisite(s): Students may not register for this class if they have already received credit for SA.100.406[C] OR SA.100.416[C]

This is an applied course on big data analytics with focus on data mining. R will be the main tool for problem solving with Python as the other option. Topics will cover data visualization, exploratory analysis, association rules, classification and regression trees, deep learning (neural networks), text mining, and social network analysis.Prerequisites:General understanding and experience with statistical models, including multivariable regression models, analysis of variance, and test of hypotheses. No previous programing experience is required, and the textbook offers extended code that can be used directly or modified.

Prerequisite(s): Students may not register for this class if they have already received credit for SA.510.131[C];SA.100.501[C] OR SA.999.702[C] OR SA.310.770[C]

Research in international affairs – whether academic or professional – calls for different methods for gathering information, interpreting data, drawing conclusions, and advancing arguments. Qualitative methods rely on direct observation, narrative forms, and tools such as interviews, archival sources, media, participant observation, ethnographic analysis and historical documentation. Qualitative approaches may draw upon inductive techniques (assessing evidence directly) and deductive reasoning (drawing on stylized or logical relationships) when identifying patterns and crafting propositions. The purposes of qualitative methods are broadly similar to those of quantitative methods: drawing inferences (resilient generalizations) from evidence, developing causal arguments about the sources and mechanisms of events, and testing propositions about political behavior. Qualitative methods are also frequently partnered with quantitative methods in 'mixed-method' research designs. However, the assumptions and procedures of qualitative methodology are distinct. This course will provide a general overview of the tools and assumptions of qualitative research methods, along with a survey of key literature and directions of inquiry.

This course offers a comprehensive overview of the diverse research methods used in the field of international relations, enabling students to better understand how scholars investigate global issues and events. We will explore various tools that researchers utilize to answer key research questions in the field. The course introduces students to both qualitative and quantitative approaches. On the qualitative side, studies will learn about comparative case-study methods as well as single case studies. Quantitative approaches covered include observational data, experimental methods, and natural experiments. The course aims to help students make practical use of these approaches, with emphasis placed on the intuition behind these diverse approaches and their respective strengths and limitations. By the end of the course, students will be equipped to critically assess the major types of research in the field of international relations and use these approaches in their own research projects.

Prerequisite(s): Students may not register for this class if they have already received credit for SA.100.408[C]

Covers basic statistical tools for data analysis. Emphasizes facility in problem-solving in statistical inference and two-variable regression and correlation analysis. Presents descriptive statistics, probability and probability distributions and their use in hypothesis testing. Uses computer to solve problems and to reinforce statistical concepts.

Prerequisite(s): Students may not register for this class if they have already received credit for SA.340.709[C]

This course is designed to furnish students with the fundamental tools of statistical analysis, including analysis of descriptive statistics, statistical inference via confidence intervals and significance tests, and correlation and simple/multiple linear regression analysis. Aim of the course is to introduce the basic statistical tools required to conduct and evaluate empirical research in economics and the social sciences. Special attention will be given to the application of these statistical tools to the analysis of real phenomena using computer to solve problems and to reinforce statistical concepts.