MA Public Opinion and Political Behaviour
MRes Political Science (MRES) options

Year 1, Component 07

Summer option from list
Introduction to Regression

This module covers regression analysis, both with continuous, ordinal, and categorical dependent variables. The focus is on applied regression analysis, but we will also deal with related topics like data treatment in Stata, interpretations, and how to test regression assumptions. The module includes ordinary least squares regression, logistic, ordinal, and multinomial regression, how to model and interpret non-linear effects as well as different types of statistical interactions. You will also focus on how to deal with breaches of assumptions.

Introduction to Social Network Analysis

This module will provide a practical, but comprehensive introduction to the analysis of social networks. Social network analysis takes the view that social research should not solely focus on the individual unit of analysis, but rather emphasises that researchers should also incorporate the social relations (networks) that connect these individual units (actors). For example, we might be interested in friendship among schoolchildren, trust among employees, collaboration among NGOs, exchanges of resources among companies, or conflict among nations.

Introduction to Quantitative Text Analysis

With the massive and ever-increasing availability of digital text data, social scientists increasingly use automated text analysis (or "text as data") to examine various kinds of social and political phenomena. This module introduces participants to a variety of its methods and tools. You will discuss their theoretical assumptions, substantive applications of these methods, and their implementation in the R statistical programming language. Teaching time – which combines lectures and coding sessions in the RStudio Cloud platform – will be hands-on, dealing with practical issues in each step of a text as data project.

Multilevel Statistical Models For The Social Sciences Using Stata

Statistical models are important tools for analysing quantitative datasets. In the social sciences, it is also common to refine or adjust models, beyond their standard formulations, in order to take account of the complexities of 'real life' social data. Through this module you will learn about statistical models in the social sciences and about certain popular strategies of using models to analyse complex or multilevel data. You will learn: – how to specify, formulate and interpret common types of statistical model – how to understand, implement and interpret multilevel models – how to assess and compare other ways of taking account of complex and multilevel data within a modelling framework – how to enhance complex data such as by merging variables or datasets and analysing them with appropriate statistical models

Introduction to Quantitative Methods in R

The module will cover how to analyze quantitative data in the free, open-source software R. Students should have a background in introductory statistics or concurrently enroll in an introductory statistics course. Prior initial exposure to statistical techniques up to linear regression (at a fundamental level) is helpful but not required. No background in R or computer programming is required. The module introduces R from a beginner's perspective. At the same time, students with experience in other tools (e.g. SPSS, Stata, or SAS) will find the course structure helpful to transfer their skillsets into R.

Introduction to Applied Bayesian Statistics

This module introduces the basic theoretical and applied principles of Bayesian statistical analysis. The Bayesian paradigm is particularly well-suited for the types of data that social scientists encounter given its recognition of the mobility of population parameters, its ability to incorporate information from prior research, and its ability to update estimates as new data are observed. The module begins with a discussion of the strengths and weaknesses of the Bayesian approach and the philosophical differences between the Bayesian and frequentist approaches. Most of the module content focuses on estimating and interpreting a variety of models (linear, dichotomous and polytomous choice, poisson, missing data, latent variable, and multilevel) from an applied Bayesian perspective.

Causal Inference and Experiments in the Social Sciences

Do campaign messages actually affect public opinion? Does a refugee's religion affect support for her asylum application? Do legislators respond when made aware of district preferences? This module develops a framework and a set of tools centred around answering causal questions such as these. We lay foundations in the potential outcomes model, allowing us to identify causal inferences. We discuss why we might conduct field, survey, and laboratory experiments, best practices for designing and registering experiments, how to overcome common problems, and how to analyse experimental data. We will also address special topics such as interference and mediation. Using experiments as a foundation, we will examine and apply methods for causal inference from observational data, such as matching, regression adjustment, instruments, and discontinuity designs.

Longitudinal Data Analysis

Longitudinal data are an essential tool for researchers as they can help answer questions about change in time, causal relationships and the timing of events. They come in many shapes, from traditional panel surveys to social media and sensor data. Because of their additional complexity, specialized statistical models are needed to analyse them. In this module you will learn how to analyse longitudinal data using R. The module is developed to include statistical models from a number of different fields, giving you a comprehensive knowledge of models such as: multilevel models for change, latent growth models, cross-lagged models and survival models. The module is also hands on, each topic being accompanied by real world applications using R and practical exercises. In addition to learning about statistical models you will also learn how to prepare and visualize longitudinal data as well as have the opportunity to discuss your own research projects, and get guidance on how you can use the methods covered in the module in your own work.

Categorical Data Analysis

This module offers an application-oriented introduction to maximum likelihood (ML) based models for categorical, discrete choice, and count data. We begin with the basics of ML estimation and a discussion of the theoretical foundations of categorical, discrete choice, and count-data models. We then focus on exploring logistic and probit regression models and learn how to apply them in the statistical software package Stata. Afterwards, we cover interpretation and hypothesis for testing these kinds of models. Against this background, we will consider more complicated estimation strategies, including ordered logit and probit regression models, multinomial logits, count models, or discrete duration models. The module concludes with an overview of advanced techniques of models for time-series cross-section (TSCS) categorical, discrete choice, and count data.

Machine Learning for Social Scientists

This module introduces methods of machine learning for social scientists. The broad objective of machine learning is to uncover patterns in data, either as an exploratory device or to make predictions. This module covers a variety of topics, including supervised, unsupervised, and ensemble learning. We discuss how the general principles of machine learning, as well as specific algorithms. The choice of technique, as well as application and interpretation take centre stage. Specific algorithms that will be discussed include artificial neural networks, bagging, boosting, classification and regression trees, clustering, decision rules, k-nearest neighbours, principal components, probabilistic learning, random forests, regression, and support vector machines. General principles include cross-validation, global and local interpretation, loss functions, optimization, regularization, variable importance, and feature selection.

Game Theory
Quantitative Data Analysis and Statistical Graphics with R
Deep Learning for Text and Vision
Survey Experimental Design
Machine Learning for Estimating Treatment Effects from Observational Data
How To Communicate and Engage Using Data Analysis in R
Machine Learning for Tabular Data
Multilevel Models: Practical Applications

This module is an applied introduction to multilevel modelling that aims to give you deep understanding of the standard model. It does not presume any prior knowledge in multilevel modelling but does require you to be very familiar with multiple regression analysis.

Bayesian Analysis for the Social and Behavioural Sciences

In recent decades, there has been an explosion of interest in Bayesian methodologies in the sciences. There are several reasons for this recent interest: first, Bayesian methods often yield easier-to-interpret answers to statistical questions than classical methods; and second, Bayesian methods are applicable in situations where classical methods are difficult or impossible to implement. In this module, you will learn the basics of practical Bayesian data analysis.

Programming and Simulation Methods for Computational Social Science

This module focuses on the research design and data analysis tools used to explore and understand social media and text data. The fundamentals of research design are the same throughout the social sciences, however the topical focus of this class is on computationally intensive data generating processes and the research designs used to understand and manipulate such data at scale.

Web Scraping and Data Management for Social Scientists
Longitudinal and Panel Data Analysis

Statistical models can be applied to longitudinal data. Chronological sequences of observations --time series data-- allow us to examine the movement of social science variables over time (e.g., public opinion, government policy, judicial decisions, socioeconomic measures), allowing analysts to estimate relationships between variables and test hypotheses. Data collected over both units (e.g., survey respondents, states, countries) and time (e.g., days, months, years) --panel data-- are common in the social sciences. By gaining leverage across units and over time, these data help us answer important questions that would be difficult if we only looked at a single point in time (e.g., cross section). Despite these advantages, longitudinal data often show forms of heterogeneity as well as temporal and spatial dependence that make standard regression approaches inappropriate.

Mixed Methods Research

Aspiring social scientists often wonder, "what is mixed methods research?" and "when should I use mixed methods in my research?" But before making decisions about using mixed methods research, other questions should be considered, such as "what is my philosophical stance?" and "what is my research question?" This module will introduce students to mixed methods research in the social sciences. This module provides (1) an introduction to mixed methods research; (2) an examination of the philosophical assumptions that guide the decision to use mixed methods research; (3) a context for data analysis and integration; and (4) a framework for drawing conclusions from mixed methods that inform future research, practice, and policy. A primary aspect will be to teach students how to determine if mixed methods are necessary, given the problem statement and research questions.

Quantitative Text Analysis

This module is designed to provide social science researchers an entry point to computational text analysis. You will gain hands-on experience designing and implementing a quantitative text analysis research project and will learn to discuss, evaluate and interpret the results. We will start with an overview of computational text analysis methods and discuss examples of their application across multiple disciplines and research fields. We will then survey the main ways in which text data can be acquired and present several major online text data sources.

Data Visualisation with R: Explore, Model and Communicate Social Data Analysis

In modern data analysis, graphics and computational statistics are increasingly used together to explore and identify complex patterns in data and to make and communicate claims under uncertainty. This course will go beyond traditional ideas of charts, graphs, maps (and also statistics!) to equip you with the critical analysis, design and technical skills to analyse and communicate with social science datasets. The course emphasises real-world applications. You will work with both new, large-scale behavioural datasets, as well as more traditional, administrative datasets located within various social science domains: Political Science, Crime Science, Urban and Transport Planning. As well as learning how to use graphics and statistics to explore patterns in these data, implementing recent ideas from data journalism you will learn how to communicate research findings – how to tell stories with data.

Quantitative Methods for Causal Inference and Policy Evaluation

In this module, students will learn the logic and methods of policy evaluation. We will focus on good research designs that answer important causal questions in public policy. In doing so, we will review the technical skills necessary to conduct credible empirical research such as field experiments, differences-in-differences, instrumental variables, and regression discontinuity designs. More importantly, we will practice the thinking necessary to develop and evaluate good research designs.

Spatial Econometrics

Spatial dependencies are a universal feature in the social sciences. Phenomena as diverse as the occurrence and outcomes of violent mass protests, policy learning and position taking in party competition, or the competitive setting of tax rates to attract foreign direct investment across neighbouring jurisdictions, all share a similar feature: actions taken by one actor are shaped in a theoretically meaningful way by the actions of one or more other actors. Spatial econometrics allows us to detect, model and estimate such interdependencies, and to work towards a causal interpretation of such relationships. The theoretical substance lies in the nature of interconnectedness between units, which can be geographic, economic, cultural, strategic etc., thus covering a wide ground of social science applications. This module begins with a data-oriented view of spatial patterns and dependencies in the data, then introduces a theory guided approach to building, estimating, and evaluating spatial and spatiotemporal regression models, and ends with a critical evaluation of the spatial approaches in the context of causal analysis.

Confirmatory Factor Analysis and Structural Equation Modelling

The module shows how theoretical assumptions concerning measurement models and substantive models can be translated into a structural equation model, and how the model can be estimated and tested with the Mplus 8 computer program. In addition, we provide syntax for all examples in R (lavaan). We will show how to use R procedures that produce lavaan code from Mplus input (mplus2lavaan) and Mplus input, Mplus compatible data and output via R (MplusAutomation). In the first part, we deal with confirmatory factor analysis (CFA), which relates multiple indicators to one (CFA) or several latent variables (Simultaneous Confirmatory Factor Analysis, Bifactor Models and second order confirmatory factor analysis) and multiple-group confirmatory factor analysis. Different specifications of measurement models are tested via confirmatory factor analysis (CFA) as a special case of a structural equation model (SEM) and we will discuss scale building procedures, measurement invariance testing and adequate reliability and validity estimates.

Advanced Methods for Text As Data: Natural Language Processing

This module will begin with an overview of text-as-data research for social scientists, orienting students to the general area and contextualizing the advanced approaches we will explore in the class. Then, we will begin to extend our text-as-data work beyond the "bag of words" to models that better represent the richness of text. The module will tyhen turn to embedding-based representations of texts and the underlying distributional theory. We will begin with static embedding models like word2vec and GloVe, and will discuss the benefits and utility of embedding-based representations for social science research. We will then further our work on embeddings by transitioning to contextual embeddings. To inform our understanding of pretrained contextual embedding models like ELMo and BERT, we will explore neural networks and deep learning in NLP, and will learn how to develop and deploy our own deep learning models. In doing so, we will cover feedforward neural networks, recurrent neural networks, and transformers. Then, we will explore transfer learning, or how to leverage pretrained models for application in our own specific domains. Finally, we will explore an area of increasing interest at the confluence of NLP and social science research: causal inference with text. In this section, we'll explore how and where text is being used as part of causal research designs, with a focus on efforts to leverage embedding based representations in those designs.

Introduction to Programming in Python for Social Scientists

Python is a powerful object-oriented, general-purpose programming language for collecting, organising, and analysing a wide array of data types. Alongside the R statistical computing platform, it provides one of the best tools available for social scientists looking to employ cutting-edge data science in their own research. This module covers the basics of the Python programming environment with a focus on data science applications: object manipulation, web scraping, data visualisation, elementary statistical methods, text analysis, and introductory machine learning tools.

Ideal Point Estimation, Item Response Theory, and Scaling Methods for Surveys and Behaviour

This module focuses on methods to discover, understand and visualise latent patterns in data and is especially suited to students with projects using survey data and other forms of relational data used in political science, sociology, economics, business, marketing, and psychology. The module introduces students to measurement theory and methods of scaling techniques, integrating Multidimensional Scaling, Item Response Theory, and Ideal Point Estimation.

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