Oxford Spring School Course Sessions
The Oxford Spring School will run from Monday 27 March – Friday 31 March.
The course consists of a week of intensive course sessions, made up of a variety of course options. Please find the teaching timetable below.
Spring School 2017 course delegates will study three core subjects over the duration of the five days. Before you complete the Spring School application form, please ensure you have chosen your three core subjects from the six subject options listed below. Please also decide if you wish to attend the optional roundtable discussion evening session.
If you are accepted on this course you will study the same subject every morning for five days, as shown in the timetable. You will study the same afternoon subject for three days (Monday-Wednesday), and the same afternoon subject for two days (Thursday-Friday).
| 9am-12pm |
|Causal Inference||Causal Inference||Causal Inference||Causal Inference||Causal Inference|
|Social Network Analysis||Social Network Analysis||Social Network Analysis||Social Network Analysis||Social Network Analysis|
| 1-2pm |
One Hour Walk-in
|Clinic on ‘R’||Clinic on ‘R’||Clinic on ‘R’||Clinic on ‘R’||Clinic on ‘R’|
| 2-4.30pm |
|Data Visualisation||Data Visualisation||Data Visualisation||Spatial Analysis||Spatial Analysis|
|Forecasting||Forecasting||Forecasting||Process Tracing||Process Tracing|
| 5.30-6.30pm |
Round Table Session
|Roundtable Discussion: ‘Publishing in Leading Journals and University Presses’|
To prevent obesity or smoking initiation among teenagers, who should be targeted in an intervention? How can we contain the spread of an infectious disease under limited resources? Who should be vaccinated first in order to be most effective during vaccination shortages? How can we dismantle a terrorist organization, a drug distribution network or disrupt the communication flow of a criminal gang? Social network analysis offers the theoretical framework and the appropriate methodology to answer questions like these by focusing on the relationships between and among social entities. Unlike transitional research methods, we shift the object of study from the individual as the unit of analysis, to the social relations that connect these individuals. A network is therefore a structure composed of units and the relationships that connect them. Network analysis is about the position of these units, the overall structure and how these affect the flow of information. The focus of the course is not so much on how to express these concepts formally through mathematics, but rather on how to use appropriate software to acquire measurements for these concepts in the data and use them rigorously in empirical hypothesis testing. The majority of the course will focus on descriptive methods of network analysis, but we will also discuss network-specific models and inferential methods for network analysis."
Do hospitals make people healthier? Is it a problem that more people die in hospitals than in bars? Does an additional year of schooling increase future earnings? Do parties that enter the parliament enjoy vote gains in subsequent elections? The answers to these questions, and to many others which affect our daily life, involve the identification and measurement of causal links: an age-old difficulty for both philosophy and statistics. To address this problem we either use experiments, or try to mimic them by collecting information on potential factors that may affect both treatment assignment and potential outcomes. In the past, this has entailed the specification of sophisticated versions of multivariate regressions. However, it is now understood that causality can only be dealt with during the design, not during the estimation process. The goal of this workshop is to familiarise participants with the logic of casual inference, the underlying theory behind it and introduce the research methods that help us approach experimental benchmarks with observational data. Hence, this will be an applied course, which aims at providing participants with ideas for strong research design in their own work.
Why let them read your data if they can see them? Visualising data is a powerful strategy for creating reports and conveying clear messages to readers, but traditional social sciences training does not cover data visualisation. This course will introduce participants to the use of graphs to report and show description, variation and correlations of data, and we will use graphical representation of effects, simulations and quantities of interests in order to report statistical results in a more straightforward way. As well as covering the basics of how to draw an effective figure or graph, we will also work on drawing maps and combining graphs. At the end of the course participants will have a wide choice of graphs for use in their future research.
Participants are asked to submit a visualisation project with its related data before the course. This can then be discussed with the instructor, and developed as part of the course. Visualisations will be done in R, and when possible, Stata codes will also be provided.
How does space matter in our analysis? Is space just about geography? This course introduces participants to conceptualizing interdependence between units, and how to empirically model these interdependencies. First, participants will be introduced to spatial data (points, polygons, raster) and how to get them and eventually geocode them. Second, we will discuss tests of spatial clustering and their visualizations (e.g. Moran’s Index and LISA). During our final session we will work on how to analyse data with geographic interdependences but also non-geographic spatial interdependencies (such as similar characteristics, interests and history) using spatial error and spatial lags models. The course’s examples will be in R.
How will leaving the European Union affect the British economy? Who will win the next French presidential election? How long will the civil war in Syria last? How many people will migrate next year? This course introduces participants to ways to build forecasting models, to forecast future outcomes, and to describe the performance of forecasting models. Such models allow researchers to conduct credible test of theories and to satisfy their and the public's natural curiosity of what will happen next. The goal of the course is to familiarise participants with the logic of forecasting with statistical models and with the practical steps for building and evaluating such models.
In qualitative, small-N designs, the strongest empirical foundation for making valid causal inferences comes from the analysis of causal mechanisms within cases. In this short course participants will learn about the three central steps required to carry out rigorous process tracing. First, what are causal mechanisms, and how should we specify our theories in order to rigorously study them? The course will begin with a discussion of the nature of causal mechanisms and of the philosophical foundations of a mechanismic view of social science. We will then explore the contrast between explanatory, stepwise theories of mechanisms and those that focus on predictions about constant conjunctions. This will be followed by a discussion of the importance of specifying micro-foundations in theories of causal mechanisms. Second, how do we derive empirical implications from theories about processes and how do we test these empirical implications? Process tracing is about making descriptive inferences regarding the presence/absence of the different steps in the hypothesized causal mechanism. Once we establish the presence of the different component parts of the mechanisms we use knowledge of context, the tools of logic and sequencing to look at these descriptive inferences as a system and infer the presence of causal flows connecting the putative causal factor with the outcome. The lectures will offer an introduction to the design of different types of process tracing tests that enable researchers to make these inferences. Finally, how do we evaluate the leverage of individual causal process observations? Participants will learn to judge the strength of process tracing tests using the Bayesian framework. By the end of the course participants will have the conceptual and methodological tools to craft research proposals that serve as roadmaps for data collection and analysis in the field.
Additional Sessions (Optional)
A daily walk-in clinic on ‘R’ will be available for an hour during the lunchbreak.
Attendees will be able to meet and network with editors of leading Political Science journals. The roundtable will take place on Tuesday evening, and will be followed by a drinks reception.