Department seminar speaker


Departmental and Data Science seminar series

Termly events held by the Department of Methodology and the Social and Economic Data Science (SEDS) research unit.


Leading social scientists consider cutting-edge quantitative and qualitative methodologies, analyse the logic underpinning an array of approaches to empirical enquiry, and discuss the practicalities of carrying out research in a variety of different contexts.

Department of Methodology Seminar Series 

Department seminars take place in COL 8.13 from 12:30 - 14:00 unless otherwise stated. A sandwich lunch will be provided from 12:15.


Who benefits from expediting drug approvals? An evaluation of the US FDA Accelerated Approval pathway
Date: 9 November 2017
Speaker: Huseyin Naci, Department of Health Policy, LSE

Abstract:The US Food and Drug Administration's (FDA) Accelerated Approval pathway allows drugs treating serious illnesses to be approved on the basis of surrogate endpoints reasonably likely to predict patient benefit. Approximately 10% of drugs approved by the FDA are in the Accelerated Approval pathway. Once a drug receives Accelerated Approval, the FDA legally requires the pharmaceutical sponsor to complete one or more confirmatory trials to demonstrate clinical efficacy on the basis of established clinical outcomes such as overall survival. In this seminar, I will present the findings of our research describing the characteristics and findings of pre-approval and post-approval studies of drugs granted FDA Accelerated Approval between 2009 and 2013. We found that approximately half of required confirmatory studies were completed during the first 3 years on the market. Although many drugs recently granted Accelerated Approval have their efficacy “confirmed” in these post-approval trials, completed trials often evaluate disease progression rather than clinical outcomes. Remarkably, clinical trials conducted before and after Accelerated Approval have similar design features, including reliance on surrogate endpoints


Beyond the transitional justice paradigm: researching wartime violence and post-war justice in Bosnia and Herzegovina 
Date: 16 November 2017
Speaker: Daniela Lai, LSE.
Abstract: Whether socioeconomic justice belongs within the transitional justice framework is still a matter of contention among scholars. One of the major hurdles to be overcome is our limited understanding of how conflict-affected communities experience socioeconomic violence and injustice, and how socioeconomic issues become part of post-war justice processes. Addressing these questions poses significant methodological challenges due to the prevalent focus of the literature on physical, direct violence and legalistic approaches to transitional justice. In this presentation, Daniela discusses three methodological components of her efforts to address these challenges while researching experiences of wartime violence and emerging justice claims among local communities in Bosnia and Herzegovina. First, compared to much IR scholarship on transitional justice, her approach required foregrounding local experiences and interpretations. Using in-depth interviews, she was able to reconstruct the participants’ experiences and perceptions of socioeconomic injustice, and show how these were part of broader narratives about Bosnia’s past and its post-war transformation. Second, the project relied on a within-case comparison of two smaller cities, with the aim of redressing invalid part-to-whole inferences about the Bosnian case that are often based on the study on few, over-researched sites such as Sarajevo, Mostar or Srebrenica. Lastly, Daniela’s work addresses the ethical challenges posed by the long-term presence of the academic community alongside international organisations in Bosnia and Herzegovina. She calls for establishing non-exploitative relationships with research participants, and argues for the importance of contextual knowledge and language skills as part of research training. 

Date: 30 November 2017
Speaker: Jack Blumenau, UCL.

Title and abstract TBC.


Social and Economic Data Science Seminars

Data Science seminars returns in Michaelmas Term 2017 and are run in conjunction with the Departments of Mathematics and Statistics. These seminars take place from 16:15 - 17:45 in COL 8.13 (unless otherwise stated) with drinks and snacks at the end.


Normalizing Digital Trace Data

Date: 19 October 2017
Speaker: Andreas Jungherr
Abstract: Over the last ten years, social scientists have found themselves confronting a massive increase in available data sources. In the debates on how to use these new data, the research potential of “digital trace data” has featured prominently. While various commentators expect digital trace data to create a “measurement revolution”, empirical work has fallen somewhat short of these grand expectations. In fact, empirical research based on digital trace data is largely limited by the prevalence of two central fallacies: First, the n=all fallacy; second, the mirror fallacy. As I will argue, these fallacies can be addressed by developing a measurement theory for the use of digital trace data. For this, researchers will have to test the consequences of variations in research designs, account for sample problems arising from digital trace data, and explicitly link signals identified in digital trace data to sophisticated conceptualizations of social phenomena. Below, I will outline the two fallacies in greater detail. Then, I will discuss their consequences with regard to three general areas in the work with digital trace data in the social sciences: digital ethnography, proxies, and hybrids. In these sections, I will present selected prominent studies predominantly from political communication research. I will close by a short assessment of the road ahead and how these fallacies might be constructively addressed by the systematic development of a measurement theory for the work with digital trace data in the social sciences.


On Elicitation Complexity
26th October 2017
Speaker: Ian Kash, Microsoft Research
Abstract: Elicitation is the study of statistics or properties which are computable via empirical risk minimization. This has applications in understanding which loss function to use in a regression for a particular statistic or finding a surrogate loss function which is easier to optimize.

While several recent papers have approached the general question of which properties are elicitable, we suggest that this is the wrong question---all properties are elicitable by first eliciting the entire distribution or data set, and thus the important question is how elicitable. Specifically, what is the minimum number of regression parameters needed to compute the property?

Building on previous work, we introduce a new notion of elicitation complexity and lay the foundations for a calculus of elicitation. We establish several general results and techniques for proving upper and lower bounds on elicitation complexity. These results provide tight bounds for eliciting the Bayes risk of any loss, a large class of properties which includes spectral risk measures and several new properties of interest.

Joint work with Rafael Frongillo.

Date: 23 November 2017
Speaker: Vasilis Syrgkanis, Microsoft

Title and abstract TBC.


A Measure of Survey Mode Differences
Tuesday 5 December 2017
Speaker: Jeff Gill, Department of Government, American University, Washington DC
Abstract: Jeff will evaluate the effects of different survey modes on respondents' patterns of answers using an entropy measure of variability. While \emph{measures of centrality} show little differences between face-to-face and Internet surveys, he will find strong patterns of \emph{distributional differences} between these modes where Internet responses tend towards more diffuse positions due to lack of personal contact during the process and the social forces provided by that format. The results provide clear evidence that mode matters in modern survey research, and he will make recommendations for interpreting results from different modes.