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Autumn Methods Workshops

Organised by the faculty of social sciences autumn 2018
The Faculty of Social Sciences offers four Autumn Workshops in Methods and Methodologies. These are especially for instructors and researchers (including doctoral candidates and postdocs) of Lund University's Faculty of Social Sciences, although the extra spots will be given to students if available. If you are a staff member or student of the Faculty of Social Sciences, these workshops are free of charge (for others, the cost varies by workshop). And yes, you may sign up for more than one.

Autumn Workshops in Methods and Methodologies.

The opportunities are (details can be foud below):

1. Getting more out of your interviews: a workshop on reconstructive interpretation

Instructor: Rafael Mrowczynski, Leipzig University, Institute for Cultural Studies & Käte Hamburger Center for Advanced Studies in Humanities “Law as Culture” at Bonn University.
Dates: Two consecutive days between 1 October to 12 October, 2018 (multiple groups).
 

2. Introduction to R

Instructor: Irina Vartanova, Institute for Futures Studies, Stockholm.
Dates: 3, 4, 5, and 8 October, 2018 (4 days).
 

3. Introduction to Set-Theoretic Methods and Qualitative Comparative Analysis (QCA)

Instructor: Ioana-Elena Oana, Central European University, Budapest.
Dates: 9 to 12 October, 2018 (4 days)
 

4. Introduction to Machine Learning for Social Scientists

Instructor: Alejandro Quiroz Flores, University of Essex.
Dates: 1 to 2 October, 2018 (1.5 days)


You will need to sign up by 22 August, 2018 using the following link: Click here.

Please direct any unanswered questions to christopher [dot] swader [at] soc [dot] lu [dot] se.


More details

Getting more out of your interviews: a workshop on reconstructive interpretation

Instructor: Rafael Mrowczynski (Leipzig University, Institute for Cultural Studies & Käte Hamburger Center for Advanced Studies in Humanities “Law as Culture” at Bonn University).
Dates: Two consecutive days between 1 October to 12 October, 2018 (to be determined)
Location: G236 (next to the 2nd floor lunchroom, Building G, Sociology)

The aims of the workshop are (1) to introduce participants to the “reconstructive approach” in qualitative social research with a particular focus on the “documentary method of interpretation” and (2) to conduct exemplary sequential interpretations of interview segments provided by workshop participants.

The major goal of the analytical approach introduced in the workshop is the reconstruction of interpretative categories and orientations which underlie interviewee’s explicit statements. It is assumed that these interpretative categories and orientations are also relevant for research participants’ actions outside of research-related settings (interviews). Even if the main analytical procedure within a research project is the qualitative content analysis (i.e., the focus is on what was said about particular social phenomena, processes or personal experiences), the reconstructive approach can help to identify conceptual “filters” or “frames” used by different interviewees when presenting their stories or interpretations of social reality (or specific aspects thereof). Hence, the workshop can be very helpful for all social researchers working currently with interview data and interested in getting support in their interpretative processes.

Rafael’s own current research focuses on professional orientations of lawyers in post-socialist countries (Poland & Russia) and is primarily based in its empirical part on the documentary interpretation of autobiographic narrative interviews. His next project in cooperation with colleagues from Freie Universität Berlin will deal with individual and collective orientations of tax officials in post-Soviet states.
 

Introduction to R

Instructor: Irina Vartanova, Institute for Futures Studies, Stockholm
Dates: 3, 4, 5, and 8 October, 2018 (4 days)
Location: October 3-5: room G335 and October 8: room G417, Department of Sociology

In the last 5 years, R became one of the most popular analytical tools in academia. It provides a platform for cutting edge statistical methods and full flexibility of visualization at no cost. However, R remains intimidating for beginners with no experience in programming. Recent developments have helped make writing R code more intuitive and easier to learn. The purpose of the workshop is to learn the basics of data analysis in R environment in an engaging way that will help to manage common hurdles as quickly as possible. During the four days, you will learn how to import and clean your data, how to apply the most common statistical methods, as well as how to visualize and report the results most effectively. We will cover many tips that will help you write clear R code which produce accurate and reproducible results. A large allocation of time will be devoted to hands on work to gain sufficient experience to implement relevant methods in your own research.

Irina Vartanova is a researcher at the Institute for Futures Studies. Having completed a PhD in psychology, she applies her data analysis expertise to working in an interdisciplinary environment and collaborating with colleagues from different fields. She specializes in advanced statistical and computational methods in social research and has a lot of experience in analyzing large survey data. Her research primarily focuses on social norms and how they change in different cultures. In her free time, she likes to learn new tricks to make her R code clearer and faster.
 

Introduction to Set-Theoretic Methods and Qualitative Comparative Analysis (QCA)

Instructor: Ioana-Elena Oana, Central European University, Budapest.
Dates: 9 to 12 October, 2018 (4 days)
Location: G405, Department of Sociology.

This workshop aims to introduce participants to set-theoretic methods and Qualitative Comparative Analysis – QCA as a research approach and as a technique for analyzing complex causal patterns in social science data. In introducing set-theoretic methods as a research approach, the course will focus on notions of causal complexity (conjunctural, asymmetrical, equifinal causation), and notions of sufficiency and necessity central to these approaches. Additionally, we will discuss differences between QCA and correlation-based techniques such as linear regression, together with more general issues such as case selection, scope conditions, and concept formation for QCA. In introducing QCA as a case-oriented analysis technique for social science data, the course will familiarize students with the basic analytical tools available for studying relationships of necessity and sufficiency. Basics of formal logic and Boolean algebra, together with the central analytical tool of QCA, the “truth-table” analysis are introduced. For enabling participants to independently run their own QCA analyses, the course also includes optional lab seminars presenting the R packages QCA and SetMethods. These lab sessions will include a short introduction to R for new users and hand-on exercises, including replications of published QCA studies discussed in the theoretical part of the class.

Nena (Ioana-Elena) Oana is a final-stage PhD Researcher in Comparative Politics at the Central European University in Budapest where she is currently working on the impact of different forms of political participation. She is the main developer of the R package SetMethods for Set-Theoretic Multi-Method Research and Advanced QCA. Nena has extensive experience in teaching QCA using R programming language, having taught and assisted for the ECPR Summer and Winter School QCA courses in the past 5 years, at workshops at European University Institute, Florence, and at the University of Exeter. Besides research methodology, her main research interests include social movements, political participation and representation, political behaviour, and political psychology.
 

Introduction to Machine Learning for Social Scientists

Instructor: Alejandro Quiroz Flores, University of Essex.
Dates: 1 to 2 October, 2018 (2 days)
Location: room G125, Department of Sociology

The advent of “big data” has revitalized our interest in machine learning methods. Machine learning techniques are particularly useful because they can overcome the limitations of more traditional econometric models while improving prediction and validity. This short course covers two fundamental machine learning models: Random Forests and Boosting. These models are introduced in the context of both regression and classification, and compared to traditional prediction tools and cross-validation. The course emphasizes the intuition behind Random Forests and Boosting rather than the mathematical aspects of the models, and it highlights applications and implementation in the social sciences. However, the course requires a good understanding of the linear model as well as discrete choice models such as probit and logit. We will use R as software and therefore it is recommended that attendants who want to learn to run the models are familiar with the R language.

Dr Alejandro Quiroz Flores is a Senior Lecturer in the Department of Government at the University of Essex. His areas of research include Political Economy, the Politics and Economics of Natural Disasters, Leader Survival, and Econometrics.

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