「EVENT REVIEW」

While it feels like regression analysis is not getting much love these days in the social sciences, it is still foundational for survey analysis – the tool that causal inferential models utilize for most (if not all their) estimation and that serves as the basis of the fancy advanced techniques receiving most of the attention today in social science outlets. For this reason, it is not only worth learning regression foundations well, but it is important to understand how advanced techniques build upon regression to analyse more flexibly.

We will start by reviewing the assumptions of regression modeling with an eye on what could go wrong with the kind of data used in the social sciences. We will explore the estimators and link functions necessary to deal with binary, ordered, and unordered (multinomial) outcomes with a specific emphasis on the correct interpretation of such regression results. Second, we start to explore the complexities of various data structures that emerge in voting behavior research, such as cross-country surveys, repeated cross-sectional surveys, panel data, or other within-person analyses. We will approach this complexity through three approaches: clustered standard errors, fixed and random effects corrections, also venturing into the world of multilevel modeling. In addition, we will also consider the (admittedly frustrating) topic of survey weights and discuss considerations and limitations in their applications. Building on this knowledge, time allowing, we will address topics of nonresponse and use imputation procedures for item missing data.

Although we will not fully cover topics of GLM, panel data analysis, multilevel modeling, sampling and weights, missing data, we will highlight the foundations important to embark on any of these paths and have glance at (and take home some R code for) examples of such analyses. This workshop will have an applied focus and will not go into the mathematical foundations of mentioned tools.

Prerequisites

If you have done regressions before but feel like you are missing some foundations, you need a review, or you would like to build on the regression basics you have expanding into the advanced topics, you have come to the right workshop. If you have never done any regression analysis and would like to receive an introduction, this workshop is probably not for you just yet.

Basic R knowledge is strongly recommended. At least, you should be able to load a dataset and run a simple lm command. If you don’t know how to do this, please get there before the workshop. (This is not much, you can do it.) Also, once you know what you are doing, you can easily transfer your knowledge to the analysis tools of your preference.

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DATE & TIME: 18 - 21 March 2023, 9.30 - 11.00 a.m. & 11.30 a.m. - 1.00 p.m.

PLACE: Room FMA/01.19 - Feldkirchenstra?e 21

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Qualitative data can be very complex. Data coding allows one to organize, relate, and visualize data. Above all, it is a powerful tool to generate results in a structured and transparent fashion. Over decades, qualitative data coding has become much more advanced, not in the least because of the arrival of software packages that support it. This course introduces PhD students to qualitative data coding. The course addresses two aspects: the logic and operations of coding, and the use of software when coding data. We will address topics such as understanding the (layered) complexity of data, data exploration, discovery of patterns, structuring and visualization, and presentation of the results. This course is especially useful for those PhD students actively working with qualitative data, if only because there will be opportunities to explore and code one’s own dataset. Please note: the course is not just focused on how the software works. It very much is about getting coding right, regardless of whether software is involved or not.

Prerequisites

Please ensure that you have the software MAXQDA or AtlasTI installed. MAXQDA is supported by the University of Bamberg, but we can also use AtlasTI. Please ensure that you have a qualitative dataset available. This can be of any type (e.g., interview transcripts, newspaper articles, photos or videos). This dataset should be accessible on the same laptop that has the software installed. PhD students who followed the BAGSS course ‘Qualitative Research Design’ get priority in enrollment.

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DATE & TIME: 18 - 22 March 2024, 9.30 a.m. - 1.00 p.m.

PLACE:  FG1/00.06 - Feldkirchenstra?e 21

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In this course, students will learn the basics of machine learning, specifically for social scientists. While the course will introduce specific models, such as support vector machines, decisions trees, or lasso regressions, we will also focus on general principles that apply to all machine learning algorithms. This will equip students with the tools to not only apply machine learning in their own research, but also judge the performance of such models and adapt to new developments in the field. The course includes practical applications with code and examples from different social science fields.

Prerequisites

All code examples and applications will be done in R. The course requires basic knowledge of R, which includes being able to load and work with data. The code examples are based on the tidyverse (e.g. filter, mutate, summarise functions), but can be adjusted to base R without much trouble. No special mathematical knowledge beyond master level statistics and quantitative methods courses is required, the course focuses on the concepts of machine learning algorithms rather than the mathematical basis. The examples and lab session don’t require any in-depth knowledge of political science or any other field.

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DATE & TIME: 18 - 22 March 2024, 9.30 a.m. - 1.00 p.m.

PLACE: FMA/00.08 - Feldkirchenstra?e 21

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