About us:

At the SODA lab, we care about the thoughtful use of data, from its collection and analysis to decision-making based on the available evidence. Our goal is to critically reflect on, interrogate and improve the use of data in social science research and beyond.

We work together as a team of researchers from many different fields, and collaborate closely with colleagues from other institutions, with practitioners and policy-makers. Between us, we combine a multitude of backgrounds, varied experiences and different knowledge. We bring these to bear on a wide range of exciting projects, many of which have direct relevance to social issues.

One of our areas of expertise and active research are (web) surveys and the data gathered through them, which are critical for many public and scientific endeavors. For example, it is vital that surveys capture what they were designed to measure, and that misunderstandings and mistakes be avoided. We know that paradata (broadly, information collected during a survey that goes beyond the responses), and more specifically the interaction of participants with an online survey as reflected in, e.g., mouse movements, can indicate the presence of difficulties. Therefore, we would like to better understand and use this data source to detect and predict issues with questionnaires, and – in close collaboration with survey practitioners – investigate the application of interaction paradata measures in the field.

As a team, we believe that diversity – in ages, abilities, ethnicities, sexual orientations, gender identities, and perspectives – makes not only for a richer life together, but also for a better team outcome. We know that people do their best work when they feel like they belong, when they are valued as individuals and treated as peers. We value empathy and honesty, care for one another and the work we do. We try to foster a collegial and collaborative environment where everyone brings their full selves to work, knowing that they’ll be supported to succeed. We hope you’ll join us!

We are looking for you:

PhD position in survey methodology, for research on survey interaction paradata collection, and analysis (m/f/x)

in Munich

Your tasks and responsibilities:

In this position, you’ll work with us to extend, evaluate and apply novel analysis techniques for interaction paradata. Our goal is to investigate whether such techniques can be widely applied in surveys. You will work alongside survey practitioners, statisticians and research software developers, contribute your own knowledge and collect more, all in order to make this happen.
Your tasks will include the following:

• Collaborating closely with survey practitioners and panel providers worldwide to design and implement paradata collection efforts.
• Analyzing the collected paradata in order to answer substantive questions about survey participants’ behavior.
• Engaging with the statistical literature to identify promising methods, evaluating them in the context of survey practitioners’ needs, applying (and maybe extending) them, and helping make statistical techniques more accessible to survey researchers.
• Presenting and discussing your work as an active member of the scientific community, for instance through conferences and articles.
• Developing new relationships with survey providers, and discussing the possibilities and uses of paradata with practitioners.
• Participating in the work of our lab, contributing to our joint work as well as our teaching and training programme.

Your qualifications:

Our group has members from multiple disciplines, and not every member (and especially not every new one) has worn all hats. Please treat the following as a rough sketch of the profile, and you are welcome to apply even if you do not check all boxes. Beyond the formal requirements of the PhD, here are some skills you can show in your application:

You have a university degree (e.g. Master’s, Diploma, or Magister) in a quantitative empirical discipline, with an excellent grasp of and hands-on experience with the methods of empirical social science (broadly defined).

You bring a strong interest in working with survey data, while going beyond the more traditional set of analysis techniques in this domain. You are interested in (learning) advanced data analysis techniques and are willing to develop deep knowledge of this field.

You are proficient in R or Python. If you haven’t/don’t already, you are willing to learn and implement best practices for robustness, reproducibility and transparency in your analyses.

You communicate excellently with individuals of all stripes, and are clear and thoughtful in speech and writing. You are open to engaging and sustaining close collaborations with project partners, and being a reliable project member yourself. You are willing to share your findings and knowledge as papers and presentations, through teaching, training and support. In doing so, you translate between communities, learning to explain domain issues to statisticians, and complex methods to expert practitioners.

You enjoy working with others, the creativity and collaboration that is at the heart of the scientific enterprise. You pay attention to details and appreciate excellence. You help others learn and grow, and provide kind and constructive feedback.

Benefits:

• Working in a collaborative, creative, inspiring international team with world-leading experts in the very center of Munich
• Manifold opportunities for learning and developing projects, networking and professional development at a thriving institute and internationally renowned research university.
• Excellent structured graduate programmes such as those at the Munich Center for Machine Learning and the Zuse School for Reliable AI.
• Opportunity to participate in and help organize outreach events and community activities run by our lab and its members, such as Women in Data Science, Data Science for Social Good, and DataFest Germany.
• A strong local network of social scientists, as well as a bustling machine learning community spanning not only LMU but also the Technical University of Munich, and many further research facilities as well as public sector and industry partners.
• Flexible work time and workplace models. For example, this job is available in part-time capacity. Likewise, the LMU Family Service supports our colleagues.
• Remuneration is based on the Collective Agreement for the Public Sector of the Länder (TV-L) including all allowances customary in the public sector.

LMU is committed to the diversity of its employees. We actively promote increased representation of scholars from communities that have been underrepresented in academia in the past.

Also possible in a part-time capacity.

Contact:

For questions about the position, please write to Felix at felix.henninger@lmu.de

Applications should be written entirely in English and should include:

• A letter explaining why you’d like to be part of our team and how you would like to contribute to the project goals.
• Your curriculum vitæ, and ideally information for two professional references who can speak to your qualifications and work experience.
• If available, links to code or articles you have written, or other work you are proud of. If you have a portfolio, please be sure to point us to it!
• A transcript of records of your last two degrees (we count university qualification results as one).

These materials should be collated into a single PDF document and sent to soda.research@stat.uni-muenchen.de until January 31st, 2023. The subject line of the email should be “Paradata – PhD Position – application” (please include this phrase in the subject line verbatim, as this is necessary to ensure that your application is processed correctly).

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