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DTSTART;VALUE=DATE:20251006
DTEND;VALUE=DATE:20251011
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SUMMARY:Modern Approaches in Economic Research
DESCRIPTION:Dates and time: 6 October 2025 – 10 October \nCourse overview\nThis course will cover several recent methodological innovations within economics\, including designing information provision experiments\, the collection and analysis of open-ended survey data\, and modern AI tools. \nInformation provision experiments\nInformation provision experiments have become an increasingly common tool in economic research. One of the main goals of the course is to familiarize PhD students with best-practice methods for these types of experiments and provide an introduction to the research frontier. We will try to answer questions such as the following: \n• What is the typical structure of a survey experiment in economics?\n• How can I address concerns about demand effects\, social desirability bias\, and external validity?\n• What makes a survey experiment publishable in top journals?\n• How can recent advances in AI technology make survey experiments more powerful? \nThere is no good textbook on survey experiments in economics\, but there are three complementary reviews that will inform our approach to designing survey experiments\, namely Haaland et al. [2023]\, Stantcheva [2023]\, Fuster and Zafar [2023]\, Bursztyn et al. [2025]. ∗Department of Economics\, NHH Norwegian School of Economics. E-mail: Ingar.Haaland@nhh.no \nOpen-ended survey data\nIn addition to the proliferation of information provision experiments\, economists are increasingly using open-ended survey data to understand economic behavior [Haaland et al.\, 2024]. We will cover recent innovations within this topic\, including questions such as: \n• Best practices for collecting open-ended survey data\n• Advantages and disadvantages compared to structured survey responses\n• How to analyze open-ended survey data\n• How to scale the collection and analysis of open-ended data using recent AI tools. \nAI tools in economics research\nThe rapid rise of AI tools\, such as ChatGPT\, is not only transforming businesses and society [Mollick\, 2024] but also revolutionizing the way we conduct economic research. This transformation is fundamentally changing the ‘research production function\,” unlocking new opportunities to analyse and generate data in ways not possible just a few years ago [Korinek\, 2023]. We will cover recent innovations at the intersection of AI and economics and answer questions such as: \n• How can I integrate ChatGPT in my own research to become more productive?\n• What are some current use cases for AI in economics?\n• How can I contribute to the emerging research field at the intersection of AI and economics? While there is no textbook on AI and economics yet\, Korinek [2023] provides a good starting point. \nSelected research applications \nIn addition to a broad introduction to information provision experiments\, open-ended survey data\, and AI tools in economic research\, we will cover selected research articles to illustrate many of the key concepts\, including (but not limited to): \n• Using open-ended survey data to better understand narratives and mental models [Andre et al.\, 2022]\n• Using AI technology to collect qualitative data at scale\, including qualitative interviews [Chopra and Haaland\, 2023]\n• Leveraging AI technology to understand news consumption decisions [Chopra et al.\, 2024\, Braghieri et al.\, 2024]\nCourse lecturers: Ingar Haaland\, Professor in the Department of Economics at the Norwegian School of Economics (NHH). \nCourse organizers: Center for Economic Behaviour and Inequality (CEBI)\, Ida Maria Hartmann and Simon Kyllebæk Andersen. \nLanguage: English. \nECTS: 4. \nMax. numbers of participants: 40. \nPreparation: It is expected that the students read the references listed below\, prior to attending the course. \nRegistration: Please register via the link in the box no later than 31 August 2025 \nCourse fee: The course is free of charge for PhD students enrolled at the Faculty of Social Sciences\, Copenhagen University\, and for PhD students enrolled at one of the PhD schools participating in the DGPE research network. Other PhD students will be charged a course fee of DKK 5.000 kr. for participation in the course. \nFurther information: For more information about the PhD course\, please contact the PhD Administration (phd@hrsc.ku.dk). \nReferences\nPeter Andre\, Ingar Haaland\, Christopher Roth\, and Johannes Wohlfart. Narratives about the macroeconomy. Discussion Paper 17305\, CEPR\, 5 2022. \nLuca Braghieri\, Sarah Eichmeyer\, Ro’ee Levy\, Markus M. Mobius\, Jacob Steinhardt\, and Ruiqi Zhong. Article-level slant and polarization of news consumption on social media\, 2024. Available at SSRN: https://ssrn.com/abstract=4932600. \nLeonardo Bursztyn\, Ingar Haaland\, Nicolas Roever\, and Christopher Roth. The social desirability atlas. Unpublished manuscript\, March 2025. \nFelix Chopra and Ingar Haaland. Conducting qualitative interviews with AI. Working Paper 10666\, CESifo\, 2023. Available at SSRN: https://ssrn.com/abstract=4583756 or http://dx.doi.org/10.2139/ssrn.4583756. \nFelix Chopra\, Ingar Haaland\, Fabian Roeben\, Christopher Roth\, and Vanessa Sticher. Ai customization and the market for news\, 2024. Mimeo. \nAndreas Fuster and Basit Zafar. Survey experiments on economic expectations. In Handbook of Economic Expectations\, chapter 4\, pages 107–130. Elsevier\, 2023. doi: 10.1016/B978-0-12-822927-9.00010-0. \nIngar Haaland\, Christopher Roth\, and Johannes Wohlfart. Designing information provision experiments. Journal of Economic Literature\, 61(1):3–40\, 3 2023. doi: 10.1257/jel. 20211658. URL https://www.aeaweb.org/articles?id=10.1257/jel.20211658. \nIngar K Haaland\, Christopher Roth\, Stefanie Stantcheva\, and Johannes Wohlfart. Understanding economic behavior using open-ended survey data. Working Paper 32421\, National Bureau of Economic Research\, May 2024. URL http://www.nber.org/papers/w32421. \nAnton Korinek. Generative ai for economic research: Use cases and implications for economists. Journal of Economic Literature\, 61(4):1281–1317\, January 2023. doi: 10.1257/jel.20231736. Ethan Mollick. Co-Intelligence: Living and Working with AI. Portfolio\, New York\, 1st edition\, 2024. ISBN 9780593656656. \nStefanie Stantcheva. How to run surveys: A guide to creating your own identifying variation and revealing the invisible. Annual Review of Economics\, 15:205–234\, 2023. doi: 10.1146/ annurev-economics-091622-010157. First published online March 27\, 202 \nDisclaimer:\nDDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/modern-approaches-in-economic-research/
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20241106
DTEND;VALUE=DATE:20241121
DTSTAMP:20260404T005854
CREATED:20240424T090309Z
LAST-MODIFIED:20240424T090309Z
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SUMMARY:Hands-on Introduction to Data Management Plans (DMPs)
DESCRIPTION:Copenhagen Centre for Social Data Science \nDates and time:\n Day 1 – Wednesday\, 6 November 2024 – 2:00 pm – 4:30 pm \n Day 2 – Wednesday\, 20 November 2024 – 2:00 pm – 4:00 pm \nGood research data management (RDM) practices are an essential part of good research practices. They increase the efficiency and quality of data collections and ensure secure handling and storage of data (to prevent data breaches\, misuse\, or loss) as well as compliance with requirements by research institutions\, publishers\, funders\, and legislation (e.g. GDPR). This course provides a hands-on training on best practices in managing research data (including quantitative and qualitative data)\, during which the course participants draft data management plans (DMPs) for their current research projects. \nAcademic aim: \n– Introduction to basic concepts and best practices in RDM\n – Drafting a DMP for a current research project and attaining the skills to write DMPs with ease\n – Gaining further knowledge about RDM requirements by the University of Copenhagen (UCPH)\, publishers\, funders\, and legislation (e.g. GDPR)\, and how to fulfil these  \nTarget group:  \nThe course is aimed at PhD students from the social sciences. It will among other things refer to specific requirements and processes at the UCPH and UCPH-Faculty of Social Sciences. It is\, however\, open to all PhD students and researchers who are looking for a hands-on training on best practices in RDM and drafting DMPs. No prior knowledge is required. \nCourse organisers and teachers:  \n– Siri Völker\, Special Consultant\, Data Manager at the Social Sciences Data Lab\, Copenhagen Center for Social Data Science\, University of Copenhagen\n – Joseph Burgess\, Senior Consultant\, Data Steward at SODAS and Team Leader at the Social Sciences Data Lab\, Copenhagen Center for Social Data Science\, University of Copenhagen\n   \nProgramme: \nDay 1 (6 November 2024) – Introduction to research data management (RDM) and data management plans (DMPs) \n2:00 – 2:45 pm  \n – Introduction to research data management (RDM):\n – What is RDM?\n – Why is RDM important?\n – Requirements for good RDM practices by the UCPH\, publishers\, funders\, and legislation (e.g. GDPR)\n – UCPH Policy for RDM and related processes\n – UCPH support services \n2:45 – 3:45 pm\n – Introduction to data management plans (DMPs):\n – What is a DMP?\n – Why is a DMP important?\n – How to draft a DMP? \n3:45 – 4:00 pm\n – The course participants receive the assignment to draft DMPs for their respective PhD projects to be discussed on day 2 \n4:00 – 4:30 pm\n – Voluntary: The course participants can start to work on their DMPs and clarify related questions with the presenters/data management experts \n Day 2 (20 November 2024) – Practical discussion of DMPs \n2:00 – 2:30 pm\n – Presentation of a best practice DMP \n2:30 – 3:45 pm\n – Discussion of the participants’ DMPs in groups with data management experts available to answer open questions \n3:45 – 4:00 pm\n – Wrap-up \n Language: English \nECTS: 0.5 \nMax. numbers of participants: 20 \nCourse fee: Free for PhD students at all Danish Universities\, except PhD students at CBS who will be charged a DKK 600 fee (1.200 per ECTS). \nRegistration: Please register via the link in the box no later than 2 October 2024 \nFurther information: For more information about the PhD course\, please contact the PhD Administration (phd@hrsc.ku.dk). \n  \nLiterature: \n– UCPH Policy for Research Data Management (intranet)\n – UCPH Data Management Plan Template v2.1 (intranet) \n  \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/hands-on-introduction-to-data-management-plans-dmps/
LOCATION:City Campus (CSS)\, Øster Farimagsgade 5\,                Room: TBA
CATEGORIES:PhD Course
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20240827
DTEND;VALUE=DATE:20240830
DTSTAMP:20260404T005854
CREATED:20240424T084517Z
LAST-MODIFIED:20240424T084517Z
UID:10001175-1724716800-1724975999@ddsa.dk
SUMMARY:Introduction to Machine Learning for the Social Sciences
DESCRIPTION:Copenhagen Centre for Social Data Science \nDates and time:\n 27-29 August 2024 from 9 am to 4 pm. \nThis course will introduce the basics of big data and machine learning and how it can be used in the context of social science research. No prior knowledge is assumed\, and it is well-suited for people with no prior experience using big data or machine learning. The course will cover different topics including how big data methods differ from inferential statistics\, data cleaning and pre-processing\, different machine learning models and explainable AI methods\, data ethics and privacy\, as well as bias and responsible AI. \nCore machine learning principles such as cross-validation\, out-of-sample prediction\, and hyper-parameter tuning will be introduced. A key focus will be on interpretable machine learning models such as regression-based models\, decision trees\, and random forests.        \nThe assessment will involve completing a machine learning analysis\, either in Python or in R. Therefore\, some prior experience with one of these coding languages is preferred. A basic understanding of inferential statistics is also preferred. Participants can bring their own data or use the data that will be provided.  \n Academic Aim:\n – To think critically about when Big Data and machine learning should be used for social science research (assess advantages\, disadvantages\, and limitations)\n – To be able to perform a machine learning analysis \nTarget group: Social science background. To gain most from the course some prior experience with R or Python and a basic understanding of inferential statistics is ideal. \nCourse organiser: Rosa Ellen Lavelle-Hill\, Assistant Professor\, Department of Psychology\, University of Copenhagen \n Disclaimer:DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
URL:https://ddsa.dk/event/introduction-to-machine-learning-for-the-social-sciences/
LOCATION:City Campus (CSS)\, Øster Farimagsgade 5\,                Room: TBA
CATEGORIES:PhD Course
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