course-schedule

AI for Research (Seminar)

Inhalt: NFDI4DataScience and the AIX Lab at Leuphana University Lüneburg are hosting their third summer school on Artificial Intelligence and Knowledge Graphs for Scholarly Data. Join us for two days (noon-to-noon) of interesting discussions and insights, networking, and summer vibes on the beautiful Leuphana campus. Under the theme "AI for Research", the third edition of AIKG SD focuses on building trustworthy, explainable, and reproducible AI-driven research tools. Besides, it covers the overarching role of Artificial Intelligence (AI) and Knowledge Graphs (KGs) in the scholarly landscape, particularly on integrating AI solutions with domain-specific KGs and curated scholarly data to support researchers throughout the research lifecycle.

Introduction to latent variable models in empirical social research (Seminar)

Dozent/in: Marit Kristine List

Inhalt: In various disciplines of quantitative empirical social research, models with latent variables (latent variable models [LVM]) play a central role in the analysis of social science constructs (e.g. abilities, characteristics or attitudes of individuals). In most cases, the measurement assumes that the observed behaviour in a specific situation (manifest variable, e.g. answers in a test) can be used to draw conclusions about generalisable, more broadly defined, not directly observable constructs (latent variable, e.g. underlying ability). The course provides an overview of LMVs for analysing relationships between latent variables and latent or manifest variables: the various model classes (structural equation models, factor analyses, latent profile analyses, latent class analyses, item response theory) are introduced and discussed using empirical examples. Possible challenges in modelling and parameter estimation are addressed and the test theory concepts relevant to LMVs are discussed. Using sample data, the basics of modelling using LMVs are taught and practised. The course is aimed at people with a basic knowledge of statistics and test theory (from their bachelor's degree). Participants should be familiar with the basic principles of regression analysis and (exploratory) factor analysis, as well as those of classical test theory. A basic knowledge of the data analysis software R is also required. In the first session (see dates), the course schedule will be discussed and the participants' prior knowledge will be assessed. There is the option of setting specific focal points on the block days, which will also be discussed in the first session.

Introduction to statistics (PhD) (Seminar)

Dozent/in: Henrik Wehrden

Inhalt: Introduction to statistics

Qualitative Methods (Seminar)

Dozent/in: Elke Schüßler

Inhalt: Qualitative research is a research strategy that emphasizes large bodies of unstructured data (textual, graphical, audio, and video data) that cannot be meaningfully analyzed by formal, statistical approaches. Despite differences, qualitative research approaches share at least the following two assumptions: (1) by systematically generating and analyzing data new theory can be discovered (inductive view), and (2) this theory stresses the understanding of the socio-economic world through an examination of interpretation of that world by its participants. Since each particular research method is informed by different philosophical traditions, this course will first introduce different perspectives on the socio-economic research process like positivism, interpretativism, realism, and postmodernism. Students will then learn how to set up a qualitative research project using different research design perspective, e.g. a case study approach, an ethnographic study, grounded theory, or a discourse analysis approach. In addition, the course will tackle different qualitative methods of data collection and analysis, including a reflection about practical and ethical considerations. The course is designed for doctoral students and advanced master's students with a strong interest in research. Throughout the course, initiative, creativity, and critical thinking on part of the students will be appreciated and encouraged. The course aims to facilitate students' progress in their thesis project.

Quantitative Research Methods (Seminar)

Dozent/in: Laura Venz

Inhalt: Quantitative research is a research strategy that focuses on the systematic collection and analysis of numerical data to test theories, examine relationships between variables, and draw generalizable conclusions. It is typically grounded in a deductive logic, where hypotheses are derived from theory and empirically tested using structured and standardized procedures. At its core, quantitative research aims to ensure objectivity, reliability, and validity in the investigation of socio-economic, psychological, and related phenomena. Beyond statistical analysis, quantitative research fundamentally relies on the rigorous design of data collection processes. This includes the careful operationalization of constructs, the development or selection of measurement instruments, and the planning and implementation of studies in both laboratory and field settings. High-quality quantitative research therefore depends not only on statistical-analytical techniques but also on the quality and integrity of the data generation process. This course focuses on the latter. The course starts with a (hybrid) kick-off session in which participants’ needs, expectations, and research interests are discussed. Based on this session, specific topics in quantitative research methodology are identified and assigned. Participants will then work individually or in pairs to prepare and lead a 60-minute session on their topic during the three-day block seminar in July. All sessions are expected to be grounded in current scientific literature. The course addresses the quantitative research process, key aspects of research design (including experimental, quasi-experimental, and observational approaches), as well as the strengths and limitations of these designs. It further covers practical challenges in data collection (e.g., data quality) and common challenges of quantitative research, such as issues of measurement validity, sampling biases, and causal inference. In addition, principles of good scientific practice and research ethics are discussed, including transparency, reproducibility, and responsible data handling. The focus is on developing a deeper understanding of how high-quality quantitative data are generated in both laboratory and field contexts. A distinctive feature of the course is its interactive and student-driven format: following the kick-off session, participants develop and lead their own sessions on selected topics, which are discussed collectively during the block seminar. The course is designed for doctoral students and advanced master’s students with a strong interest in quantitative research. Initiative, creativity, and critical thinking are expected and encouraged. The course aims to support participants in advancing their own research projects by strengthening their methodological competence in quantitative research.

Think Mathematically, Act Algorithmically: Optimization Techniques (PhD) (Seminar)

Dozent/in: Paolo Mercorelli

Inhalt: MATLAB is a platform for scientific calculation and high-level programming which uses an interactive environment that allows to conduct complex calculation tasks more efficiently than with traditional languages, such as C, C++ and FORTRAN. It is the one of the most popular platforms currently used in the sciences and engineering. Optimization techniques can be applied in a wide interdisciplinary range of applications in control, estimation and identification problems. Using Matlab, the course embraces the most important optimization techniques and methods which can be used in control systems. The proposed techniques, e.g. Model Predictive Control, Two Point Boundary Optimization Problem, etc. are implemented using Matlab/Simulink. Through discussions on the mathematical origin of the proposed techniques and methods and their inspiring ideas the students should reach, not only a good command on the software, but also a critical spirit to interpret the nature of the algorithms and to reflect on their foundations and structures.

Course Schedule

Lehrveranstaltungen