Complex Large-Scale Data Analysis -A Course Series on Traditional Statistical Modellin

In the era of big data and rapidly advancing AI technologies, the real challenge is no longer whether data exist, but how to extract credible, interpretable, and policy-relevant evidence from massive and highly complex datasets. This four-course series uses International Large-Scale Assessment (ILSA) data as its central example to guide participants—from beginners to those with prior analytical experience—through the process of analysing complex large-scale data in the social sciences. Confronting the challenges inherent in complex secondary datasets—such as multilevel structures, cross-national sampling, complex weighting schemes and latent constructs—the series moves from foundational concepts to advanced applications, covering traditional statistical methods (e.g. multilevel models, structural equation modelling, causal inference models), machine learning techniques, and emerging uses of generative AI in data analysis. Participants will develop an understanding of the differences, strengths, limitations, and complementarities of these approaches within the context of secondary data analysis.

This is a series of four courses and more information on each course/session can be found using the links below.

• Introduction to Machine Learning and AI Methods to analyse large-scale data: An example using International Large-Scale Assessment Data
• Can Machines Really Understand Data? — Using Machine Learning and AI methods to Analyse Survey and International Large-Scale Assessment Data
• How Can Generative AI (LLMs) Help with Analysing Survey and International Large-Scale Assessment Data
• Which analytical choices are better? – Comparing Traditional Statistical Methods with Machine Learning and AI-supported methods

You may register for any number of sessions individually. If you choose to register for all four, a discount will be applied.

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