Introduction to Structural Equation Modelling

Dr James Hall · University of Southampton

An introduction to Structural Equation Modelling for researchers with a background in regression or factor analysis, covering path analysis, latent variable estimation, measurement and structural models, model fit, and basic SEM implementation in R using the lavaan package and the HolzingerSwineford1939 dataset.

Introduction to Assessment Data: Classical Test Theory and Item Response Theory

Prof Christian Bokhove · University of Southampton

In this session we explore how we can analyse assessment data within the frameworks of Classical Test Theory and Item Response Theory. We start with simple data and models, and then slowly build our understanding towards more complex models. We do this within the statistical package R within RStudio. To try the analyses yourself, install this open-source software and download the script and sample datasets from the Data link on the right.

Introduction to Social Network Analysis

Dr Natalia Lavrushkina · Bournemouth University

An applied introduction to Social Network Analysis for researchers interested in studying relationships, collaboration, advice, communication and knowledge-sharing. The session covers core SNA concepts, network visualisation, basic metrics, ethical issues in network data collection, and includes a guided Polinode practical using an example email communication dataset.

Working through the practical: The session included a guided practical using Polinode, which is web-based and needs no installation. To repeat it, read the description of the example dataset: en.wikipedia.org/wiki/Enron_Corpus and set up a free individual account with Polinode: polinode.com.

Introduction to Multilevel Modelling

Dr Laone Maphane · NCRM / University of Southampton

An introduction to multilevel modelling for researchers working with nested or hierarchical data, such as students within schools. The session covers the rationale for multilevel approaches, intraclass correlation, random intercept models, pupil- and school-level predictors, within- and between-group decomposition, and model diagnostics, with a guided practical in R using the lme4 package and a real secondary school examination dataset.

Introduction to Machine Learning

Mr Brody Hannan · University of Southampton

A hands-on introduction to machine learning for researchers with a traditional statistics background, covering decision trees, random forests, gradient boosting, and clustering using real-world census data. Includes a lecture covering theory and key concepts, before a guided workshop using US Census data.

Introduction to Modelling Causal Inference

Dr Yin Wang · NCRM / University of Southampton

An introduction to causal inference for researchers working with observational data, with a focus on Synthetic Difference-in-Differences (SDID). The session covers the causal identification problem, the difference-in-differences framework, how SDID constructs a weighted synthetic counterfactual, uncertainty estimation via placebo reassignment, and result visualisation, with a guided practical in R using the synthdid package and the California Proposition 99 cigarette consumption dataset.

Making better decisions: analysing attributes of individuals who engage effectively with ideas

Prof Chris Brown · University of Southampton

Introduction to Structural Equation Modelling

Dr James Hall · University of Southampton

SEM for Growth Modelling

Dr James Hall · University of Southampton

Making Statistical Methods Matter: lessons from educational research

Panel Discussion

Multilevel Modelling

Prof Chris Downey · University of Southampton

Causal Inference in Observational Data

Dr Yin Wang · University of Southampton

Machine Learning for Social Scientists

Dr James Hall · University of Southampton

Item Response Theory (IRT)

Prof Christian Bokhove · University of Southampton

Connecting Research and Practice: collaboration opportunities with Southampton

QMEG Group