Stata training and workshops

This course is designed as an introduction to modern difference-in-differences (DiD) methods using Stata. It provides an applied introduction to recent developments in DiD, with particular emphasis on the limitations of conventional unit and time fixed- effects models (two-way fixed effects, TWFE) when treatment adoption is staggered and treatment effects are heterogeneous across groups or evolve dynamically over time. The theory is combined with a replication of Aaronson and Mazumder’s (2011) study of the Rosenwald schools and with exercises comparing TWFE with modern DiD estimators.

This course introduces multilevel/mixed modeling for nested and longitudinal data and its implementation in Stata. Mixed models contain both fixed effects, analogous to regression coefficients, and random effects, effects that vary across clusters. Participants will learn how to use mixed models to answer research questions about the observation- and cluster-level data and how to disaggregate these effects. Introductory theory, estimation, model building, and diagnostics will be discussed and demonstrated through many examples.

This workshop introduces economists to methods for analysing text and other forms of unstructured data using Python. Participants will learn how to transform text documents into structured datasets, apply natural language processing (NLP) and machine learning methods, and incorporate text-based measures into empirical economic research.

This course provides an overview of Longitudinal Data Analysis. As well as the statistical theory, an overview of the many applications and capabilities of LDA is given. The course is not particularly mathematical, but instead places emphasis on the fundamental concepts of LDA and how it is used by applied researchers. Exercises for this workshop will be conducted in Stata. Stata has been chosen as it provides both basic and advanced functionality for conducting longitudinal data analyses.

This 5-day course aims to provide you with the understanding and experience to undertake a basic research project in the social or health sciences using Stata as the statistical tool. The course is suitable for beginners to the Stata package and will be presented in a way that introduces survey research. It is also appropriate to those familiar with Stata as it extends the capabilities of more experienced researchers.

This practical course focuses on the selection, interpretation, and presentation of treatment-effect estimates produced by widely used causal inference methods. Rather than concentrating on the mathematical derivation of estimators, the course helps participants understand which method is appropriate for a particular research question, data structure, and identification strategy, and how the resulting estimates should be interpreted.

Multiple regression is a statistical workhorse of the social sciences. This three-day short course is designed for participants who want to strengthen their understanding of regression analysis and apply it confidently in academic research. Participants may use the statistical package they prefer. The course notes support users of SPSS, Stata and R.

Exploring dynamic and static panel time series models with hands-on Stata applications. This two-day online course introduces participants to time series panel data models, focusing on key challenges such as slope heterogeneity, structural breaks, and cross-sectional dependence. Through intuitive instruction and hands-on Stata exercises, attendees will learn to estimate and interpret static and dynamic models for large panels in applied economics contexts.

This course introduces structural equation modeling and its implementation in Stata. Stata allows for fitting structural equation models in two ways—by using the command syntax or using the SEM Builder to draw path diagrams. Examples will demonstrate both approaches, starting with linear regression up to confirmatory factor analysis and growth curve modeling. Model identification and evaluation will also be extensively discussed. The course concludes with a brief introduction to multilevel models and generalized linear models within the SEM framework.

This course provides an introduction to the theory and practice of panel-data analysis. After introducing the fixed-effects and random-effects approaches to unobserved individual-level heterogeneity, the course covers linear models with exogenous covariates, linear models with endogenous variables, dynamic linear models, and some nonlinear models. A quick introduction to the generalized method of moments estimation technique is also included. The differences between the individual-specific and population-averaged interpretations are discussed throughout the course. Concepts are extensively illustrated using exercises and examples worked in Stata.