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Learn about Bayesian analysis and how to perform it in Stata. The course will cover fundamental Bayesian concepts and contrast them with classical frequentist concepts. You will learn about Bayesian statistics, including how to construct and estimate posterior distributions, select priors, and perform Bayesian inference. You will become familiar with Bayesian computation using Markov chain Monte Carlo (MCMC) methods and with how to investigate convergence and efficiency of MCMC. You will learn how to compute various posterior summaries, compare Bayesian models, and perform Bayesian hypothesis testing. You will also learn how to fit Bayesian regression models to different types of outcomes. By the end of the course, you should be comfortable performing basic Bayesian analysis in Stata.

Learn how to use Stata's treatment-effects estimators to estimate the effect caused by getting one treatment instead of another in observational data. We will discuss how observational data differ from experimental data and use the potential-outcomes framework to obtain the average treatment effect and the average treatment effect on the treated using a variety of estimators, including those suitable for endogenously assigned treatment. We will cover the conceptual and theoretical underpinnings of treatment effects as well as many examples using Stata.

Presenting results effectively is a crucial step in statistical analyses. With new features introduced in Stata 17 and 18, you can now create publication-quality tables using Stata’s built-in tools. These tools include the reimagined table command, the new etable command, the new dtable command, and the new collect suite of commands. These are all based on the collect system and can be used independently or collaboratively.

In this course, you will learn how to incorporate results such as summary statistics or regression results into tables and into complete reports. Upon completion of this course, you will be able to customize the layout, formatting, and style of tables and export them to Word®, Excel®, LaTeX, HTML, and other formats. You will also be able to incorporate tables into full reports, including Stata results, graphs, and customized text in Word and Excel.

This course will cover the use of Stata to perform multiple-imputation (MI) analysis. MI is a simulation-based technique for handling missing data. The course will provide a brief introduction to MI and will focus on how to perform MI in Stata using the mi command. The three stages of MI (imputation, complete-data analysis, and pooling) will be discussed in detail with accompanying Stata examples. Various imputation techniques will be discussed, including multivariate normal imputation (MVN) and MI using chained equations (MICE). Also, a number of examples demonstrating how to efficiently manage multiply imputed data within Stata will be provided. Linear and logistic regression analysis of multiply imputed data as well as several postestimation features will be presented.

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 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 excercises and examples worked in Stata.

Regression modeling is a fundamental tool for researchers who want to establish causal quantitative relationships from observational data. This course is intended as an overview of the theoretical concepts necessary to understand regression models and how to implement them using Stata. The concepts will be reinforced with exercises that the participants will work on with the assistance of the instructor.

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 covers how to use Stata for survey data analysis assuming a fixed population. It begins by reviewing the sampling methods used to collect survey data and then discusses how they act in the estimation of totals, ratios, and regression coefficients. It then covers variance estimation methods implemented in Stata’s survey estimation commands. The course will also cover strata with a single sampling unit, certainty sampling units, subpopulation estimation, and poststratification. Interactive Stata sessions are dispersed between lectures.

Learn how to effectively analyze survival-time data using Stata. This training introduces the concepts of censoring, truncation, hazard rates, and survival functions. Participants will learn how to prepare data for survival analysis, compute descriptive statistics, create life tables, obtain Kaplan–Meier curves, and fit both semiparametric (Cox) regression and parametric regression models. Discover how to set the survival-time characteristics of your dataset just once and then use many of Stata's survival-time estimators and summary statistics commands with those data.

The course will be interactive, use real data, and offer ample opportunity for working exercises to reinforce what is learned. By the end of the course, participants should be able to describe and graph their data, fit an appropriate survival-analysis model in Stata, and interpret the results.

Gain a comprehensive understanding of four key elements in Stata: data management, statistical analysis, graphics, and reproducible reporting. Designed for both beginners and users seeking to increase their proficiency, this course equips you with the fundamentals for working efficiently in Stata. By the end of the course, you will be able to import, clean and manage your data, perform basic analyses and visualize results, create customized graphics, and write dynamic reports using Stata.