Introduction to Bayesian analysis using Stata
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. (detailed description)
Length: 2 full days (classroom) or 4 half days (web based)

Causal inference using Stata: Estimating average treatment effects
Learn how and when to use Stata’s treatment-effects estimators to analyze treatment effects in observational data. Use regression adjustment, inverse probability weights, doubly robust methods, propensity-score matching, and covariate matching to estimate average treatment effects (ATEs) and ATEs on the treated. We will cover the conceptual and theoretical underpinnings of treatment effects as well as many examples using Stata. (detailed description)
Length: 2 full days (classroom) or 4 half days (web based)

Customized tables using Stata
Learn how to incorporate results such as summary statistics or regression results into tables and into complete reports. Upon completion of this course, 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. (detailed description)
Length: 2 full days (classroom) or 4 half days (web based)

Handling missing data using multiple imputation
Learn the fundamentals of multiple imputation for regaining power lost by missing data. The course covers all aspects of using multiple-imputation (MI) analysis interactively. Topics include creation of MI data using the multivariate-normal and chained-equations (aka fully conditional specification) imputation methods, manipulation of MI data, and analysis of MI data. Exercises supplement the lessons and Stata examples. (detailed description)
Length: 2 full days (classroom) or 4 half days (web based)

Lasso using Stata: Methods for prediction and inference
Learn how to use lasso to predict outcomes and estimate effects when you are faced with many variables–perhaps poorly described and poorly understood variables–and classical regression techniques break down. This course provides introductions to lasso in Stata and to using lasso-based estimators in Stata for causal parameters in a high-dimensional model. (detailed description)
Length: 2 full days (classroom) or 4 half days (web based)

Multilevel/mixed models using Stata
Measure and account for clustering and grouping at multiple levels. Whether linear or nonlinear, multilevel modeling allows for random intercepts and slopes at multiple levels, reducing the problems of too-much or too-little data aggregation. The course is interactive, uses real data, offers ample opportunity for specific research questions, and provides exercises to reinforce what you learn. (detailed description)
Length: 2 full days (classroom) or 4 half days (web based)

Panel-data analysis using Stata
Learn both the theory and the 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 nonlinear models. Exercises supplement the lessons and Stata examples. (detailed description)
Length: 2 full days (classroom) or 4 half days (web based)

Regression modeling using Stata
Regression modeling is a fundamental tool for researchers who want to establish causal quantitative relationships from observational data. Learn the theoretical concepts necessary to understand regression models and how to implement them using Stata. With the assistance of the instructor, you will reinforce those concepts with exercises and examples. (detailed description)
Length: 1 full day (classroom) or 2 half days (web based)

Structural equation modeling using Stata
Learn how to illustrate, specify, and estimate structural equation models in Stata, using both Stata's SEM builder and the sem command. The course introduces several types of models, including path analysis, confirmatory factor analysis, full structural equation models, and latent growth curves. Exercises supplement the lessons and Stata examples. (detailed description)
Length: 2 full days (classroom) or 4 half days (web based)

Survey data analysis using Stata
Set up and analyze data from complex survey designs. The course covers the sampling methods used to collect survey data and how they affect the estimation of totals, ratios, and regression coefficients. The course also covers Stata's support for many survey variance estimators, including linearization, balanced and repeated replications (BRR), and jackknife. Exercises supplement the lessons and Stata examples. (detailed description)
Length: 2 full days (classroom) or 4 half days (web based)

Survival analysis using Stata
Learn how to effectively analyze survival-time data and 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. (detailed description)
Length: 2 full days (classroom) or 4 half days (web based)

Time-series analysis using Stata
Learn how to efficiently use Stata for time-series analysis. Topics include data management, estimation of univariate and multivariate stationary and nonstationary time series, model selection, hypothesis testing, and interpretation. Exercises supplement the lessons and Stata examples. (detailed description)
Length: 2 full days (classroom) or 4 half days (web based)

Using Stata effectively: Data management, analysis, and graphics fundamentals
Aimed at both new Stata users and those who wish to learn techniques for efficient day-to-day use of Stata, this course enables you to use Stata in a reproducible manner, making collaborative changes and follow-up analyses much simpler. Exercises will supplement the lectures and Stata examples. (detailed description)
Length: 2 full days (classroom) or 4 half days (web based)

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