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Public training courses

Learn Stata from StataCorp's experts. These courses are ideal for researchers and individuals who want to learn more or gain a deeper understanding of Stata.

Structural Equation Modeling Using Stata
September 18–19, 2014, in Washington, DC
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.

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Using Stata Effectively: Data Management, Analysis, and Graphics Fundamentals
September 23–24, 2014, in Washington, DC
October 7–8, 2014, in Washington, DC
December 8–9, 2014, in New York City, NY
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.

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Survey Data Analysis Using Stata
September 25–26, 2014, in Washington, DC
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.

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Multilevel/Mixed Models Using Stata
October 9–10, 2014, in Washington, DC
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.

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Estimating Average Treatment Effects Using Stata
October 16–17, 2014, in Washington, DC
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.

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Computers with Stata installed are provided at all public training sessions. Enrollment is limited.

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