These courses, led by StataCorp experts, are offered two ways—in classrooms and web based. Training courses are ideal for researchers and individuals who want to learn or have a deeper understanding of Stata.
Courses limited to 24 participants
Web based
8–11 October 2024 | 11:00 a.m. to 3:00 p.m. CDT (4:00 p.m. to 8:00 p.m. UTC)
Enrollment deadline 3 October
$995
Web based
21–24 October 2024 | 11:00 a.m. to 3:00 p.m. CDT (4:00 p.m. to 8:00 p.m. UTC)
Enrollment deadline 15 October
$1,395
Web based
18–21 November 2024 | 11:00 a.m. to 2:30 p.m. CST (5:00 p.m. to 8:30 p.m. UTC)
Enrollment deadline 12 November
$1,395
Web based
10–13 December 2024 | 11:00 a.m. to 3:00 p.m. CST (5:00 p.m. to 9:00 p.m. UTC)
Enrollment deadline 4 December
$1,395
Web based
4–7 February 2025 | 11:00 a.m. to 2:30 p.m. CST (5:00 p.m. to 8:30 p.m. UTC)
Enrollment deadline 29 January
$1,395
Web based
18–21 March 2025 | 11:00 a.m. to 3:00 p.m. CST (5:00 p.m. to 8:00 p.m. UTC)
Enrollment deadline 12 March
$1,395
Web based
14–17 April 2025 | 11:00 a.m. to 2:30 p.m. CDT (4:00 p.m. to 7:30 p.m. UTC)
Enrollment deadline 8 April
$1,395
15% discount for group enrollments of three or more participants.
All prices USD.
Classroom trainings offer hands-on learning by our team of instructors. Course topics range from Stata basics to more advanced features. You can follow along with course notes that are provided by your course instructor and work examples using Stata on computers. Courses run from 8:30 a.m. to 4:30 p.m. across two days, with meals and refreshments provided.
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Web-based trainings offer the same interactive experience as our classroom training. Attend from your home or office, watch live instruction, follow along with course notes, ask questions, and work examples using Stata. Sessions are three to four hours daily with hourly breaks. We provide a temporary Stata license, printed notes, and datasets for seamless participation.
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Expand all descriptions
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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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