Item response models.
Use item response models to reveal unobservable characteristics using questionnaires. Obtain parameter estimates and graphs from binary response models, ordinal response models, categorical response models, or a mixture. And more.
Structural equation models.
Construct models for continuous, binary, count, ordinal, multinomial, or survival outcomes and incorporate unobserved components at any level. Specify weights at each level of the model. Use with complex survey data. And more.
Linear and generalized linear models (GLMs).
Fit linear, quantile, truncated, and censored regressions and maximum likelihood models for binary, count, fractional, continuous, ordered, and multivariate outcomes. And more.
Contrasts, pairwise comparisons, and margins.
Use estimation results to obtain estimates and graphs of interactions, average effects, partial effects, contrasts, and pairwise comparisions. Draw profile and interactions plots. And more.
Estimate treatment effects for continuous, binary, count, and survival outcomes and for multilevel and multivalued treatments. Obtain estimates of effects under endogeneity. Choose from inverse probability weights (IPW), propensity-score matching, covariate matching, regression adjustment, doubly-robust augmented IPW and IPW with regression adjustment models. And more.
Multilevel mixed-effects models.
Fit fixed- and random-effects mixed-effects models to multilevel data with continuous, binary, count, and survival outcomes. Construct models for different correlation structures and nesting levels. And more.
Use descriptive statistics such as means, proportions, and ratios, and fit linear and nonlinear regressions, multilevel mixed-effects models, panel-data models, survival models, and much more using multiple imputation to account for missing data in your sample.
Power and sample size.
Determine the sample size needed for your experiment to recover meaningful effects without wasting resources. Obtain one-sample and two-sample tests of means, variances, proportions, and correlations. And more.
Fit Bayesian regression models using a Metropolis–Hastings Markov chain Monte Carlo (MCMC) method. Choose from a variety of supported models or program your own. Check convergence visually using extensive graphical tools. Compute posterior mean estimates and credible intervals for model parameters and functions of model parameters. Perform interval and model-based hypothesis testing. Compare models using Bayes factors.
Program your own estimator using Stata's built-in matrix language, MATA. Use MATA interactively with Stata. Obtain inversions, decompositions, eigenvalues and eigenvectors, and numerical derivatives. Use LAPACK routines, real and complex numbers, string matrices, and object-oriented programming. And more.
Intuitive and simple to use.
Once you learn the syntax of one estimator, graphics command, and data-management tool, you will effortlessly understand the rest.
Accuracy and reliability.
Stata is extensively and continually tested. Stata's tests produce approximately 4 million lines of output.
One package. No modules.
When you buy Stata, you obtain everything for your statistical, graphical, and data analysis needs. You do not need to buy separate modules or import your data to specialized software.
Write your own Stata programs.
You can easily write your own Stata programs and commands to share with others or to simplify your work using Stata's do-files, ado-files, and matrix-language program, MATA. Moreover, you can benefit from the thousands of Stata user-written programs.
22 volumes with more than 12,000 pages of PDF documentation containing calculation formulas, detailed examples, references to the literature, and in-depth discussions. Stata's documentation is a great place to learn about Stata and the statistics, graphics, or data-management tools you are using for your research.
Top-notch technical support.
Stata's technical support is known for their prompt, accurate, detailed, and clear responses. People answering your questions have Master's and PhD degrees in relevant areas of research.
Stata's YouTube has over 100 videos with a dedicated playlist of methodologies important to academic professionals. And, they are a convenient teaching aid in the classroom.
Stata Press offers books with clear, step-by-step examples that make teaching easier and that enable students to learn and educational professionals to implement the latest best practices in analysis.