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Quantitative researchers in education rely on Stata because it is a complete statistical package that provides a broad statistical base. Whether you are developing new tests or researching topics as diverse as learning and development, teacher effectiveness, or school finance, Stata puts the best statistical methods at your fingertips. All analysis is seamlessly integrated with graphics and data management into one package that lets you pursue a broad range of education questions.

Features for educational professionals

Item response theory models
binary (1PL, 2PL, 3PL), ordinal, and categorical response models  •  item characteristic curves  •  test characteristic curves  •  item information functions  •  test information functions  •  differential item functioning (DIF)  •  more

Structural equation models
graphical path diagram builder  •  standardized and unstandardized estimates  •  modification indices  •  direct and indirect effects  •  continuous, binary, count, ordinal, and survival outcomes  •  multilevel models  •  random slopes and intercepts  •  factor scores, empirical Bayes, and other predictions  •  groups and tests of invariance  •  goodness of fit  •  handles MAR data by FIML  •  correlated data  •  survey data  •  more

Linear, binary, and count regressions
linear regression  •  logistic, probit, tobit  •  Poisson and negative binomial  •  multinomial, ordered, conditional, and nested logistic  •  quantile regression  •  zero-inflated and truncated count models  •  bootstrap, jackknife, and robust and cluster–robust variances  •  more

balanced and unbalanced designs  •  factorial, nested, and mixed designs  •  repeated measures  •  marginal means  •  contrasts  •  more

Marginal means, contrasts, and interactions
marginal means  •  least-squares means  •  adjusted means  •  profile plots  •  interaction plots  •  compare means, intercepts, or slopes  •  compare with reference category, adjacent category, grand mean, etc.  •  orthogonal polynomials  •  pairwise comparisons  •  multiple comparison adjustments  •  more

Treatment effects
inverse probability weight (IPW)  •  doubly robust methods  •  propensity score matching  •  regression adjustment  •  covariate matching  •  multilevel treatments  •  endogenous treatments  •  average treatment effects (ATEs)  •  ATEs on the treated (ATETs)  •  potential-outcome means (POMs)  •  continuous, binary, count, fractional, and survival outcomes  •  more

Multilevel mixed-effects models
continuous, binary, count, and survival outcomes  •  two-, three-, and higher-level models  •  generalized linear models  •  random intercepts  •  random slopes  •  crossed random effects  •  BLUPs of effects and fitted values  •  hierarchical models  •  residual error structures  •  DDF adjustments  •  support for survey data  •  more

Multiple imputation
nine univariate imputation methods  •  multivariate normal imputation  •  chained equations  •  explore pattern of missingness  •  manage imputed datasets  •  fit model and pool results  •  transform parameters  •  joint tests of parameter estimates  •  predictions  •  more

Power and sample size
power  •  sample size  •  effect size  •  minimum detectable effect  •  means  •  proportions  •  variances  •  correlations  •  ANOVA  •  case–control studies  •  cohort studies  •  contingency tables  •  survival analysis  •  balanced or unbalanced designs  •  results in tables or graphs  •  more

Bayesian analysis
thousands of built-in models  •  univariate and multivariate models  •  linear and nonlinear models  •  continuous, binary, ordinal, and count outcomes  •  continuous univariate, multivariate, and discrete priors  •  add your own models  •  adaptive Metropolis–Hastings sampling  •  Gibbs sampling  •  convergence diagnostics  •  posterior summaries  •  hypothesis testing  •  model comparison  •  more

interactive sessions  •  large-scale development projects  •  optimization  •  matrix inversions  •  decompositions  •  eigenvalues and eigenvectors  •  LAPACK engine  •  real and complex numbers  •  string matrices  •  interface to Stata datasets and matrices  •  numerical derivatives  •  object-oriented programming  •  more

Check out Stata's full list of features, or see what's new in Stata 14.

Why Stata?

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.

Extensive documentation.
Stata offers 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.

Learn more

We can show you how

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.

Visit our channel

NetCourses: Online training made simple

Learn how to perform rigorous panel-data analysis or univariate time series, all from the comfort of your home or office. NetCourses make it easy.

For Stata users, by Stata users

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.

Alan C. Acock

Alan C. Acock

Nicholas J. Cox

Nicholas J. Cox and H. Joseph Newton (editors)

Ulrich Kohler and Frauke Kreuter

J. Scott Long and Jeremy Freese

Michael N. Mitchell

Michael N. Mitchell

Michael N. Mitchell

Sophia Rabe-Hesketh and Anders Skrondal
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