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 Education 

 

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 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.

 

Treatment effects
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.

 

Multiple imputation
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.

 

Bayesian analysis
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.

 

Mata
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.

 

 

 

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