Survey methods
Whether your data require a simple weighted adjustment because of differential
sampling rates or you have data from a complex multistage survey, Stata's
survey features can provide you with correct standard errors and confidence
intervals for your inferences. Simply specify the relevant characteristics of
your sampling design, such as sampling weights (including weights at multiple
stages), clustering (at one, two, or more stages), stratification, and
poststratification. After that, most of Stata's estimation commands can adjust
their estimates to correct for your sampling design.
And much more.
Multilevel mixed-effects models
Whether the groupings in your data arise in a nested fashion (patients nested
in clinics and clinics nested in regions) or in a nonnested fashion (regions
crossed with occupations), you can fit a multilevel model to account for the
lack of independence within these groups. Fit models for continuous, binary,
count, ordinal, and survival outcomes. Estimate variances of random intercepts
and random coefficients. Compute intraclass correlations. Predict random
effects. Estimate relationships that are population averaged over the random
effects.
And much more.
Panel data
Take full advantage of the extra information that panel data provide while
simultaneously handling the peculiarities of panel data. Study the
time-invariant features within each panel, the relationships across panels,
and how outcomes of interest change over time. Fit linear models or nonlinear
models for binary, count, ordinal, censored, or survival outcomes with
fixed-effects, random-effects, or population-averaged estimators. Fit dynamic
models or models with endogeneity.
And much more.
Structural equation modeling (SEM)
Estimate mediation effects, analyze the relationship between an unobserved
latent concept such as depression and the observed variables that measure
depression, model a system with many endogenous variables and correlated
errors, or fit a model with complex relationships among both latent and
observed variables. Fit models with continuous, binary, count, ordinal,
fractional, and survival outcomes. Even fit multilevel models with groups of
correlated observations such as children within the same schools. Evaluate
model fit. Compute indirect and total effects. Fit models by drawing a path
diagram or using the straightforward command syntax.
And much more.
Linear, binary, and count regressions
Fit classical linear models of the relationship between a continuous outcome,
such as weight, and the determinants of weight, such as height, diet, and
levels of exercise. If your response is binary (for example, diabetic or
not), ordinal (education level), or count (number of children), don't worry.
Stata has maximum likelihood estimators—logistic, ordered logistic, Poisson,
and many others—that estimate the relationship between such outcomes and
their determinants. A vast array of tools is available to analyze such
models. Predict outcomes and their confidence intervals. Test equality of
parameters or any linear or nonlinear combination of parameters.
And much more.
Multiple imputation
Account for missing data in your sample using multiple imputation. Choose from
univariate and multivariate methods to impute missing values in continuous,
censored, truncated, binary, ordinal, categorical, and count variables. Then,
in a single step, estimate parameters using the imputed datasets, and combine
results. Fit a linear model, logit model, Poisson model, hierarchical model,
survival model, or one of the many other supported models. Use the mi command,
or let the Control Panel interface guide you through your entire MI analysis.
And much more.
Adjusted predictions, contrasts, and interactions
Adjusted predictions and contrasts let you analyze the relationships between
your outcome variable and your covariates, even when that outcome is binary,
count, ordinal, or categorical. Compute adjusted predictions with covariates
set to interesting or representative values. Or compute marginal means for
each level of a categorical covariate. Make comparisons of the adjusted
predictions or marginal means using contrasts. If you have multilevel or
panel data and random effects, these effects are automatically integrated out
to provide marginal (that is, population-averaged) estimates. After fitting
almost any model in Stata, analyze the effect of covariate interactions, and
easily create plots to visualize those interactions.
And much more.
Survival analysis
Analyze duration outcomes—outcomes measuring the time to an event such as
failure or death—using Stata's specialized tools for survival analysis.
Account for the complications inherent in survival data, such as sometimes not
observing the event (censoring), individuals entering the study at differing
times (delayed entry), and individuals who are not continuously observed
throughout the study (gaps). You can estimate and plot the probability of
survival over time. Or model survival as a function of covariates using Cox,
Weibull, lognormal, and other regression models. Predict hazard ratios, mean
survival time, and survival probabilities. Do you have groups of individuals
in your study? Adjust for within-group correlation with a random-effects or
shared frailty model.
And much more.
Causal inference
Estimate experimental-style causal effects from observational data. With
Stata's treatment-effect estimators, we can use a potential-outcomes
(counterfactuals) framework to estimate, for instance, the effect of a health
education program in schools on teenage smoking. Fit
models for continuous, binary, count, fractional, and survival outcomes with
binary or multivalued treatments using inverse-probability weighting (IPW),
propensity-score matching, nearest-neighbor matching, regression adjustment,
or doubly robust estimators. If the assignment to a treatment is not
independent of the outcome, you can use an endogenous treatment-effects
estimator.
And much more.
Time series
Handle the statistical challenges inherent to time-series
data—autocorrelations, common factors, autoregressive conditional
heteroskedasticity, unit roots, cointegration, and much more. Analyze
univariate time series using ARIMA, ARFIMA, Markov-switching models, ARCH and
GARCH models, and unobserved-components models. Analyze multivariate time
series using VAR, structural VAR, VEC, multivariate GARCH, dynamic-factor
models, and state-space models. Compute and graph impulse responses. Test for
unit roots.
And much more.
IRT (item response theory)
Explore the relationship between unobserved latent characteristics such as
hospital satisfaction and the probability of responding positively to
questionnaire items related to satisfaction. Or explore the relationship
between unobserved health and self-reported responses to questions about
mobility, independence, and other health-affected activities. IRT can be used
to create measures of such unobserved traits or place individuals on a scale
measuring the trait. It can also be used to select the best items for
measuring a latent trait. IRT models are available for binary, graded, rated,
partial-credit, and nominal response items. Visualize the relationships using
item characteristic curves, and measure overall test performance using test
information functions.
And much more.
Bayesian analysis
Fit Bayesian regression models using one of the Markov chain Monte Carlo (MCMC)
methods. You can choose from a variety of supported models
or even program your own. Extensive graphical tools are available to check
convergence visually. Compute posterior mean estimates and credible intervals
for model parameters and functions of model parameters. You can perform both
interval- and model-based hypothesis testing. Compare models using Bayes
factors.
And much more.
Dynamic documents
Stata is designed for reproducible research, including the ability to
create dynamic documents incorporating your analysis results. Create
Word or PDF files, populate Excel worksheets with results and format
them to your liking, and mix Markdown, HTML, Stata results, and Stata
graphs, all from within Stata.
And much more.
As a Stata user for nearly 25 years, I've always appreciated its clean, consistent interface and the peace of mind that comes from StataCorp's rigorous testing and commitment to accuracy. Now with the past few releases, Stata can do virtually anything required by the practicing statistician — it can fit an enormous range of models used throughout the biological and social sciences and has powerful tools for examining and presenting the results of these models. And with -ml- and Mata (Stata's bytecode-compiled, object-oriented, C-like matrix programming language), it's easy to implement new models when necessary. Stata is the one piece of software I couldn't do without.
— Phil Schumm
Senior Statistician and Director of the Research Computing Group
in the Department of Public Health Sciences at the University of
Chicago
Intuitive and easy 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 community-contributed programs.
Extensive documentation.
Stata offers 27 volumes with more than 14,000 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 public health professionals. And they are a convenient teaching aid in the classroom.
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.
Stata Press offers books with clear, step-by-step examples that make teaching easier and that enable students to learn and researchers in public health to implement the latest best practices in analysis.