>> Home >> Disciplines >> Sociology

Sociology

Quantitative sociologists rely on Stata because of its breadth, reproducibility, and ease of use. Whether you are researching health, race and ethnicity, family, gender, inequality, or demography, Stata provides all the statistics, graphics, and data management tools needed to address a broad range of sociological questions.




Features for sociologists

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.

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, multilevel 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.

Multilevel mixed-effects models
Whether the groupings in your data arise in a nested fashion (students nested in schools and schools nested in districts) 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 peculiar difficulties that panel data present. Study the time-invariant idiosyncratic 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.

Linear, binary, and count regressions
Fit classical linear models of the relationship between a continuous outcome, such as wage, and the determinants of wage, such as education level, age, experience, and economic sector. If your response is binary (for example, employed or unemployed), ordinal (education level), or count (number of children), don't worry. Stata has maximum likelihood estimators–probit, ordered probit, 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.

Structural equation modeling (SEM)
Estimate mediation effects, analyze the relationship between an unobserved latent concept such as a person's level of conservatism and the observed variables that measure conservatism, 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.

Adjusted predictions, interactions, and moderation
Adjusted predictions and marginal means 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 moderating variables, and easily create interaction plots. And much more.

Bayesian analysis
Fit Bayesian regression models using a Metropolis–Hastings Markov chain Monte Carlo (MCMC) method. 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.

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 job training program on employment or the effect of college completion on civic participation. 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.

Programming and matrix programming
Want to program your own commands to perform estimation, perform data management, or implement other new features? Stata is so programmable that thousands of Stata users have implemented and published thousands of user-written commands. These commands look and act just like official Stata commands. A unique feature of Stata's programming environment is Mata, a fast and compiled matrix programming language. Of course, it has all the advanced matrix operations you need. It also has access to the power of LAPACK. What's more, it has built-in solvers and optimizers to make implementing your own maximum likelihood, GMM, or other estimators easier. And you can leverage all of Stata's estimation and other features from within Mata. Many of Stata's official commands are themselves implemented in Mata. And much more.

Most impressive is the fact that you guys find fixes for every problem or question that we have, quickly and efficiently. And little by little, as Stata develops, we are dropping the other programs we use, too. I don't know of any other software company that responds as well to its users' questions, suggestions, and comments. Great work!

— Julien Teitler
Department of Sociology, University of Pennsylvania

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

Why Stata?

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 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 your work in sociology. 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 sociologists to implement the latest best practices in analysis.


Alan C. Acock

Nicholas J. Cox

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

J. Scott Long

J. Scott Long and Jeremy Freese

Michael N. Mitchell

Sophia Rabe-Hesketh and Anders Skrondal

Stata

Shop

Support

Company


The Stata Blog: Not Elsewhere Classified Find us on Facebook Follow us on Twitter LinkedIn Google+ YouTube
© Copyright 1996–2016 StataCorp LP   •   Terms of use   •   Privacy   •   Contact us