Sociology 

 

Quantitative sociologists rely on Stata because of its breadth, reproducibility, and ease of use. Whether you are researching health, race/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:

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.

 

Multilevel and mixed models
Obtain descriptive statistics, parameter estimates, and predictions for hierarchical, nested, and clustered data with unobserved random effects and random coefficients.

 

Survey methods
Handle probability sampling weights, multiple stages of cluster sampling, stage-level sampling weights, stratification, and poststratification. Use variance techniques of balanced repeated replications, the bootstrap, the jackknife, successive difference replication, and linearization. Fit many different statistical models on complex survey data. And more.

 

Panel data
Obtain descriptive statistics and estimates for linear, nonlinear, and dynamic panel-data models. Get access to instrumental-variables random-effects, fixed-effects, and population-averaged estimates. Build your own dynamic model or use traditional models like Arellano–Bond. Fit models for binary, count, and continuous 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.

 

Cross-sectional models
Fit linear, quantile, truncated, and censored regressions and maximum likelihood models for binary, count, fractional, continuous, ordered, and multivariate outcomes. 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.

 

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.

 

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.