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Behavioral sciences

Quantitative behavioral scientists rely on Stata for its accuracy, extensibility, reproducibility, and ease of use. Whether you are researching cognitive development, studying personality traits, or developing measurement instruments, Stata provides all the statistics, graphics, and data management tools needed to pursue a broad range of behavioral science questions.

Features for behavioral scientists

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, or fit a model with complex relationships among both latent and observed variables. ... Fit models with continuous, binary, count, and ordinal outcomes. Even fit hierarchical 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. Read more

Hierarchical models
Whether the groupings in your data arise in a nested fashion (students nested in classrooms and classrooms nested in schools) or in a nonnested fashion (regions crossed with occupations), you can fit a hierarchical 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. Read more

General linear models
Fit one- and two-way models. Or fit models with three, four, or even more factors. Analyze data with nested factors, with fixed and random factors, or with repeated measures. Use ANCOVA models when you have continuous covariates and MANOVA models when you have multiple outcome variables. ... Further explore the relationships between your outcome and predictors by estimating effect sizes and computing least-squares and marginal means. Perform contrasts and pairwise comparisons. Analyze and plot interactions. And much more. Read more

Power and sample size
Before you conduct your experiment, determine the sample size needed to detect meaningful effects without wasting resources. Do you intend to perform tests of means or variances? Do you want to compare proportions or correlations? Do you plan to fit a one-way, two-way, or repeated-measures ANOVA model? ...Use Stata's power commands or interactive Control Panel to compute power and sample size, create customized tables, and automatically graph the relationships between power, sample size, and effect size for your planned study. And much more. Read 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, employed or unemployed), 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. Read more

IRT (item response theory)
Explore the relationship between unobserved latent characteristics such as mathematical aptitude and the probability of correctly answering test questions (items). 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. Read more

Contrasts, marginal means, and profile plots
Quickly and easily obtain contrasts for categorical variables and their interactions. A simple R.edlevel will give you all the contrasts of education level with a reference category. A.edlevel will give you each paired contrast with the next higher education level. There are many more named contrasts, and you can specify your own. ... If you don't like typing, use a dialog box to select your contrasts. Marginal means are just a simple command or mouse click away after almost any estimation command. Evaluating interaction effects, the effects of moderating variables, is just as easy. And this is not just for linear models but for models with binary, ordinal, and count outcomes. Even for hierarchical models with correct handling of random effects. A simple command or a few mouse clicks will get you a profile plot of any of these results. And much more. Read 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. Read more

Multivariate methods
Use multivariate analyses to evaluate relationships among variables from many different perspectives. Perform multivariate tests of means, or fit multivariate regression and MANOVA models. Explore relationships between two sets of variables, such as aptitude measurements and achievement measurements, ... using canonical correlation. Examine the number and structure of latent concepts underlying a set of variables using exploratory factor analysis. Or use principal component analysis to find underlying structure or to reduce the number of variables used in a subsequent analysis. Discover groupings of observations in your data using cluster analysis. If you have known groups in your data, describe differences between them using discriminant analysis. And much more. Read 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 family structure on child development or the effect of unemployment on anxiety. ... 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. Read more

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. Read 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. Read 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. Read more

With my statistical training originating from psychology, there was a great emphasis on factorial designs, especially learning to understand interactions, both graphically and analytically. Stata allows you to graph the results of any interaction. Furthermore, Stata lets you easily dissect interactions into their components, easily obtaining simple effects, simple contrasts, partial interactions, and interaction contrasts. You can even easily graph the results of contrasts.

— Michael Mitchell
Senior statistician at the USC Children's Data Network, author of four Stata Press books, and former UCLA statistical consultant who envisioned and designed the UCLA Statistical Consulting Resources website

Check out Stata's full list of features, or see what's new in Stata 15.

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Intuitive and easy to use.
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Write your own Stata programs.
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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.

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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 behavioral scientists to implement the latest best practices in analysis.

Michael N. Mitchell

Alan C. Acock

Alan C. Acock

Nicholas J. Cox

James W. Hardin and Joseph M. Hilbe

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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