»  Home »  Disciplines »  Institutional research

Institutional research

Institutional researchers rely on Stata for its breadth, accuracy, reproducibility, and ease of use. Whether you are evaluating predictors of student retention, analyzing faculty turnover rates, or studying the effects of tuition policies, Stata provides all the data manipulation, visualization, statistics, and reporting tools you need to complete your analyses.




Features for institutional research

Importing and manipulating data
Scrape data from the web, import them from standard formats, or pull them in via ODBC and SQL. Match-merge, append, reshape, transpose, sort, filter. Stata handles Unicode, frames (multiple datasets in memory), BLOBs, regular expressions, and more, whether working with hundreds of thousands or even billions of data points.

Visualization
Create graphs and customize them programmatically or interactively with the Graph Editor. Edits can even be recorded and "replayed" on other graphs for reproducibility. Export to industry standard formats suitable for web (SVG, PNG) or print (PDF, TIFF, EPS, PS).

Dynamic document generation
Use Markdown to create Word documents and HTML files with embedded Stata code, output, and graphs. Automate Word, PDF, or Excel reports with both high-level export capabilities and low-level, fine-grained programmatic access to automate production of the documents your team needs. Read more about Markdown, about Word Documents, about PDF documents, or about Excel.

Linear, binary, and count regressions
Fit classical linear regression models of the relationship between a continuous outcome, such as college algebra grade, and the determinants of the grade, such as SAT math score and high school GPA. If your response is binary (for example, completed degree or not), ordinal (education level), count (number of students), or categorical (business, engineering, liberal arts, or education major), don't worry. Stata has maximum likelihood estimators—logistic, ordered logistic, Poisson, multinomial logit, and many others—that estimate the relationship between such outcomes and their determinants. A vast array of tools is available after fitting such models. Predict outcomes and their confidence intervals. Test equality of parameters. Compute linear and nonlinear combinations of parameters.

Multilevel mixed-effects models
Whether the groupings in your data arise in a nested fashion (students nested in colleges and colleges nested in universities) or in a nonnested fashion (colleges crossed with student's home state), 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.

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.

Structural equation modeling (SEM)
Estimate mediation effects, analyze the relationship between an unobserved latent concept such as a quantitative reasoning and the observed variables that measure quantitative reasoning, 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 students within the same college. Evaluate model fit. Compute indirect and total effects. Fit models by drawing a path diagram or using the straightforward command syntax.

Meta-analysis
Combine results of multiple studies to estimate an overall effect. Use forest plots to visualize results. Use subgroup analysis and meta-regression to explore study heterogeneity. Use funnel plots and formal tests to explore publication bias and small-study effects. Use trim-and-fill analysis to assess the impact of publication bias on results. Perform cumulative meta-analysis. Use the meta suite, or let the Control Panel interface guide you through your entire meta-analysis.

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 being a first-generation student on retention or the effect of taking developmental courses on graduation rates. Fit models for continuous, binary, count, fractional, and survival outcomes with binary or multivalued treatments using propensity-score matching, inverse-probability weighting (IPW), 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.

Choice models
Model your discrete choice data. If your outcome is, for instance, high-school graduates' choices to attend college, attend a trade school, or to work, you can fit a conditional logit, multinomial probit, or mixed logit model. Is your outcome instead a ranking of prefered alternatives? Fit a rank-ordered probit or rank-ordered logit model. Regardless of the model fit, you can use the margins to easily interpret the results. Estimate how much distance to the nearest college affects the probability of enrolling in college and even the probability of going to a trade school.

Survival analysis
Analyze duration outcome—outcomes measuring the time to an event such as graduation—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.

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.

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 tools are available to check convergence, including multiple chains. 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. Compute model fit using posterior predictive values. Generate predictions.

Check out Stata's full list of features, or see what's new in Stata 16.

Why Stata?

Intuitive and easy to use.
Once you learn the syntax of one estimator, graphics command, or 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. Each of those lines is compared against known-to-be-accurate results across flavors of Stata and every operating system Stata supports to ensure accuracy and reproducibility.

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. Share them with others or use them to simplify your work. Utilize Stata's do-files, ado-files, and Mata: Stata's own advanced programming language that adds direct support for matrix programming. You can also access and benefit from the thousands of existing Stata community-contributed programs.

Extensive documentation.
Stata offers 31 manuals with more than 15,000 pages of PDF documentation containing detailed examples, in-depth discussions, references to relevant literature, and methods and formulas. Stata's documentation is a great place to learn about Stata and the statistics, graphics, data management, and data science 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

Would you like to see Stata in action?

Join us for one of our free live webinars. Ready. Set. Go Stata shows you how to quickly get started manipulating, graphing, and analyzing your data. Or, go deeper in one of our special-topics webinars.

Would you like to see more?

Stata's YouTube has over 250 videos with playlists for a variety of methodologies important to institutional researchers. And they are a convenient teaching aid in the classroom.


Visit our channel

NetCourses: Online training made simple

Get started quickly at using Stata effectively, or even learn how to perform rigorous time-series, panel-data, or survival analysis, all from the comfort of you 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 institutional researchers to implement the latest best practices in analysis.


Alan C. Acock

Alan C. Acock

Nicholas J. Cox

Richard Valliant and Jill A. Dever

Scott Baldwin

J. Scott Long and Jeremy Freese

Michael N. Mitchell

Michael N. Mitchell

Michael N. Mitchell

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 YouTube Instagram
© Copyright 1996–2019 StataCorp LLC   •   Terms of use   •   Privacy   •   Contact us