A self-guided tour to help you find and analyze data using Stata, R, Excel and SPSS. The goal is to provide basic learning tools for classes, research and/or professional development

- DSS Online Training/Getting Started sectionA collection of tutorials to help you getting started in your data analysis.

- Using -- outreg2 -- to make nice tables (Stata)"Using outreg2 to report regression output, descriptive statistics, frequencies and basic crosstabulations."
- Using stargazer to report regression outputs, summary statistics"Using stargazer to report regression output and descriptive statistics in R (for non-LaTeX users)."

- Using Stat/TransferEasiest way to go from SPSS, SAS or other proprietary statistical software to Stata

- Getting Started in Data Analysis using StataThis Stata tutorial include topics reading data in Stata (from Excel to Stata, from SPSS to Stata, from SAS to Stata), data management (recode, generate, sort variables), frequencies, crosstabs, merge, scatter plots, histograms, descriptive statistics, regression and more!
- Getting Started in Data Analysis using RThis R tutorial provides a side-by-side comparison with Stata. It includes first steps, importing/exporting data to/from R/Stata. Frequencies, crosstablations, scatterplots, histograms, descriptive statistics.
- Exploring Data using Stata: Descriptive StatisticsThis tutorial provides instructions on exploring the basic features of data and conducting preliminary analysis using Stata.
- Panel data analysis in Stata (fixed and random effects)A basic introduction on fixed and random effects models to analyze panel data.
- Panel data analysis in R (fixed and random effects)Introduction to fixed/random effects using R
- Linear Regression using StataBasic introduction to linear regression analysis, diagnostics and presentation (using Stata)
- Linear Regression using RIntroduction to linear regression and diagnostics using R.
- Logit, ordered logit, and multinomial logit in StataIntroduction to logit, ordered logit, and multinomial logit regression
- Factor Analysis in StataBasic introduction to factor analysis
- Merge/Append using StataMerge, append datasets using Stata
- Merge/Append using RMerge, append using R
- Reshape data using StataReshape wide to long and long to wide in Stata
- Reshape data using RReshape long to wide and wide to long in R
- From NLS Investigator to StataThis document offers a quick introduction to the NLS Investigator. It follows a basic approach and focus on searching, downloading and putting the data into Stata
- Introduction to RStudioBasic intro on how to use RStudio
- TransposeRows as columns in Excel
- Z-test for proportionsExcel file on how to estimate a z-test for proportions.
- Predicted probabilities/marginal effects using margins StataEstimating predicted probabilities and marginal effects after (ordered) logit/probit models --margins--. Export output to word using ---outreg2--
- Logit/ordered logit, predicted probabilities and marginal effects uisng REstimating logit/ordered logit models using R. Predicted probabilities and marginal effects are are also included.
- Intro to Data Visualization: StataStata code to the most common graphs used in statistical analysis.
- Differences-in-differences using StataBasic differences-in-differences estimation (Stata)
- Differences-in-differences using RBasic differences-in-differences estimation (R)
- Fuzzy merge using RA step-by-step guide to conduct fuzzy matching using R
- Fuzzy Merge in StataA step-by-step guide to conduct fuzzy matching using Stata
- Loops in StataUsing loops to handle repetitive tasks in Stata
- Cubic interpolation using RHow and When to use Cubic Interpolation

- Fixed Random Effects/Panel Data in REstimation of basic fixed effects and random effects models using R
- Fixed Random Effects/Panel Data in StataEstimation of basic fixed effects and random effects models using Stata
- Linear Regression in StataBasics on how to run, define and interpret linear regression and time series output.
- Multilevel AnalysisIntroduction to multilevel analysis
- Time Series in RA Step-by-step guide of time series analysis and event study
- Event Study/Time Series in StataA Step-by-step guide to conducting Event Study Analysis in Stata
- Missing Data: Multiple Imputation in StataThis guide discusses multiple imputation techniques for missing data using Stata.

- Causal Inference for The Brave and TrueA light-hearted yet rigorous approach to learning causal inference.
- Difference-in-Difference (DiD)This repository tracks the developments in Difference-in-Difference (DiD) software packages. Brief explanations of how to use these packages is also provided. The Resources section includes information on relevant readings, books, videos, and workshops in this field.
- The Concept of Confidence Interval"Statisticians use a confidence interval to express the degree of uncertainty associated with a sample statistic. A confidence interval is an interval estimate combined with a probability statement."
- Interpreting regression outputs when some variables are log transformedThe page discusses how to interpret a regression model when some variables in the model have been log-transformed.
- Interpreting regressions outputs with standardized variablesThis page discusses how to interpret regression coefficients when the variables are standardized.
- Interpreting interaction coefficientsThis presentation presents a broad overview of methods for interpreting interactions in logistic regression.
- TED: Visualizing DataOn data visualization

- DSS lab locationThe DSS lab is located in Firestone Library A-12G (A-floor)