Getting Started in Data Analysis: Stata, R, SPSS, Excel: Home

A collection of tutorials to help you getting started in your data analysis.

"Using outreg2 to report regression output, descriptive statistics, frequencies and basic crosstabulations."

"Using stargazer to report regression output and descriptive statistics in R (for non-LaTeX users)."

Easiest way to go from SPSS, SAS or other proprietary statistical software to Stata

This 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!

This 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.

This tutorial provides instructions on exploring the basic features of data and conducting preliminary analysis using Stata.

A basic introduction on fixed and random effects models to analyze panel data.

Introduction to fixed/random effects using R

Basic introduction to linear regression analysis, diagnostics and presentation (using Stata)

Introduction to linear regression and diagnostics using R.

Introduction to logit, ordered logit, and multinomial logit regression

Basic introduction to factor analysis

Merge, append datasets using Stata

Merge, append using R

Reshape wide to long and long to wide in Stata

Reshape long to wide and wide to long in R

This 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

Basic intro on how to use RStudio

Rows as columns in Excel

Excel file on how to estimate a z-test for proportions.

Estimating predicted probabilities and marginal effects after (ordered) logit/probit models --margins--. Export output to word using ---outreg2--

Estimating logit/ordered logit models using R. Predicted probabilities and marginal effects are are also included.

Stata code to the most common graphs used in statistical analysis.

Basic differences-in-differences estimation (Stata)

Basic differences-in-differences estimation (R)

A step-by-step guide to conduct fuzzy matching using R

A step-by-step guide to conduct fuzzy matching using Stata

Using loops to handle repetitive tasks in Stata

How and When to use Cubic Interpolation

Estimation of basic fixed effects and random effects models using R

Estimation of basic fixed effects and random effects models using Stata

Basics on how to run, define and interpret linear regression and time series output.

Introduction to multilevel analysis

A Step-by-step guide of time series analysis and event study

A Step-by-step guide to conducting Event Study Analysis in Stata

This guide discusses multiple imputation techniques for missing data using Stata.

A light-hearted yet rigorous approach to learning causal inference.

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.

"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."

The page discusses how to interpret a regression model when some variables in the model have been log-transformed.

This page discusses how to interpret regression coefficients when the variables are standardized.

This presentation presents a broad overview of methods for interpreting interactions in logistic regression.

On data visualization

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