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

- Introduction to Econometrics byCall Number: HB139 .S765 2019ISBN: 9780134461991Publication Date: 2018-11-06Introduction to Econometrics, Fourth Edition, by James H. Stock and Mark W. Watson, provides an outstanding introduction to econometrics. Using an ingenious set of real-world questions and answers, they produced an excellent introduction to estimation, inference, and interpretation in econometrics.

The text also provides an excellent introduction to causal inference and explains the role of regression as a tool for it.

The fourth edition adds some key concepts and methods used in big-data analysis and machine learning. - Econometric Analysis byCall Number: HB139 .G74 2018ISBN: 9780134461366Publication Date: 2018Econometric Analysis has been the leading textbook for graduate econometrics in social science programs worldwide since 1990. It is also a major reference work for empirical research. Econometric Analysis ranked 34th with over 48,000 citations in Google Scholar's October 2014 Nature Journal list of the world's 100 all time most cited works. In 2022, with over 90,000 citations, Econometric Analysis is the most cited work ever written by an economist.
- Introductory Econometrics byCall Number: (Stokes) HB139 .W665 2018ISBN: 9781337558860Publication Date: 2019-01-04Unlike traditional texts, this book's practical yet professional approach demonstrates how econometrics has moved beyond a set of abstract tools to become genuinely useful for answering questions across a variety of disciplines. The author has organized the book's presentation around the type of data being analyzed with a systematic approach that only introduces assumptions as they are needed. This makes the material easier to understand and, ultimately, leads to better econometric practices. Packed with relevant applications, the text incorporates more than 100 data sets in different formats. Updates introduce the latest developments in the field, including the recent advances in the so-called "causal effects" or "treatment effects" to provide a complete understanding of the impact and importance of econometrics today.
- Econometric analysis of cross section and panel data byCall Number: HB139 .W663 2010ISBN: 9780262232586Publication Date: 2010
- Mostly Harmless Econometrics byCall Number: OnlineISBN: 9780691120348Publication Date: 2009-01-04From Joshua Angrist, winner of the Nobel Prize in Economics, and Jörn-Steffen Pischke, an irreverent guide to the essentials of econometrics The core methods in today's econometric toolkit are linear regression for statistical control, instrumental variables methods for the analysis of natural experiments, and differences-in-differences methods that exploit policy changes. In the modern experimentalist paradigm, these techniques address clear causal questions such as: Do smaller classes increase learning? Should wife batterers be arrested? How much does education raise wages? Mostly Harmless Econometrics shows how the basic tools of applied econometrics allow the data to speak. In addition to econometric essentials, Mostly Harmless Econometrics covers important new extensions--regression-discontinuity designs and quantile regression--as well as how to get standard errors right. Joshua Angrist and Jörn-Steffen Pischke explain why fancier econometric techniques are typically unnecessary and even dangerous. The applied econometric methods emphasized in this book are easy to use and relevant for many areas of contemporary social science. An irreverent review of econometric essentials A focus on tools that applied researchers use most Chapters on regression-discontinuity designs, quantile regression, and standard errors Many empirical examples A clear and concise resource with wide applications
- Mastering "metrics" : the path from cause to effect byCall Number: HB139 .A53984 2015ISBN: 9780691152837Publication Date: 2015
- Causal Inference : The Mixtape byCall Number: OnlineISBN: 9780300255881Publication Date: 2021An accessible, contemporary introduction to the methods for determining cause and effect in the social sciences.

Causal inference encompasses the tools that allow social scientists to determine what causes what. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied—for example, the impact (or lack thereof) of increases in the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the influence on economic growth of introducing malaria nets in developing regions. The book introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and the Stata programming languages. - Quantitative Social Science: An Introduction in Stata byCall Number: H62 .I5365 2021ISBN: 9780691191089Publication Date: 2021The Stata edition of the groundbreaking textbook on data analysis and statistics for the social sciences and allied fields
- Microeconometrics using Stata byCall Number: HB139 .C36 2022ISBN: 9781597183598Publication Date: 2022
- R for Data Science byPublication Date: 2023
- An Introduction to Statistical and Data Sciences Using R byPublication Date: 2024This book is good for people who are completely new to coding and interested in learning the basics of R programming.
- Data analysis using regression and multilevel / hierarchical models byCall Number: OnlineISBN: 052168689XPublication Date: 2006
- Unifying political methodology : the likelihood theory of statistical inference byCall Number: OnlineISBN: 9780472022519Publication Date: 1998
- Econometric analysis of panel data byCall Number: HB139 .B35 2021ISBN: 9783030539528Publication Date: 2021
- Longitudinal and panel data : analysis and applications in the social sciences byCall Number: OnlineISBN: 0521535387Publication Date: 2004
- Statistical analysis : an interdisciplinary introduction to univariate & multivariate methods byCall Number:

QA278 .K323 1986ISBN: 0942154991 - Microeconometrics : methods and applications byCall Number: HB172 .C343 2005ISBN: 0521848059Publication Date: 2005
- Bayesian Data Analysis byCall Number: QA279.5 .G45 2013ISBN: 158488388XPublication Date: 2013This book is a comprehensive guide to Bayesian analysis. The third edition includes R code for many of the examples, making it a valuable resource for learning Bayesian methods with R.

- First Steps in RBy Seth Falcon & Martin Morgan.
- Quick-R: Home Page"R is an elegant and comprehensive statistical and graphical programming language. Unfortunately, it can also have a steep learning curve. I created this website for experienced users of popular statistical packages such as SAS, SPSS, Stata, and Systat (although current R users should also find it useful). My goal is to help you quickly access this language in your work. "
- Data Analysis Examples: R"The pages below contain examples (often hypothetical) illustrating the application of different statistical analysis techniques using different statistical packages"
- Resources to help you learn and use R (UCLA)Data analysis using R
- Video tutorials for R"How to do stuff in r. two minutes or less"
- The R Project for Statistical Computing"R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible. "

- Quick-R: Data Management"Once you have access to your data, you will want to massage it into useful form. This includes creating new variables (including recoding and renaming existing variables), sorting and merging datasets, aggregating data, reshaping data, and subsetting datasets (including selecting observations that meet criteria, randomly sampling observeration, and dropping or keeping variables). "
- R Programming/Data Management"In this section, we deal with methods to read, manage and clean the data. Data can be stored in a large variety of R objects (vectors, lists, dataframes, etc). However, it is usual to store them in a dataframe and this is the reason why we focus here on dataframes."
- Merge/Append using RMerge, append using R
- Reshape data using RReshape long to wide and wide to long in R
- Missing Data/Imputation - RBrief guide to handle missing data and imputation in R
- The Split-Apply-Combine Strategy for Data Analysis"Many data analysis problems involve the application of a split-apply-combine strategy, where you break up a big problem into manageable pieces, operate on each piece independently and then put all the pieces back together. This insight gives rise to a new R package that allows you to smoothly apply this strategy, without having to worry about the type of structure in which your data is stored.

The paper includes two case studies showing how these insights make it easier to work with batting records for veteran baseball players and a large 3d array of spatio-temporal ozone measurements."

- Descriptive Statistics in RThis guide focuson on descriptive analysis in R and touch on some basic data cleaning prior to your analysis.
- Quick-R: Frequencies"This section describes the creation of frequency and contingency tables from categorical variables, along with tests of independence, measures of association, and methods for graphically displaying results."
- Fitting distributions wiht R"Fitting distributions consists in finding a mathematical function which represents in a good way a statistical variable."

- Linear Regression using RIntroduction to linear regression and diagnostics using R.
- Differences-in-differences using RBasic differences-in-differences estimation (R)
- Logit, Ordered Logit, and Multinomial Logit Models in R: A Hands-on TutorialAn introductory guide to estimate logit, ordered logit, and multinomial logit models using R
- Random Coefficient Models for Longitudinal DataExamples using Stata, SPSS, SAS and R
- Using stargazer in R to make nice output tablesUsing Stargazer in R to report regression outputs, summary statistics.
- Quick-R: Generalized Linear ModelsExamples for logistic, Poisson, survival
- Econometrics using RBrief intro to cross-sectional regression, time series, and data manipulation in R

- R Time Series TutorialBrief intro to time series analysis
- Time Series in R (Princeton)TIme series analysis in R
- Unit Roots and Cointegration"This issue focuses on time series models, with special emphasis on the tests of unit roots and cointegration. I am providing instructions for both R and STATA. I would like to remark that the theoretical background given in class is essential to proceed.
- Granger causality"A variable X Granger-causes Y if Y can be better predicted using the histories of both X and Y than it can using the history of Y alone."
- Cubic interpolation in RHow and When to use Cubic Interpolation in R

- Data Visualization in RIntroduction to data visualization in R.
- Quick-R: Basic Graphs"One of the main reasons data analysts turn to R is for its strong graphic capabilities."
- R Graph GalleryA collection of graphs using R (code provided). Previous site http://rgraphgallery.blogspot.com/

- Getting started with the `boot' package in R for bootstrap inference"The package boot has elegant and powerful support for bootstrapping. In order to use it, you have to repackage your estimation function as follows."
- Quick-R: Bootstrapping"The boot package provides extensive facilities for bootstrapping and related resampling methods. You can bootstrap a single statistic (e.g. a median), or a vector (e.g., regression weights). This section will get you started with basic nonparametric bootstrapping."

- Zelig tutorial"Zelig is a single, easy-to-use program that can estimate, help interpret, and present the results of a large range of statistical methods. It literally is "everyone's statistical software" because Zelig uses (R) code from many researchers."
- Zelig: Everyone’s Statistical SoftwareIntroduction to Zelig based on the paper by Kosuke Imai, Gary King, and Olivia Lau. "Toward A Common

Framework for Statistical Analysis and Development", Journal of Computational and Graphical Statistics, Vol. 17, No. 4 (December), pp. 892-913