New in the Stata Bookstore
The Stata Bookstore carries publications about Stata and statistics. Every title we add is reviewed and includes a comment from our Stata technical group. You can find the latest titles by scrolling through the new arrivals section on the Bookstore home page. Here are some of the latest additions:
Causal Inference: The Mixtape
By Scott Cunningham
Published by Yale University Press
Scott Cunningham's Causal Inference: The Mixtape is a useful reference for any researcher delving into causal inference. In each chapter, theoretical details are clearly presented, followed by how to apply the theory to answer causal inference problems using statistical software. The examples are accompanied by readily available data and replication code.
Econometric Analysis of Panel Data, Sixth Edition
By Badi H. Baltagi
Published by Springer
Badi H. Baltagi's Econometric Analysis of Panel Data, Sixth Edition is a standard reference for performing estimation and inference on panel datasets from an econometric standpoint. This book provides both a rigorous introduction to standard panel estimators and concise explanations of many newer, more advanced techniques. The sixth edition has been substantially updated to reflect modern developments in panel-data analysis. This edition also includes new material on dynamic panels, limited dependent variables, nonstationary panels, and spatial panel data.
Bootstrapping: An Integrated Approach with Python and Stata
By Felix Bittmann
Published by De Gruyter
Felix Bittmann's Bootstrapping: An Integrated Approach with Python and Stata is a great resource for students and researchers who want to learn and apply bootstrap methods. Examples range from straightforward use of Stata's bootstrap prefix and vce(bootstrap) option to more advanced techniques such as writing a program for resampling residuals. With this knowledge, readers will be ready to apply bootstrapping in their own analyses using Stata.
Modern Epidemiology, Fourth Edition
By Timothy L. Lash, Tyler J. VanderWeele, Sebastien Haneuse, and Kenneth J. Rothman
Published by Wolters Kluwer
Timothy L. Lash, Tyler J. VanderWeele, Sebastien Haneuse, and Kenneth J. Rothman's Modern Epidemiology, Fourth Edition text has broad coverage of introductory and advanced epidemiological methods, with discussions that are clear and concise. The fourth edition includes discussion of methods such as agent-based modeling, instrumental variables, mediation analysis, and causal modeling.
Generalized Linear Models for Bounded and Limited Quantitative Variables
By Michael Smithson and Yiyun Shou
Published by Sage Publications
Michael Smithson and Yiyun Shou's Generalized Linear Models for Bounded and Limited Quantitative Variables provides a focused discussion on the theoretical and applied aspects of modeling outcomes with natural boundaries, such as proportions, and outcomes subjected to censoring or truncation. Researchers and students who have some familiarity with generalized linear models will find this book to be a great resource when they are ready to model bounded and limited dependent variables.
Statistics in Medicine, Fourth Edition
By Robert H. Riffenburgh and Daniel L. Gillen
Published by Academic Press
Robert H. Riffenburgh and Daniel L. Gillen's Statistics in Medicine, Fourth Edition is an excellent book, useful as a reference for researchers in the medical sciences and as a textbook. It focuses largely on understanding statistical concepts rather than on mathematical and theoretical underpinnings. The authors cover both introductory statistical techniques and advanced methods commonly appearing in medical journals.
A Step-by-Step Guide to Exploratory Factor Analysis with Stata
By Marley W. Watkins
Published by Routledge
Marley W. Watkins' A Step-by-Step Guide to Exploratory Factor Analysis with Stata is a concise, approachable guide for applied researchers in the behavioral, medical, and social sciences. This book begins with an introduction to the Stata interface, commands, Do-file Editor, and resources available for help, followed by an easy-to-follow 10-step approach for conducting exploratory factor analysis in Stata.
