# Structural Equation Modelling with Partial Least Squares Using Stata and R

Structural equation modeling (SEM) is a statistical framework that can model both observed and unobserved (latent) variables through complex relationships. While the traditional covariance-based SEM aims to find parameter estimates that minimize the distance between the observed and model-implied covariances of the observed variables, partial least-squares SEM (PLS-SEM) aims to find parameter estimates that maximize explained variance.

# Introduction to Partial Least Squares Structural Equation Modelling (Pls-Sem) Using Stata

The course is of particular interest to researchers and professional working in social sciences, psychology, business administration, marketing and management.

# Fundamentals of Supervised Machine Learning

This book presents the fundamental theoretical notions of supervised machine learning along with a wide range of applications using Python, R, and Stata.

# Estimating Linear Regression Models with Exogenous and Endogenous Variables in Stata

This applied course offer a rigorous overview of the more advanced technical capabilities currently available in Stata for linear regression analysis. Thus providing participants with a unique hands-on opportunity to acquire the necessary theoretical and applied skills to independently apply advanced linear regression techniques in Stata.

# CATS 2.O

CATS (Cointegration Analysis of Time Series) is a set of cointegration analysis procedures written by Jonathan G. Dennis, Katarina Juselius, Sören Johansen and Henrik Hansen of the University of Copenhagen for use with Estima's RATS software.

# MVSP

A powerful multivariate analysis program.

# Summer School | Text Analysis: A Qualitative and Quantitative Approach

Today researchers across a wide variety of fields find themselves having to analyse an increasing amount of qualitative information. The objective of this summer school therefore, is to provide participants the requisite toolkit necessary for the successful planning, conducting and subsequent statistical analysis of qualitative text. To this end, an overview of the following methodologies: qualitative analysis, quantitative content analysis and text mining, to text analysis is provided. The opening sessions focus on the fundamental role of data preparation to the analysis, before moving on to identifying themes and correlations using both the text mining and content analysis approach. The final sessions address the more advanced topics of importing and exporting data, together with document classification.

# Social Network Analysis Using Stata

The field of Social Network Analysis is one of the most rapidly growing fields of the social sciences. Social network analysis focuses on the relationships that exist between individuals (or other units of analysis) such as friendship, advice, trust, or trade relationships. As such, network analysis is concerned with the visualization and analysis of network structures, as well as with the importance of networks for individuals’ propensities to adopt different kinds of behaviors. Up until now, researchers wishing to implement this type of analysis have been forced to use specialized software for network analysis. A new set of user written commands (developed by Thomas Grund, coauthor of the forthcoming Stata Press title “An Introduction to Social Network Analysis and Agent-Based Modeling Using Stata”) are however, now available for Stata. This workshop introduces the so-called * nwcommands suite* of over 90 Stata commands for social network analysis. The suite includes commands for importing, exporting, loading, saving, handling, manipulating, replacing, generating, visualizing, and animating networks. It also includes commands for measuring various properties of the networks and the individual nodes, for detecting network patterns and measuring the similarity of different networks, as well as advanced statistical techniques for network analysis including MR-QAP and ERGM.

# GAUSS 24

GAUSS 24 is a software specialised in statistical and econometric analysis. It is ideal for advanced users who do not find other statistics programmes flexible enough.

# Rats 10.0

Rats 10.0 is comprehensive econometrics and time series analysis software package, featuring excellent graphics and the availability of many ad-hoc routines, including those for ARCH, GARCH and Cointegration.

# WORDSTAT for STATA

WordStat for Stata was created to allow Stata users (from version 13 to 18) running under Windows, to apply text analytics techniques on any string variables stored in a Stata data file. WordStat combines natural language processing, content analysis and statistical techniques to quickly extract topics, patterns and relationships in large amount of text. It can process millions of words in seconds and compare extracted themes across any other numerical, categorical, or date variables in the Stata file.

# Wordstat 2024

WordStat is a flexible and easy-to-use text analysis software – whether you need text mining tools for fast extraction of themes and trends, or careful and precise measurement with state-of-the-art quantitative content analysis tools.

# What’sBest! 19.0

What'sBest! is an application (add-in) for Excel that allows large optimisation models to be built in a free form layout** **within a spreadsheet.

# Parametric and Nonparametric Production Frontier Models in Stata

Production frontier models have over the years become an indispensable tool of analysis for both scholars and practitioners interested in the measurement of performances through efficiency scores, in academia, business and government. This course provides participants with both the knowledge and requisite applied toolset for applying frontier methods to cross-section and panel data in Stata.

# Microeconometrics Using Stata, Volume II: Nonlinear Models and Causal Inference Methods

Any applied economic researcher using Stata and anyone teaching or studying microeconometrics will benefit from Cameron and Trivedi's two volumes. They are an invaluable reference of the theory and intuition behind microeconometric methods using Stata. Those familiar with Cameron and Trivedi's *Microeconometrics: Methods and Applications* will find the same rigor. Those familiar with the previous edition of *Microeconometrics Using Stata* will find the same explanation of Stata commands, their interpretation, and their connection with microeconometric theory as well as an introduction to computational concepts that should be part of any researcher's toolbox.

# Stata 18

Stata is a comprehensive statistical software package offering academic and professional users working in biostatistics, economics, epidemiology, psychology, public health and the political and social sciences, an array of statistical functions and full data management capabilities. Stata is easy to use for beginners via its system of drop down menus, but at the same time offers extremely sophisticated programming capabilities for the more advanced user.

# A Step-by-Step Guide to Exploratory Factor Analysis with Stata

A Step-By-Step Guide to Exploratory Factor Analysis With Stata, by Marley W. Watkins, 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.

# Causal Inference: The Mixtape

*Causal Inference: The Mixtape* is a book for practitioners. The purpose of the book is to allow researchers to understand causal inference and work with their data to answer relevant questions in the area. It is the emphasis on the use of statistical software that sets Cunningham's book apart. 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.

# Multivariate Garch (Volatility) Models for Risk Management

The objective of our Multivariate Garch Models for Risk Management course is to provide participants with a comprehensive overview of the principal methodologies, both theoretical and applied, adopted for the analysis of risk in financial markets.

# Econometric Analysis of Panel Data

*Econometric Analysis of Panel Data, Sixth Edition*, by Badi H. Baltagi, 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.

# A Visual Analysis of Spatial Data – Mapping in Stata

This course offers an introduction to the visual analysis of spatial data using the statistical software Stata. The course begins with an overview of the peculiar characteristics of spatial data and the implications of such for the analysis of spatial data, before moving on to discuss the concept of spatial proximity and the centrality of this particular concept to spatial data analysis.

# Maximising the Potential of Stata’s new Python Capabilities

TStat’s “Maximising the Potential of Stata’s New Python Capabilities” course offers participants an excellent opportunity to acquire the introductory programming skills required to integrate Python’s capability into Stata 18. The course opens with an introductory session focusing on the Python programming basics required by users wishing to exploit the Stata – Python connectivity, before moving on to illustrate how to use Python in a Stata environment and the vice versa. In the closing session a series of practical applications will be discussed in order to highlight WHEN and HOW one should exploit the connectivity between Python and Stata for one’s research.

# Modern Epidemiology

*Modern Epidemiology, Fourth Edition* provides a complete desk reference of the methods of modern epidemiology, with contributions from the 4 authors plus 35 leading experts from various subfields.

# Statistics in Medicine

*Statistics in Medicine, Fourth Edition*, by Robert H. Riffenburgh and Daniel L. Gillen, 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.

# Bootstrapping: An Integrated Approach with Python and Stata

*Bootstrapping: An Integrated Approach with Python and Stata*, by Felix Bittmann, is a great resource for students and researchers who want to learn and apply bootstrap methods.

# Generalized Linear Models for Bounded and Limited Quantitative Variables

*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.

# Forecasting Energy Prices and Volatility with Stata

The modelling and forecasting of energy prices and volatility has become of utmost importance in the current turbulent times. The statistical features of energy data, which tends to follow periodic patterns and exhibit spikes, non-constant means and non-constant variances, renders the task of forecasting energy prices somewhat challenging.

The objective of TStat’s “Forecasting Energy Prices and Volatility with Stata” course is to provide participants with the specific analytical tools to undertake a rigorous and in-depth analysis of prices in international energy markets. The programme covers a wide range of econometric methods currently available to researchers and practitioners, such as: i) univariate and multivariate time series models to estimate and forecast prices and ii) univariate and multivariate GARCH models for the estimation and forecast of price volatility.

# Dynamic Panel Data Analysis

This course provides a rigorous overview of existing DPD techniques, thus offering students the opportunity to acquire the more advanced technical capabilities currently available for panel data analysis.

# Environmental Econometrics Using Stata

*Environmental Econometrics Using Stata*is written for applied researchers that want to understand the basic theory of modern statistical methods and how to use them. It is also perfectly suited for teaching. Each chapter is motivated with real data and ends with a set of exercises. The book is also inherently interdisciplinary. The questions posed by environmental issues are relevant to researchers in the physical sciences, economics, sociology, political science, and public health, among other fields.

# Linear Panel Data Models in Stata

This introductory course offers participants the opportunity to acquire the necessary theoretical background and the applied skills to enable them to: i) independently employ micro panel data techniques to their own research topics, and ii) to understand and evaluate micro panel data analysis published in the academic literature.

# Non-Linear Panel Data Models in Stata

This course follows on from our Linear Panel Data Models in Stata course to offer the necessary theoretical background and the applied skills to enable participants to: i) independently employ non-linear micro panel data techniques to their own research topics, and ii) to understand and evaluate micro panel data analyses published in the academic literature.

# Introduction to Spatial Panel Data Models Using Stata

Our “Introduction to Spatial Panel Data analysis using Stata” course offers participants the opportunity to acquire the necessary theoretical and empirical toolset for modelling data which are correlated in time and space using both official and community written Stata spatial estimation commands. The opening session reviews Stata’s inbuilt * sp* command suite and illustrates how one prepares data for a spatial longitudinal analysis, before moving on to discuss different estimation techniques for both spatial fixed- and random-effects “static” models and for dynamic models with additive and/or interactive fixed-effects.

# Taking Your Stata Programming Skills To The Next Level: Developing And Modifying Stata Ado Files

The objective of TStat Training’s more advanced course is to provide participants with the programming commands and options required to autonomously develop and modify Stata ADO files. The opening session offers a quick overview of the fundamental concepts and commands (* macros, vectors, scalers, looping, branching, temporary objects, foreach, forvalues*) intrinsic to successful programming development. Session two moves on to illustrate the most effective way to develop a Stata ADO file, introducing participants to more specific programming concepts (such as arguments, local subroutines and the temporary storing of results) and Stata’s programming commands

*and*

**tokenize**,**macro shift, marksample****and**

*markout*“*byable*”*. In section three participants are introduced to Stata’s inbuilt matrix capabilities, before moving on in the final session to developing their own programs for linear and maximum likelihood estimators.*

**sortpreserve**

In common with TStat’s course philosophy, each session is composed of both a theoretical component (in which the programming techniques are fully explained via a series of course specific developed examples), and an applied (hands-on) segment, during which participants have the opportunity to implement the techniques under the watchful eye of the course tutor.

At the end of the course, it is expected that participants will be able to independently implement both the techniques learnt and personalize the ADO program templates specifically developed during the course in order to enhance the effectiveness of their research.

# QDA MINER 2024

QDA Miner is an easy-to-use qualitative data analysis software package for coding, annotating, retrieving and analyzing small and large collections of documents and images. QDA Miner qualitative data analysis tool may be used to analyze interview or focus group transcripts, legal documents, journal articles, speeches, even entire books, as well as drawings, photographs, paintings, and other types of visual documents.

# EViews 14

EViews 14 is econometric software that offers academic researchers, companies, government agencies and students access to powerful statistical, forecasting and modelling tools through an innovative and easy-to-use interface.

# LINGO 21.0

LINGO 21.0 is a powerful tool developed for solving linear, non-linear and integer optimisation models more efficient and faster.

# Lindo API 15.0

Lindo API 15.0 is a dynamic library, that allows its potential to be used within other applications.

# Analysing Micro Data in Stata

TStat’s introduction to micro data analysis course focuses, from both a theoretical and applied point of view, on the following methodologies: count models, binary dependent variable models, multinomial models, Tobit and Interval Regression models, models with treatment variables and Sample Selection and the Control function approach.

# Automating Your Research in Stata: “A Little Bit of Programming Goes An Awfully Long Way!”

This course aims to provide participants with the fundamental Stata programming toolkit in order to facilitate, automate, replicate and personalize both data analysis, management and presentation.

# Introduction to Spatial Analysis Using Stata

Many phenomena in the fields of economics, medical and social science, such as unemployment, crime rates or infectious diseases tend to be spatially correlated. Spatial econometrics has developed to include techniques and methods to model the spatial characteristics of such data, by taking into account both spillover effects and spatial heterogeneity.

# Migrating To Stata Painlessly!

“Migrating to Stata Painlessly!” is a reduced version of our "*Up and Running in Stata*" course. The course covers everything from the very basics, in order to get one up and running in Stata, through to an overview of the Stata commands available for preliminary data analysis, data management, importing and exporting data formats and the creation of graphs in Stata.

# A Gentle Introduction to Stata’s Programming Language Mata

Mata is Stata’s powerful extremely FAST built-in matrix compiled programming language, similar to R, Matlab and GAUSS. One of the main drawbacks in learning Mata however, is that the Reference Manual, whilst extremely detailed, offers little advice on how Mata can be actively implemented.

# Factor Models and Risk Management Tools

The growth in financial instruments during the last decade has resulted in a significant development of econometric methods (financial econometrics) applied to financial data. The objective of our Factor Models & Risk Management Tools course is to provide participants with a comprehensive overview of the principal methodologies, both theoretical and applied, adopted for risk analysis and risk management. To this end, the course focuses on the implementation of both factor models and principal components analysis for the identification of specific asset, country and global risk factors and on risk management tools/measures.

# Modelling Volatility and Contagion in Finance

The growth in financial instruments during the last decade has resulted in a significant development of econometric methods (financial econometrics) applied to financial data. The objective of our Modelling Volatility and Contagion in Finance course is to provide participants with a comprehensive overview of the principal methodologies, both theoretical and applied, adopted for the analysis of risk in financial markets. To this end, the course focuses on the modelling and forecasting of financial time series of asset returns; the modelling of cross market correlations, volatility spillovers and contagion in financial asset markets. During the course, a number of alternative GARCH models and models of conditional correlations will be reviewed.

# Time Series Modelling and Forecasting using Stata

Time Series data is today available for a wide range of several phenomena in Business, Finance, Economics, Public Health, the Political and Social Sciences. The aim of TStat Training’s Times Series Modelling and Forecasting Course is therefore, to provide researchers and professionals with the standard tool kit required for the analysis of time series data in Stata. As such the program has been developed to offer an overview of the most commonly used methods for analysing, modelling and forecasting the dynamic behaviour of time series data, offering practical examples of empirical modelling using real-world data. The course begins with an introduction to Stata’s basic time series commands, before moving onto the analysis of time series features and to univariate time series models. Sessions 3 and 4 instead focus on the estimation of both multivariate time series models with stationary and nonstationary data and univariate models of volatility.

In common with TStat’s training philosophy, throughout the course theory and methods are illustrated in an intuitive way and are complemented by practical exercises undertaken in Stata, during which the course tutor discusses and highlights potential pitfalls and the advantages of individual techniques. Particular attention is also given to both the interpretation and presentation of empirical results. In this manner, the course leader is able to bridge the “often difficult” gap between theory and practice of time series modelling and forecasting.

Upon completion, it is expected that participants are able to autonomously implement the statistical methods discussed during the course to their own data, customizing when necessary, the Stata *do-file* routines specifically developed for the course.

# VIII Summer School | Modelling and Forecasting Energy Markets

The objective of TStat’s “Modelling and Forecasting Energy Markets” Summer School is therefore to provide participants with the specific analytical tools to undertake a rigorous and in-depth analysis of both demand and prices in international energy markets.

# Stat/Transfer 17

Stat/Transfer has provided fast, reliable, and convenient data transfer between popular software packages for thousands of users, worldwide.

Stat/Transfer knows about statistical data - it handles missing data, value and variable labels and all of the other details that are necessary to move as much information as is possible from one file format to another.

# Illustrating Stata

Illustrating Stata provides participants with the necessary introductory toolset to enable them to carry out efficient data analysis and data management in Stata in a reproducible manner.

# Introduction to Machine Learning in Stata

This intensive introductory course offers therefore an introduction to the standard machine learning algorithms currently applied to social, economic and public health data in order to illustrate (using a series of both official and user written Stata commands), how Machine Learning techniques can be applied to search for patterns in large (often extremely “noisy”) databases, which can subsequently be used to make both decisions and predictions.

# Psychological Statistics and Psychometrics Using Stata

*Psychological Statistics and Psychometrics Using Stata* by Scott Baldwin is a complete and concise resource for students and researchers in the behavioral sciences.

Baldwin's primary goal in this book is to help readers become competent users of statistics. To that end, he first introduces basic statistical methods such as regression, *t* tests, and ANOVA.

# Survey Weights: A Step-by-Step Guide to Calculation

*Survey Weights: A Step-by-Step Guide to Calculation*, by Richard Valliant and Jill Dever, is an excellent reference for survey data analysts and researchers. This book details the reasons for weighting and shows how to perform different weight-adjustment methods in Stata.

# A Course in Item Response Theory and Modeling with Stata

*A Course in Item Response Theory and Modeling with Stata,* by Tenko Raykov and George A. Marcoulides, is a comprehensive introduction to the concepts of item response theory (IRT) that includes numerous examples using Stata's powerful suite of IRT commands. The authors' unique development of IRT builds on the foundations of classical test theory, nonlinear factor analysis, and generalized linear models. The examples demonstrate how to fit many kinds of IRT models, including one-, two-, and three-parameter logistic models for binary items as well as nominal, ordinal, and hybrid models for polytomous items.

# The Mata Book: A Book for Serious Programmers and Those Who Want to Be

*The Mata Book: A Book for Serious Programmers and Those Who Want to Be* is the book that Stata programmers have been waiting for. Mata is a serious programming language for developing small- and large-scale projects and for adding features to Stata. What makes Mata serious is that it provides structures, classes, and pointers along with matrix capabilities.

# Health Econometrics Using Stata

Health Econometrics Using Stata by Partha Deb, Edward C. Norton, and Willard G. Manning provides an excellent overview of the methods used to analyze data on healthcare expenditure and use. Aimed at researchers, graduate students, and practitioners, this book introduces readers to widely used methods, shows them how to perform these methods in Stata, and illustrates how to interpret the results. Each method is discussed in the context of an example using an extract from the Medical Expenditure Panel Survey.

# Essentials of a Successful Biostatistical Collaboration

Essentials of a Successful Biostatistical Collaboration by Arul Earnest is a unique approach to a biostatistics text in that the focus is not purely on study design and statistical analyses. While these topics are certainly discussed, equal attention is given to topics such as planning, project management, and effective communication that are important for any biostatistician who is collaborating with a research team.

# Financial Econometrics Using Stata

Financial Econometrics Using Stata by Simona Boffelli and Giovanni Urga provides an excellent introduction to time-series analysis and how to do it in Stata for financial economists. Aimed at researchers, graduate students, and industry practitioners, this book introduces readers to widely used methods, shows them how to perform these methods in Stata, and illustrates how to interpret the results.

# Econometric Evaluation of Socio-Economic Programs

*Econometric Evaluation of Socio-Economic Programs* by Giovanni Cerulli provides an excellent introduction to estimating average treatment effects from observational data. This book provides thorough introductions to the models and estimators implemented in **teffects**, **etregress**, and **etpoisson** and provides many examples using these commands and some similar commands written by the author.

# Multilevel and Longitudinal Modeling Using Stata Volume II: Categorical Responses, Counts, and Survival

*Multilevel and Longitudinal Modeling Using Stata, Fourth Edition*, by Sophia Rabe-Hesketh and Anders Skrondal, is a complete resource for learning to model data in which observations are grouped—whether those groups are formed by a nesting structure, such as children nested in classrooms, or formed by repeated observations on the same individuals. This text introduces random-effects models, fixed-effects models, mixed-effects models, marginal models, dynamic models, and growth-curve models, all of which account for the grouped nature of these types of data. As Rabe-Hesketh and Skrondal introduce each model, they explain when the model is useful, its assumptions, how to fit and evaluate the model using Stata, and how to interpret the results. With this comprehensive coverage, researchers who need to apply multilevel models will find this book to be the perfect companion. It is also the ideal text for courses in multilevel modeling because it provides examples from a variety of disciplines as well as end-of-chapter exercises that allow students to practice newly learned material.

# Multilevel and Longitudinal Modeling Using Stata Volume I: Continuous Responses

Volume I begins with a review of linear regression and then builds on this review to introduce two-level models, the simplest extensions of linear regression to models for multilevel and longitudinal/panel data. Rabe-Hesketh and Skrondal introduce the random-intercept model without covariates, developing the model from principles and thereby familiarizing the reader with terminology, summarizing and relating the widely used estimating strategies, and providing historical perspective. Once the authors have established the foundation, they smoothly generalize to random-intercept models with covariates and then to a discussion of the various estimators (between, within, and random effects). The authors also discuss models with random coefficients. The text then turns to models specifically designed for longitudinal and panel data—dynamic models, marginal models, and growth-curve models. The last portion of volume I covers models with more than two levels and models with crossed random effects.

# Stata for the Behavioral Sciences

*Stata for the Behavioral Sciences*, by Michael Mitchell, is the ideal reference for researchers using Stata to fit ANOVA models and other models commonly applied to behavioral science data. Drawing on his education in psychology and his experience in consulting, Mitchell uses terminology and examples familiar to the reader as he demonstrates how to fit a variety of models, how to interpret results, how to understand simple and interaction effects, and how to explore results graphically.

# Qualitative Data Analysis using QDA Miner and WordStat for Stata

Today researchers across a wide variety of fields find themselves having to analyze an increasing amount of qualitative information. The objective of this workshop therefore, is to offer participants an introduction to the requisite tools required for the successful planning, conducting and reporting of qualitative data analysis. To this end, an overview of the following approaches: quantitative analysis, quantitative content analysis and text mining, to text analysis is provided. The opening sessions focus on the fundamental role of data preparation to the analysis, before moving on to identifying themes and correlations using both text mining and content analysis. The final session addresses the more advanced topics of importing and exporting data, together with document classification. In common with TStat’s training philosophy, the workshop takes a hands-on approach to quantitative data analysis. Implementation of the methodologies discussed being illustrated using the scientific software QDA Miner, WordStat and WordStat for Stata.

# Speaking Stata Graphics

Speaking Stata Graphics is ideal for researchers who want to produce effective, publication-quality graphs. A compilation of articles from the popular “Speaking Stata” column by Nicholas J. Cox, this book provides valuable insights about Stata's built-in and user-written statistical-graphics commands.

# An Introduction to Survival Analysis Using Stata

*An Introduction to Survival Analysis Using Stata, Revised Third Edition* is the ideal tutorial for professional data analysts who want to learn survival analysis for the first time or who are well versed in survival analysis but are not as dexterous in using Stata to analyze survival data. This text also serves as a valuable reference to those readers who already have experience using Stata’s survival analysis routines.

# Introduction to Time Series Using Stata

*Introduction to Time Series Using Stata, Revised Edition*, by Sean Becketti, is a practical guide to working with time-series data using Stata. In this book, Becketti introduces time-series techniques—from simple to complex—and explains how to implement them using Stata. The many worked examples, concise explanations that focus on intuition, and useful tips based on the author’s experience make the book insightful for students, academic researchers, and practitioners in industry and government.

# Applied Econometrics for Health Economists

*Applied Econometrics for Health Economists: A Practical Guide*, Second Edition, by Andrew Jones intuitively discusses the major methods used in applied health economics. In each discussion, Jones uses Stata to analyze real data and interprets the results.

# Negative Binomial Regression

*Negative Binomial Regression,* Second Edition, by Joseph M. Hilbe, reviews the negative binomial model and its variations. Negative binomial regression—a recently popular alternative to Poisson regression—is used to account for overdispersion, which is often encountered in many real-world applications with count responses.

# Logistic Regression Models

*Logistic Regression Models, *by Joseph Hilbe, arose from Hilbe’s course in logistic regression at statistics.com. The book includes many Stata examples using both official and user-written commands and includes Stata output and graphs.

# A Stata Companion to Political Analysis

The book surveys the statistical methods that professional political scientists use; its treatment of research methods deftly incorporates data management, graphical analysis, and statistics in the political science domain. In this edition, the authors use Stata's factor variable notation, which simplifies working with categorical variables and interactions.

# Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data

William Dupont’s *Statistical Modeling for Biomedical Researchers*, Second Edition is ideal for a one-semester graduate course in biostatistics and epidemiology. Dupont assumes only a basic knowledge of statistics, such as that obtained from a standard introductory statistics course. Stata is used extensively throughout the text, making it possible to introduce computationally complex methods with little or no higher-level mathematics.

# Event History Analysis with Stata

*Event History Analysis with Stata*, by Hans-Peter Blossfeld, Katrin Golsch, and Götz Rohwer, presents survival analysis from a social science perspective. Introducing the mathematics and statistics of survival analysis, along with substantive discussions of social science data issues, the authors give examples throughout using Stata (version 15) and data from the German Life History Study.

# Using Stata for Principles of Econometrics

This book is a supplement to * Principles of Econometrics, 5th Edition* by R. Carter Hill, William E. Griffiths and Guay C. Lim (Wiley, 2018), hereinafter POE5. This book is not a substitute for the textbook, nor is it a standalone computer manual. It is a companion to the textbook, showing how to perform the examples in the textbook using Stata Release 15. This book will be useful to students taking econometrics, as well as their instructors, and others who wish to use Stata for econometric analysis.

# A Gentle Introduction to Stata

Alan C. Acock's *A Gentle Introduction to Stata, Revised Sixth Edition* is aimed at new Stata users who want to become proficient in Stata. After reading this introductory text, new users will be able to not only use Stata well but also learn new aspects of Stata.

# Interpreting and Visualizing Regression Models Using Stata

Michael Mitchell’s *Interpreting and Visualizing Regression Models Using Stata, Second Edition* is a clear treatment of how to carefully present results from model-fitting in a wide variety of settings. It is a boon to anyone who has to present the tangible meaning of a complex model in a clear fashion, regardless of the audience.

# The Workflow of Data Analysis Using Stata

*The Workflow of Data Analysis Using Stata*, by J. Scott Long, is an essential productivity tool for data analysts. Aimed at anyone who analyzes data, this book presents an effective strategy for designing and doing data-analytic projects.

In this book, Long presents lessons gained from his experience with numerous academic publications, as a coauthor of the immensely popular Regression Models for Categorical Dependent Variables Using Stata, and as a coauthor of the SPOST routines, which are downloaded over 20,000 times a year.

# Data Management Using Stata: A Practical Handbook

The book is modular in structure, with modules based on data management tasks rather than on clusters of commands. This format is helpful because it allows readers to find just what they need to solve a problem at hand. To complement this format, the book is in a style that will teach even sporadic readers good habits in data management, even if the reader chooses to read chapters out of order.

# Meta-Analysis in Stata: An Updated Collection from the Stata Journal

Meta-analysis allows researchers to combine results of several studies into a unified analysis that provides an overall estimate of the effect of interest and to quantify the uncertainty of that estimate. Stata has some of the best statistical tools available for doing meta-analysis. The unusual thing about these tools is that none of them are part of official Stata. They are all created by and documented by experts in the broader research community who also happen to be proficient Stata developers.

# Microeconometrics Using Stata, Volume I: Cross-Sectional and Panel Regression Methods

Any applied economic researcher using Stata and anyone teaching or studying microeconometrics will benefit from Cameron and Trivedi's two volumes. They are an invaluable reference of the theory and intuition behind microeconometric methods using Stata. Those familiar with Cameron and Trivedi's *Microeconometrics: Methods and Applications* will find the same rigor. Those familiar with the previous edition of *Microeconometrics Using Stata* will find the same explanation of Stata commands, their interpretation, and their connection with microeconometric theory as well as an introduction to computational concepts that should be part of any researcher's toolbox.

# An Introduction to Modern Econometrics using Stata

*An Introduction to Modern Econometrics Using Stata*, by Christopher F. Baum, successfully bridges the gap between learning econometrics and learning how to use Stata. The book presents a contemporary approach to econometrics, emphasizing the role of method-of-moments estimators, hypothesis testing, and specification analysis while providing practical examples showing how the theory is applied to real datasets by using Stata.

# An Introduction to Stata Programming

This new edition reflects some of the most important statistical tools added since Stata 10. Of note are factor variables and operators, the computation of marginal effects, marginal means, and predictive margins using **margins**, the use of **gmm** to implement generalized method of moments estimation, and the use of **suest** for seemingly unrelated estimation.

# Maximum Likelihood Estimation with Stata

*Maximum Likelihood Estimation with Stata, Fifth Edition* is the essential reference and guide for researchers in all disciplines who wish to write maximum likelihood (ML) estimators in Stata. Beyond providing comprehensive coverage of Stata's command for writing ML estimators, the book presents an overview of the underpinnings of maximum likelihood and how to think about ML estimation.

# Regression Models for Categorical Dependent Variables using Stata

*Regression Models for Categorical Dependent Variables Using Stata, Third Edition*, by J. Scott Long and Jeremy Freese, is an essential reference for those who use Stata to fit and interpret regression models for categorical data. Although regression models for categorical dependent variables are common, few texts explain how to interpret such models; this text decisively fills the void.

# Data Analysis using Stata

*Data Analysis Using Stata, Third Edition* has been completely revamped to reflect the capabilities of Stata 12. This book will appeal to those just learning statistics and Stata, as well as to the many users who are switching to Stata from other packages. Throughout the book, Kohler and Kreuter show examples using data from the German Socio-Economic Panel, a large survey of households containing demographic, income, employment, and other key information.

# A Visual Guide to Stata Graphics

Michael Mitchell’s *A Visual Guide to Stata Graphics, Fourth Edition* provides an essential introduction and reference for Stata graphics. The fourth edition retains the features that made the first three editions so useful:

- A complete guide to Stata’s
**graph**command - Exhaustive examples of customized graphs
- Visual indexing of features—just look for a picture that matches what you want to do

# Generalized Linear Models and Extensions

This text thoroughly covers GLMs, both theoretically and computationally, with an emphasis on Stata. The theory consists of showing how the various GLMs are special cases of the exponential family, showing general properties of this family of distributions, and showing the derivation of maximum likelihood (ML) estimators and standard errors. Hardin and Hilbe show how iteratively reweighted least squares, another method of parameter estimation, is a consequence of ML estimation using Fisher scoring. The authors also discuss different methods of estimating standard errors, including robust methods, robust methods with clustering, Newey–West, outer product of the gradient, bootstrap, and jackknife. The thorough coverage of model diagnostics includes measures of influence such as Cook’s distance, several forms of residuals, the Akaike and Bayesian information criteria, and various *R*^{2}-type measures of explained variability.

**What's new in this edition:**

- New chapter on multivariate models
- New chapter on Bayesian analysis
- Generalized negative binomial models of Waring and Famoye
- Bias-corrected GLMs
- More examples of creating synthetic data for various binomial and count models

**This book is of particular interest for:**

- Applied researchers who analyze binary, count, and categorical data
- Instructors who teach GLM courses
- Researchers familiar with generalized linear models but who are new to Stata
- Stata users looking for a theoretical reference for generalized linear models

# Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model

This text is concerned with obtaining a compromise between Cox and parametric models that retains the desired features of both types of models. The book is aimed at researchers who are familiar with the basic concepts of survival analysis and with the stcox and streg commands in Stata.

# Discovering Structural Equation Modeling Using Stata

*Discovering Structural Equation Modeling Using Stata*, by Alan Acock, successfully introduces both the statistical principles involved in structural equation modeling (SEM) and the use of Stata to fit these models. The book uses an application-based approach to teaching SEM.

# Aplicaciones en Economía y Ciencias Sociales con Stata

*Aplicaciones en Economía y Ciencias Sociales con Stata* es la primera publicación en español de Stata Press. El contenido ha sido el resultado de un trabajo que reúne a diversos autores en diferentes áreas de conocimiento y que muestran el uso de una variedad de herramientas de análisis disponibles en Stata.

# Thirty years with Stata: A Retrospective

The view from the inside opens with an essay by Bill Gould, Stata's president and cofounder, that discusses the challenges and concepts that guided the design and implementation of Stata.

# Bayesian Analysis with Stata

The book is careful to introduce concepts and coding tools incrementally so that there are no steep patches or discontinuities in the learning curve. The content helps the user see exactly what computations are done for simple standard models and shows the user how those computations are implemented. Understanding these concepts is important for users because Bayesian analysis lends itself to custom or very complex models, and users must be able to code these themselves.

# One Hundred Nineteen Stata Tips

*One Hundred Nineteen Stata Tips* provides concise and insightful notes about commands, features, and tricks that will help you obtain a deeper understanding of Stata. The book comprises the contributions of the Stata community that have appeared in the *Stata Journal* since 2003.

# PROSUITE

ProSuite is an integrated collection of Provalis Research tools that allow one to explore, analyze and relate both structured and unstructured data.

# SIMSTAT 2.6

*Simstat* goes beyond mere statistical analysis. It offers output management features not found in any other program, as well as its own scripting language to automate statistical analysis and to write small applications, interactive tutorials with multimedia capabilities, as well as computer assisted interviewing systems.

# Consulenza STATA

Consulenza STATA e altri software Consulenza STATA e altri software Consulenza STATA e altri software Consulenza STATA e altri software Consulenza STATA e altri software Consulenza STATA e altri software Consulenza STATA e altri software Consulenza STATA e altri software Consulenza STATA e altri software Consulenza STATA e altri software Consulenza STATA e altri software Consulenza STATA e altri software Consulenza STATA e altri software Consulenza STATA e altri software Consulenza STATA e altri software Consulenza STATA e altri software Consulenza STATA e altri software Consulenza STATA e altri software Consulenza STATA e altri software Consulenza STATA e altri software Consulenza STATA e altri software Consulenza STATA e altri software Consulenza STATA e altri software Consulenza STATA e altri software

# Up and Running in Stata

Up and Running in *Stata* provides participants with the necessary introductory toolset to enable them to carry out efficient data analysis and data management in *Stata* in a reproducible manner. The course covers everything from the very basics, in order to get one up and running in *Stata*, to an overview of the available *Stata* commands for preliminary data analysis, data management, importing and exporting data formats, merging of databases and the creation of graphs in *Stata*.

# Introduction to a Bayesian analysis in Stata

# Financial Time Series Analysis with Stata

# Aperte iscrizioni corso Modelli di Regressione Logistica

# An Introduction to Stata for Health Researchers

The book is based on the assumption that the reader has some basic knowledge of statistics but no knowledge of Stata. The authors build the reader's abilities as a builder would build a house: laying a firm foundation in Stata, framing a general structure in which good work can be accomplished, adding the details that are particular to various types of statistical analyses, and, finally, trimming with a thorough treatment of graphics and special topics such as power and sample-size computations.

# Probabilité et Statistique pour les Sciences de la Santé: Apprentissage au Moyen du Logiciel Stata

Cet ouvrage (en français) non seulement présente, de façon rigoureuse, les concepts et méthodes statistiques, mais aussi utilise des exemples concrets pour illustrer chaque concept théorique nouvellement introduit.