Event History Analysis With Stata

  • Filename: event-history-analysis-with-stata.
  • ISBN: 9781135595920
  • Release Date: 2012-10-12
  • Number of pages: 312
  • Author: Hans-Peter Blossfeld
  • Publisher: Psychology Press



Event History Analysis With Stata provides an introduction to event history modeling techniques using Stata (version 9), a widely used statistical program that provides tools for data analysis. The book emphasizes the usefulness of event history models for causal analysis in the social sciences and the application of continuous-time models. The authors illustrate the entire research path required in the application of event-history analysis, from the initial problems of recording event-oriented data, to data organization, to applications using the software, to the interpretation of results. The book also demonstrates, through example, how to implement hypotheses tests and how to choose the right model. The strengths and limitations of various techniques are emphasized in each example, along with an introduction to the model, details on how to input data, and the related Stata commands. Each application is accompanied by a brief explanation of the underlying statistical concept. Readers are offered the unique opportunity to easily run and modify all of the book’s application examples on a computer, by visiting the author’s Web site at http://www.uni-bamberg.de/sowi/soziologie-i/eha/. Examples include survival rates of patients in medical studies; unemployment periods in economic studies; and the time it takes a criminal to break the law after his release in a criminological study. This new book supplements Event History Analysis, by Blossfeld et al, and Techniques of Event History Modeling, by Blossfeld and Rohwer, extending on their coverage of practical applications and statistical theory. Intended for researchers in a variety of fields such as statistics, economics, psychology, sociology, and political science, Event History Analysis With Stata also serves as a text, in combination with the authors’ other two books, for courses on event history analysis.

Techniques of Event History Modeling

  • Filename: techniques-of-event-history-modeling.
  • ISBN: 9781135639129
  • Release Date: 2001-09-01
  • Number of pages: 320
  • Author: Hans-Peter Blossfeld
  • Publisher: Psychology Press



Including new developments and publications which have appeared since the publication of the first edition in 1995, this second edition: *gives a comprehensive introductory account of event history modeling techniques and their use in applied research in economics and the social sciences; *demonstrates that event history modeling is a major step forward in causal analysis. To do so the authors show that event history models employ the time-path of changes in states and relate changes in causal variables in the past to changes in discrete outcomes in the future; and *introduces the reader to the computer program Transition Data Analysis (TDA). This software estimates the sort of models most frequently used with longitudinal data, in particular, discrete-time and continuous-time event history data. Techniques of Event History Modeling can serve as a student textbook in the fields of statistics, economics, the social sciences, psychology, and the political sciences. It can also be used as a reference for scientists in all fields of research.

Introducing Survival and Event History Analysis

  • Filename: introducing-survival-and-event-history-analysis.
  • ISBN: 9781848601024
  • Release Date: 2011-01-19
  • Number of pages: 279
  • Author: Melinda Mills
  • Publisher: SAGE Publications



This book is an accessible, practical and comprehensive guide for researchers from multiple disciplines including biomedical, epidemiology, engineering and the social sciences. Written for accessibility, this book will appeal to students and researchers who want to understand the basics of survival and event history analysis and apply these methods without getting entangled in mathematical and theoretical technicalities. Inside, readers are offered a blueprint for their entire research project from data preparation to model selection and diagnostics. Engaging, easy to read, functional and packed with enlightening examples, ‘hands-on’ exercises, conversations with key scholars and resources for both students and instructors, this text allows researchers to quickly master advanced statistical techniques. It is written from the perspective of the ‘user’, making it suitable as both a self-learning tool and graduate-level textbook. Also included are up-to-date innovations in the field, including advancements in the assessment of model fit, unobserved heterogeneity, recurrent events and multilevel event history models. Practical instructions are also included for using the statistical programs of R, STATA and SPSS, enabling readers to replicate the examples described in the text.

An Introduction to Survival Analysis Using Stata Second Edition

  • Filename: an-introduction-to-survival-analysis-using-stata-second-edition.
  • ISBN: 9781597180412
  • Release Date: 2008-05-15
  • Number of pages: 372
  • Author: Mario Cleves
  • Publisher: Stata Press



An Introduction to Survival Analysis Using Stata, Third Edition provides the foundation to understand various approaches for analyzing time-to-event data. It is not only a tutorial for learning survival analysis but also a valuable reference for using Stata to analyze survival data. Although the book assumes knowledge of statistical principles, simple probability, and basic Stata, it takes a practical, rather than mathematical, approach to the subject. This updated third edition highlights new features of Stata 11, including competing-risks analysis and the treatment of missing values via multiple imputation. Other additions include new diagnostic measures after Cox regression, Stata's new treatment of categorical variables and interactions, and a new syntax for obtaining prediction and diagnostics after Cox regression. After reading this book, you will understand the formulas and gain intuition about how various survival analysis estimators work and what information they exploit. You will also acquire deeper, more comprehensive knowledge of the syntax, features, and underpinnings of Stata's survival analysis routines.

Event History Modeling

  • Filename: event-history-modeling.
  • ISBN: 0521546737
  • Release Date: 2004-03-29
  • Number of pages: 218
  • Author: Janet M. Box-Steffensmeier
  • Publisher: Cambridge University Press



Event History Modeling provides an accessible, up-to-date guide to event history analysis for researchers and advanced students in the social sciences. The authors explain the foundational principles of event-history analysis, and analyse numerous examples which they estimate and interpret using standard statistical packages, such as STATA and S-Plus. They review recent and critical innovations in diagnostics, including testing the proportional hazards assumption, identifying outliers, and assessing model fit. They also discuss common problems encountered with time-to-event data, and make recommendations regarding the implementation of duration modeling methods.

Event History Analysis

  • Filename: event-history-analysis.
  • ISBN: 0803920555
  • Release Date: 1984-11-01
  • Number of pages: 87
  • Author: Paul D. Allison
  • Publisher: SAGE



Drawing on recent "event history" analytical methods from biostatistics, engineering, and sociology, this clear and comprehensive monograph explains how longitudinal data can be used to study the causes of deaths, crimes, wars, and many other human events. Allison shows why ordinary multiple regression is not suited to analyze event history data, and demonstrates how innovative regression - like methods can overcome this problem. He then discusses the particular new methods that social scientists should find useful.

Applied Longitudinal Data Analysis

  • Filename: applied-longitudinal-data-analysis.
  • ISBN: 0199760721
  • Release Date: 2003-03-27
  • Number of pages: 644
  • Author: Judith D. Singer
  • Publisher: Oxford University Press



Change is constant in everyday life. Infants crawl and then walk, children learn to read and write, teenagers mature in myriad ways, the elderly become frail and forgetful. Beyond these natural processes and events, external forces and interventions instigate and disrupt change: test scores may rise after a coaching course, drug abusers may remain abstinent after residential treatment. By charting changes over time and investigating whether and when events occur, researchers reveal the temporal rhythms of our lives. Applied Longitudinal Data Analysis is a much-needed professional book for empirical researchers and graduate students in the behavioral, social, and biomedical sciences. It offers the first accessible in-depth presentation of two of today's most popular statistical methods: multilevel models for individual change and hazard/survival models for event occurrence (in both discrete- and continuous-time). Using clear, concise prose and real data sets from published studies, the authors take you step by step through complete analyses, from simple exploratory displays that reveal underlying patterns through sophisticated specifications of complex statistical models. Applied Longitudinal Data Analysis offers readers a private consultation session with internationally recognized experts and represents a unique contribution to the literature on quantitative empirical methods. Visit http://www.ats.ucla.edu/stat/examples/alda.htm for: · Downloadable data sets · Library of computer programs in SAS, SPSS, Stata, HLM, MLwiN, and more · Additional material for data analysis

Event History and Survival Analysis

  • Filename: event-history-and-survival-analysis.
  • ISBN: 9781483316055
  • Release Date: 2014-02-19
  • Number of pages: 112
  • Author: Paul D. Allison
  • Publisher: SAGE Publications



Social scientists are interested in events and their causes. Although event histories are ideal for studying the causes of events, they typically possess two features—censoring and time-varying explanatory variables—that create major problems for standard statistical procedures. Several innovative approaches have been developed to accommodate these two peculiarities of event history data. This volume surveys these methods, concentrating on the approaches that are most useful to the social sciences. In particular, Paul D. Allison focuses on regression methods in which the occurrence of events is dependent on one or more explanatory variables. He gives attention to the statistical models that form the basis of event history analysis, and also to practical concerns such as data management, cost, and useful computer software. The Second Edition is part of SAGE’s Quantitative Applications in the Social Sciences (QASS) series, which continues to serve countless students, instructors, and researchers in learning the most cutting-edge quantitative techniques.

Survival Analysis

  • Filename: survival-analysis.
  • ISBN: 0387239189
  • Release Date: 2005-01-01
  • Number of pages: 590
  • Author: David G. Kleinbaum
  • Publisher: Springer Science & Business Media



This greatly expanded second edition of Survival Analysis- A Self-learning Text provides a highly readable description of state-of-the-art methods of analysis of survival/event-history data. This text is suitable for researchers and statisticians working in the medical and other life sciences as well as statisticians in academia who teach introductory and second-level courses on survival analysis. The second edition continues to use the unique "lecture-book" format of the first (1996) edition with the addition of three new chapters on advanced topics: Chapter 7: Parametric Models Chapter 8: Recurrent events Chapter 9: Competing Risks. Also, the Computer Appendix has been revised to provide step-by-step instructions for using the computer packages STATA (Version 7.0), SAS (Version 8.2), and SPSS (version 11.5) to carry out the procedures presented in the main text. The original six chapters have been modified slightly to expand and clarify aspects of survival analysis in response to suggestions by students, colleagues and reviewers, and to add theoretical background, particularly regarding the formulation of the (partial) likelihood functions for proportional hazards, stratified, and extended Cox regression models David Kleinbaum is Professor of Epidemiology at the Rollins School of Public Health at Emory University, Atlanta, Georgia. Dr. Kleinbaum is internationally known for innovative textbooks and teaching on epidemiological methods, multiple linear regression, logistic regression, and survival analysis. He has provided extensive worldwide short-course training in over 150 short courses on statistical and epidemiological methods. He is also the author of ActivEpi (2002), an interactive computer-based instructional text on fundamentals of epidemiology, which has been used in a variety of educational environments including distance learning. Mitchel Klein is Research Assistant Professor with a joint appointment in the Department of Environmental and Occupational Health (EOH) and the Department of Epidemiology, also at the Rollins School of Public Health at Emory University. Dr. Klein is also co-author with Dr. Kleinbaum of the second edition of Logistic Regression- A Self-Learning Text (2002). He has regularly taught epidemiologic methods courses at Emory to graduate students in public health and in clinical medicine. He is responsible for the epidemiologic methods training of physicians enrolled in Emorya??s Master of Science in Clinical Research Program, and has collaborated with Dr. Kleinbaum both nationally and internationally in teaching several short courses on various topics in epidemiologic methods.

Multilevel and Longitudinal Modeling Using Stata Second Edition

  • Filename: multilevel-and-longitudinal-modeling-using-stata-second-edition.
  • ISBN: 9781597180405
  • Release Date: 2008-02-07
  • Number of pages: 562
  • Author: Sophia Rabe-Hesketh
  • Publisher: Stata Press



This is a book about applied multilevel and longitudinal modeling. Other terms for multilevel models include hierarchical models, random-effects or random-coefficient models, mixed-effects models, or simply mixed models. Longitudinal data are also referred to as panel data, repeated measures, or cross-sectional time series. A popular type of multilevel model for longitudinal data is the growth-curve model. Our emphasis is on explaining the models and their assumptions, applying the methods to real data, and interpreting results.

Statistics with STATA Version 12

  • Filename: statistics-with-stata-version-12.
  • ISBN: 9780840064639
  • Release Date: 2012-08-28
  • Number of pages: 496
  • Author: Lawrence Hamilton
  • Publisher: Cengage Learning



For students and practicing researchers alike, STATISTICS WITH STATA Version 12 opens the door to the full use of the popular Stata program--a fast, flexible, and easy-to-use environment for data management and statistics analysis. Integrating Stata’s impressive graphics, this comprehensive book presents hundreds of examples showing how to apply Stata to accomplish a wide variety of tasks. Like Stata itself, STATISTICS WITH STATA will make it easier for readers to move fluidly through the world of modern data analysis. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

Delaying the Postsecondary Education Dream A Discrete time Event History Analysis of Financial Aid Information Effects on Time to initial Enrollment

  • Filename: delaying-the-postsecondary-education-dream-a-discrete-time-event-history-analysis-of-financial-aid-information-effects-on-time-to-initial-enrollment.
  • ISBN: 9780549769866
  • Release Date: 2008
  • Number of pages: 178
  • Author:
  • Publisher: ProQuest



Using traditional logistic regression, multinomial logistic regression, Non-Parametric Event History Analysis (EHA) methods, as well as discrete-time and Competing Risks EHA regression, I explore the relationships between race/ethnicity and SES on the timing of initial postsecondary education enrollment. Additionally, I investigate whether financial aid information as a form of capital is associated with the timing and institutional type of postsecondary education enrollment.

Propensity Score Analysis

  • Filename: propensity-score-analysis.
  • ISBN: 9781483322520
  • Release Date: 2014-06-11
  • Number of pages: 448
  • Author: Shenyang Guo
  • Publisher: SAGE Publications



Fully updated to reflect the most recent changes in the field, the Second Edition of Propensity Score Analysis provides an accessible, systematic review of the origins, history, and statistical foundations of propensity score analysis, illustrating how it can be used for solving evaluation and causal-inference problems. With a strong focus on practical applications, the authors explore various strategies for employing PSA, discuss the use of PSA with alternative types of data, and delineate the limitations of PSA under a variety of constraints. Unlike existing textbooks on program evaluation and causal inference, this book delves into statistical concepts, formulas, and models within the context of a robust and engaging focus on application.

Applied Survey Data Analysis

  • Filename: applied-survey-data-analysis.
  • ISBN: 1420080679
  • Release Date: 2010-04-05
  • Number of pages: 487
  • Author: Steven G. Heeringa
  • Publisher: CRC Press



Taking a practical approach that draws on the authors’ extensive teaching, consulting, and research experiences, Applied Survey Data Analysis provides an intermediate-level statistical overview of the analysis of complex sample survey data. It emphasizes methods and worked examples using available software procedures while reinforcing the principles and theory that underlie those methods. After introducing a step-by-step process for approaching a survey analysis problem, the book presents the fundamental features of complex sample designs and shows how to integrate design characteristics into the statistical methods and software for survey estimation and inference. The authors then focus on the methods and models used in analyzing continuous, categorical, and count-dependent variables; event history; and missing data problems. Some of the techniques discussed include univariate descriptive and simple bivariate analyses, the linear regression model, generalized linear regression modeling methods, the Cox proportional hazards model, discrete time models, and the multiple imputation analysis method. The final chapter covers new developments in survey applications of advanced statistical techniques, including model-based analysis approaches. Designed for readers working in a wide array of disciplines who use survey data in their work, this book also provides a useful framework for integrating more in-depth studies of the theory and methods of survey data analysis. A guide to the applied statistical analysis and interpretation of survey data, it contains many examples and practical exercises based on major real-world survey data sets. Although the authors use Stata for most examples in the text, they offer SAS, SPSS, SUDAAN, R, WesVar, IVEware, and Mplus software code for replicating the examples on the book’s website: http://www.isr.umich.edu/src/smp/asda/

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