Event History Modeling

Author: Janet M. Box-Steffensmeier
Publisher: Cambridge University Press
ISBN: 9780521546737
Size: 47.14 MB
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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.

Regression Models For Categorical And Limited Dependent Variables

Author: J. Scott Long
Publisher: SAGE
ISBN: 9780803973749
Size: 75.17 MB
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A unified treatment of the most useful models for categorical and limited dependent variables (CLDVs) is provided in this book. Throughout, the links among the models are made explicit, and common methods of derivation, interpretation and testing are applied. In addition, the author explains how models relate to linear regression models whenever possible.

Time Series Analysis For The Social Sciences

Author: Janet M. Box-Steffensmeier
Publisher: Cambridge University Press
ISBN: 1316060500
Size: 39.56 MB
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Time series, or longitudinal, data are ubiquitous in the social sciences. Unfortunately, analysts often treat the time series properties of their data as a nuisance rather than a substantively meaningful dynamic process to be modeled and interpreted. Time Series Analysis for the Social Sciences provides accessible, up-to-date instruction and examples of the core methods in time series econometrics. Janet M. Box-Steffensmeier, John R. Freeman, Jon C. Pevehouse and Matthew P. Hitt cover a wide range of topics including ARIMA models, time series regression, unit-root diagnosis, vector autoregressive models, error-correction models, intervention models, fractional integration, ARCH models, structural breaks, and forecasting. This book is aimed at researchers and graduate students who have taken at least one course in multivariate regression. Examples are drawn from several areas of social science, including political behavior, elections, international conflict, criminology, and comparative political economy.

Fixed Effects Regression Models

Author: Paul D. Allison
Publisher: SAGE Publications
ISBN: 1483389278
Size: 46.15 MB
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This book demonstrates how to estimate and interpret fixed-effects models in a variety of different modeling contexts: linear models, logistic models, Poisson models, Cox regression models, and structural equation models. Both advantages and disadvantages of fixed-effects models will be considered, along with detailed comparisons with random-effects models. Written at a level appropriate for anyone who has taken a year of statistics, the book is appropriate as a supplement for graduate courses in regression or linear regression as well as an aid to researchers who have repeated measures or cross-sectional data. Learn more about "The Little Green Book" - QASS Series! Click Here

Introducing Survival And Event History Analysis

Author: Melinda Mills
Publisher: SAGE Publications
ISBN: 1848601026
Size: 62.90 MB
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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.

Event History Analysis With Stata

Author: Hans-Peter Blossfeld
Publisher: Psychology Press
ISBN: 1135595933
Size: 71.25 MB
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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.

Event History Analysis

Author: Paul D. Allison
Publisher: SAGE
ISBN: 9780803920552
Size: 49.78 MB
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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.

Spatial Analysis For The Social Sciences

Author: David Darmofal
Publisher: Cambridge University Press
ISBN: 0521888263
Size: 75.96 MB
Format: PDF, ePub
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This book shows how to model the spatial interactions between actors that are at the heart of the social sciences.

Propensity Score Analysis

Author: Shenyang Guo
Publisher: SAGE
ISBN: 1452235007
Size: 52.94 MB
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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.