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Sooner or later anyone who does statistical analysis runs into problems with missing data in which information for some variables is missing for some cases. Why is this a problem? Because most statistical methods presume that every case has information on all the variables to be included in the analysis. Using numerous examples and practical tips, this book offers a nontechnical explanation of the standard methods for missing data (such as listwise or casewise deletion) as well as two newer (and, better) methods, maximum likelihood and multiple imputation. Anyone who has been relying on ad-hoc methods that are statistically inefficient or biased will find this book a welcome and accessible solution to their problems with handling missing data.
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.
This extremely well-written, straightforward book gives you the flexibility to cover regression more thoroughly than do most statistics texts, without financially taxing your students, and is written at a level that undergraduate students can easily comprehend.
Easy to read and comprehensive, Survival Analysis Using SAS: A
Practical Guide, Second Edition, by Paul D. Allison, is an
accessible, data-based introduction to methods of survival
analysis. Researchers who want to analyze survival data with SAS
will find just what they need with this fully updated new edition
that incorporates the many enhancements in SAS procedures for
survival analysis in SAS 9. Although the book assumes only a
minimal knowledge of SAS, more experienced users will learn new
techniques of data input and manipulation. Numerous examples of SAS
code and output make this an eminently practical book, ensuring
that even the uninitiated become sophisticated users of survival
analysis. The main topics presented include censoring, survival
curves, Kaplan-Meier estimation, accelerated failure time models,
Cox regression models, and discrete-time analysis. Also included
are topics not usually covered in survival analysis books, such as
time-dependent covariates, competing risks, and repeated events.
Survival Analysis Using SAS: A Practical Guide, Second Edition, has
been thoroughly updated for SAS 9, and all figures are presented
using ODS Graphics. This new edition also documents major
enhancements to the STRATA statement in the LIFETEST procedure;
includes a section on the PROBPLOT command, which offers graphical
methods to evaluate the fit of each parametric regression model;
introduces the new BAYES statement for both parametric and Cox
models, which allows the user to do a Bayesian analysis using MCMC
methods; demonstrates the use of the counting process syntax as an
alternative method for handling time-dependent covariates; contains
a section on cumulative incidence functions; and describes the use
of the new GLIMMIX procedure to estimate random-effects models for
discrete-time data.
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
bookis 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
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