Best fixed effects regression models list

We spent many hours on research to finding fixed effects regression models, reading product features, product specifications for this guide. For those of you who wish to the best fixed effects regression models, you should not miss this article. fixed effects regression models coming in a variety of types but also different price range. The following is the top 10 fixed effects regression models by our suggestions:

Best fixed effects regression models

Product Features Editor's score Go to site
Fixed Effects Regression Models (Quantitative Applications in the Social Sciences) Fixed Effects Regression Models (Quantitative Applications in the Social Sciences)
Go to amazon.com
Fixed Effects Regression Methods for Longitudinal Data Using SAS Fixed Effects Regression Methods for Longitudinal Data Using SAS
Go to amazon.com
Survival Analysis Using SAS: A Practical Guide, Second Edition Survival Analysis Using SAS: A Practical Guide, Second Edition
Go to amazon.com
Logistic Regression Using SAS: Theory and Application, Second Edition Logistic Regression Using SAS: Theory and Application, Second Edition
Go to amazon.com
Missing Data (Quantitative Applications in the Social Sciences) Missing Data (Quantitative Applications in the Social Sciences)
Go to amazon.com
Multiple Regression: A Primer (Research Methods and Statistics) Multiple Regression: A Primer (Research Methods and Statistics)
Go to amazon.com
Event History and Survival Analysis (Quantitative Applications in the Social Sciences) Event History and Survival Analysis (Quantitative Applications in the Social Sciences)
Go to amazon.com
Methods and Applications of Linear Models: Regression and the Analysis of Variance Methods and Applications of Linear Models: Regression and the Analysis of Variance
Go to amazon.com
An R Companion to Linear Statistical Models An R Companion to Linear Statistical Models
Go to amazon.com
Applied Longitudinal Analysis Applied Longitudinal Analysis
Go to amazon.com
Related posts:

1. Fixed Effects Regression Models (Quantitative Applications in the Social Sciences)

Feature

Used Book in Good Condition

Description

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 Here

2. Fixed Effects Regression Methods for Longitudinal Data Using SAS

Feature

ISBN13: 9781590475683
Condition: New
Notes: BRAND NEW FROM PUBLISHER! 100% Satisfaction Guarantee. Tracking provided on most orders. Buy with Confidence! Millions of books sold!

Description

Fixed Effects Regression Methods for Longitudinal Data Using SAS, written by Paul Allison, is an invaluable resource for all researchers interested in adding fixed effects regression methods to their tool kit of statistical techniques. First introduced by economists, fixed effects methods are gaining widespread use throughout the social sciences. Designed to eliminate major biases from regression models with multiple observations (usually longitudinal) for each subject (usually a person), fixed effects methods essentially offer control for all stable characteristics of the subjects, even characteristics that are difficult or impossible to measure. This straightforward and thorough text shows you how to estimate fixed effects models with several SAS procedures that are appropriate for different kinds of outcome variables. The theoretical background of each model is explained, and the models are then illustrated with detailed examples using real data. The book contains thorough discussions of the following uses of SAS procedures: PROC GLM for estimating fixed effects linear models for quantitative outcomes, PROC LOGISTIC for estimating fixed effects logistic regression models, PROC PHREG for estimating fixed effects Cox regression models for repeated event data, PROC GENMOD for estimating fixed effects Poisson regression models for count data, and PROC CALIS for estimating fixed effects structural equation models. To gain the most benefit from this book, readers should be familiar with multiple linear regression, have practical experience using multiple regression on real data, and be comfortable interpreting the output from a regression analysis. An understanding of logistic regression and Poisson regression is a plus. Some experience with SAS is helpful, but not required.

This book is part of the SAS Press program.

3. Survival Analysis Using SAS: A Practical Guide, Second Edition

Feature

Used Book in Good Condition

Description

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 is part of the SAS Press program.

4. Logistic Regression Using SAS: Theory and Application, Second Edition

Feature

Used Book in Good Condition

Description

If you are a researcher or student with experience in multiple linear regression and want to learn about logistic regression, Paul Allison's Logistic Regression Using SAS: Theory and Application, Second Edition, is for you! Informal and nontechnical, this book both explains the theory behind logistic regression, and looks at all the practical details involved in its implementation using SAS. Several real-world examples are included in full detail. This book also explains the differences and similarities among the many generalizations of the logistic regression model. The following topics are covered: binary logistic regression, logit analysis of contingency tables, multinomial logit analysis, ordered logit analysis, discrete-choice analysis, and Poisson regression. Other highlights include discussions on how to use the GENMOD procedure to do loglinear analysis and GEE estimation for longitudinal binary data. Only basic knowledge of the SAS DATA step is assumed. The second edition describes many new features of PROC LOGISTIC, including conditional logistic regression, exact logistic regression, generalized logit models, ROC curves, the ODDSRATIO statement (for analyzing interactions), and the EFFECTPLOT statement (for graphing nonlinear effects). Also new is coverage of PROC SURVEYLOGISTIC (for complex samples), PROC GLIMMIX (for generalized linear mixed models), PROC QLIM (for selection models and heterogeneous logit models), and PROC MDC (for advanced discrete choice models).

This book is part of the SAS Press program.

5. Missing Data (Quantitative Applications in the Social Sciences)

Description

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.

6. Multiple Regression: A Primer (Research Methods and Statistics)

Feature

Pine Forge Press

Description

Presenting topics in the form of questions and answers, this popular supplemental text offers a brief introduction on multiple regression on a conceptual level. Author Paul D. Allison answers the most essential questions (such as how to read and interpret

7. Event History and Survival Analysis (Quantitative Applications in the Social Sciences)

8. Methods and Applications of Linear Models: Regression and the Analysis of Variance

Feature

Used Book in Good Condition

Description

Praise for the Second Edition

"An essential desktop reference book . . . it should definitely be on your bookshelf."
Technometrics

A thoroughly updated book, Methods and Applications of Linear Models: Regression and the Analysis of Variance, Third Edition features innovative approaches to understanding and working with models and theory of linear regression. The Third Edition provides readers with the necessary theoretical concepts, which are presented using intuitive ideas rather than complicated proofs, to describe the inference that is appropriate for the methods being discussed.

The book presents a unique discussion that combines coverage of mathematical theory of linear models with analysis of variance models, providing readers with a comprehensive understanding of both the theoretical and technical aspects of linear models. With a new focus on fixed effects models, Methods and Applications of Linear Models: Regression and the Analysis of Variance, Third Edition also features:

  • Newly added topics including least squares, the cell means model, and graphical inspection of data in the AVE method
  • Frequent conceptual and numerical examples for clarifying the statistical analyses and demonstrating potential pitfalls
  • Graphics and computations developed using JMP software to accompany the concepts and techniques presented
  • Numerous exercises presented to test readers and deepen their understanding of the material

An ideal book for courses on linear models and linear regression at the undergraduate and graduate levels, the Third Edition of Methods and Applications of Linear Models: Regression and the Analysis of Variance is also a valuable reference for applied statisticians and researchers who utilize linear model methodology.

9. An R Companion to Linear Statistical Models

Description

Focusing on user-developed programming, An R Companion to Linear Statistical Models serves two audiences: those who are familiar with the theory and applications of linear statistical models and wish to learn or enhance their skills in R; and those who are enrolled in an R-based course on regression and analysis of variance. For those who have never used R, the book begins with a self-contained introduction to R that lays the foundation for later chapters.

This book includes extensive and carefully explained examples of how to write programs using the R programming language. These examples cover methods used for linear regression and designed experiments with up to two fixed-effects factors, including blocking variables and covariates. It also demonstrates applications of several pre-packaged functions for complex computational procedures.

10. Applied Longitudinal Analysis

Feature

Wiley

Description

Praise for the First Edition

". . . [this book] should be on the shelf of everyone interestedin . . . longitudinal data analysis."
Journal of the American Statistical Association

Features newly developed topics and applications of theanalysis of longitudinal data

Applied Longitudinal Analysis, Second Edition presentsmodern methods for analyzing data from longitudinal studies and nowfeatures the latest state-of-the-art techniques. The bookemphasizes practical, rather than theoretical, aspects of methodsfor the analysis of diverse types of longitudinal data that can beapplied across various fields of study, from the health and medicalsciences to the social and behavioral sciences.

The authors incorporate their extensive academic and researchexperience along with various updates that have been made inresponse to reader feedback. The Second Edition features six newlyadded chapters that explore topics currently evolving in the field,including:

  • Fixed effects and mixed effects models
  • Marginal models and generalized estimating equations
  • Approximate methods for generalized linear mixed effectsmodels
  • Multiple imputation and inverse probability weightedmethods
  • Smoothing methods for longitudinal data
  • Sample size and power

Each chapter presents methods in the setting of applications todata sets drawn from the health sciences. New problem sets havebeen added to many chapters, and a related website features sampleprograms and computer output using SAS, Stata, and R, as well asdata sets and supplemental slides to facilitate a completeunderstanding of the material.

With its strong emphasis on multidisciplinary applications andthe interpretation of results, Applied LongitudinalAnalysis, Second Edition is an excellent book for courses onstatistics in the health and medical sciences at theupper-undergraduate and graduate levels. The book also serves as avaluable reference for researchers and professionals in themedical, public health, and pharmaceutical fields as well as thosein social and behavioral sciences who would like to learn moreabout analyzing longitudinal data.

Conclusion

All above are our suggestions for fixed effects regression models. This might not suit you, so we prefer that you read all detail information also customer reviews to choose yours. Please also help to share your experience when using fixed effects regression models with us by comment in this post. Thank you!

You Might Also Like