Fixed Effects Regression Models

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Author: Paul D. Allison

Publisher: SAGE Publications

ISBN: 1483389278

Category: Social Science

Page: 136

View: 2379

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

Assessing Inequality

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Author: Lingxin Hao

Publisher: SAGE

ISBN: 1412926297

Category: Social Science

Page: 149

View: 6140

Through developing a decomposition analysis of the inequality measures and promoting their effective use in research, this book provides readers with a step-by-step understanding of the inequality measures that are currently used.

Nonrecursive Models

Endogeneity, Reciprocal Relationships, and Feedback Loops

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Author: Pamela Paxton,John R. Hipp,Sandra Marquart-Pyatt,Sandra T. Marquart-Pyatt

Publisher: SAGE

ISBN: 1412974445

Category: Social Science

Page: 128

View: 6845

Nonrecursive Models provides explicit guidance to researchers on the estimation and assessment of nonrecursive simultaneous equation models in a clear, condensed and precise form. It guides readers through the specification and identification of simultaneous equation models, how to assess the quality of the estimates, and how to correctly interpret results.

Quantitative Narrative Analysis

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Author: Roberto Franzosi

Publisher: SAGE

ISBN: 1412925258

Category: Language Arts & Disciplines

Page: 175

View: 6726

Researchers and students in the social sciences.

The Association Graph and the Multigraph for Loglinear Models

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Author: Harry J. Khamis

Publisher: SAGE

ISBN: 1452238952

Category: Mathematics

Page: 136

View: 6645

The Association Graph and the Multigraph for Loglinear Models will help students, particularly those studying the analysis of categorical data, to develop the ability to evaluate and unravel even the most complex loglinear models without heavy calculations or statistical software. This supplemental text reviews loglinear models, explains the association graph, and introduces the multigraph to students who may have little prior experience of graphical techniques, but have some familiarity with categorical variable modeling. The author presents logical step-by-step techniques from the point of view of the practitioner, focusing on how the technique is applied to contingency table data and how the results are interpreted.

Multiple Correspondence Analysis

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Author: Brigitte Le Roux,Henry Rouanet

Publisher: SAGE Publications

ISBN: 1483389464

Category: Social Science

Page: 128

View: 5921

Requiring no prior knowledge of correspondence analysis, this text provides a nontechnical introduction to Multiple Correspondence Analysis (MCA) as a method in its own right. The authors, Brigitte LeRoux and Henry Rouanet, present thematerial in a practical manner, keeping the needs of researchers foremost in mind. Key Features Readers learn how to construct geometric spaces from relevant data, formulate questions of interest, and link statistical interpretation to geometric representations. They also learn how to perform structured data analysis and to draw inferential conclusions from MCA. The text uses real examples to help explain concepts. The authors stress the distinctive capacity of MCA to handle full-scale research studies. This supplementary text is appropriate for any graduate-level, intermediate, or advanced statistics course across the social and behavioral sciences, as well as for individual researchers. Learn more about "The Little Green Book" - QASS Series! Click Here

Spatial Regression Models

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Author: Michael D. Ward,Kristian Skrede Gleditsch

Publisher: SAGE

ISBN: 1412954150

Category: Mathematics

Page: 99

View: 5026

Assuming no prior knowledge this book is geared toward social science readers, unlike other volumes on this topic. The text illustrates concepts using well known international, comparative, and national examples of spatial regression analysis. Each example is presented alongside relevant data and code, which is also available on a Web site maintained by the authors.

Fixed Effects Regression Methods for Longitudinal Data Using SAS

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Author: Paul D. Allison

Publisher: SAS Institute

ISBN: 9781590477786

Category: Mathematics

Page: 160

View: 8708

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.

Hierarchical Linear Models

Applications and Data Analysis Methods

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Author: Stephen W. Raudenbush,Anthony S. Bryk

Publisher: SAGE

ISBN: 9780761919049

Category: Mathematics

Page: 485

View: 5809

Popular in its first edition for its rich, illustrative examples and lucid explanations of the theory and use of hierarchical linear models (HLM), the book has been updated to include: an intuitive introductory summary of the basic procedures for estimation and inference used with HLM models that only requires a minimal level of mathematical sophistication; a new section on multivariate growth models; a discussion of research synthesis or meta-analysis applications; aata analytic advice on centering of level-1 predictors, and new material on plausible value intervals and robust standard estimators.