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23
TEXTBOOKS
&
HANDBOOKS
APPLIED ORDINAL LOGISTIC REGRESSION USING STATA: From Single-Level to
Multilevel Modeling
Xing Liu, Eastern Connecticut State University
The first book to provide a unified framework for both single-level and multilevel modeling of ordinal categorical data,
Applied Ordinal Logistic Regression Using Stata helps readers learn how to conduct analyses, interpret the results
from Stata output, and present those results in scholarly writing. Using step-by-step instructions, this non-technical,
applied book leads students, applied researchers, and practitioners to a deeper understanding of statistical concepts
by closely connecting the underlying theories of models with the application of real-world data using statistical
software.
CONTENTS 1. Stata Basics / 2. Review of Basic Statistics / 3. Logistic Regression for Binary Data / 4. Proportional Odds Models for Ordinal
Response Variables / 5. Partial Proportional Odds Models and Generalized Ordinal Logistic Regression Models / 6. Continuation Ratio Models / 7.
Adjacent Categories Logistic Regression Models / 8. Stereotype Logistic Regression Models / 9. Ordinal Logistic Regression with Complex Survey
Sampling Designs / 10. Multilevel Modeling for Continuous and Binary Response Variables / 11. Multilevel Modeling for Ordinal Response Variables
/ 12. Beyond Ordinal Logistic Regression Models: Ordinal Probit Regression Models and Multinomial Logistic Regression Models
PAPERBACK ISBN: 978-1-4833-1975-9 • ©2016 • 552 PAGES • •
online
resources
APPLIED REGRESSION ANALYSIS & GENERALIZED LINEAR MODELS THIRD EDITION
John Fox, McMaster University, Canada
Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition
provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as
bootstrapping and missing data. Updated throughout, this edition includes new chapters on mixed-effects models for
hierarchical and longitudinal data.
CONTENTS 1. Statistical Models and Social Science / I DATA CRAFT / 2. What Is Regression Analysis? / 3. Examining Data / 4. Transforming
Data / II LINEAR MODELS AND LEAST SQUARES / 5. Linear Least-Squares Regression / 6. Statistical Inference for Regression / 7. Dummy-Variable
Regression / 8. Analysis of Variance / 9. Statistical Theory for Linear Models / 10. The Vector Geometry of Linear Models / III LINEAR-MODEL
DIAGNOSTICS / 11. Unusual and Inuential Data / 12 Non-Normality, Nonconstant Variance, Nonlinearity / 13. Collinearity and Its Purported
Remedies / IV GENERALIZED LINEAR MODELS / 14. Logit and Probit Models / 15. Generalized Linear Models / V EXTENDING LINEAR AND
GENERALIZED LINEAR MODELS / 16 Time-Series Regression and Generalized Least-Squares / 17. Nonlinear Regression / 18. Nonparametric
Regression / 19. Robust Regression / 20. Missing Data in Regression Models / 21. Bootstrapping Regression Models / 22. Model Selection,
Averaging, and Validation / VI MIXED-EFFECTS MODELS / 23. Linear Mixed-Effects Models / 24. Generalized Linear and Nonlinear Mixed-Effects
Models
HARDCOVER ISBN: 978-1-4522-0566-3 • ©2016 • 816 PAGES • •
online
resources
USING MPLUS FOR STRUCTURAL EQUATION MODELING: A Researcher's Guide
SECOND EDITION
E. Kevin Kelloway, Saint Mary's University, Canada
Ideal for researchers and graduate students in the social sciences who require knowledge of structural equation
modeling techniques to answer substantive research questions, Using Mplus for Structural Equation Modeling
provides a reader-friendly introduction to the major types of structural equation models implemented in the Mplus
framework. This practical book, which updates author E. Kevin Kelloway's 1998 book Using LISREL for Structural
Equation Modeling, retains the successful five-step process employed in the earlier book, with a thorough update for
use in the Mplus environment.
CONTENTS 1: Introduction / 2: Structural Equation Models: Theory and Development / 3: Assessing Model Fit / 4: Using MPlus / 5: Confirmatory
Factor Analysis / 6: Observed Variable path Analysis / 7: Latent Variable Path Analysis / 8: Longitudinal Analysis / 9: Multilevel Analysis
PAPERBACK ISBN: 978-1-4522-9147-5 • ©2015 • 248 PAGES • •
online
resources
BEST SELLER
PROPENSITY SCORE ANALYSIS: Statistical Methods and Applications SECOND EDITION
Shenyang Guo, Washington University in St. Louis • Mark W. Fraser, University of North Carolina at
Chapel Hill
Propensity Score Analysis provides readers with a systematic review of the origins, history, and statistical foundations
of PSA and illustrates 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.
CONTENTS 1: Introduction / 2: Counterfactual Framework and Assumptions / 3: Conventional Methods for Data Balancing / 4: Sample Selection
and Related Models / 5: Propensity Score Matching and Related Models / 6: Propensity Score Subclassification / Chapter 7: Propensity Score
Weighting / 8: Matching Estimators / 9: Propensity Score Analysis with Nonparametric Regression / 10: Propensity Score Analysis of Categorical or
Continuous Treatments / 11: Selection Bias and Sensitivity Analysis / 12: Concluding Remarks
HARDCOVER ISBN: 978-1-4522-3500-4 • ©2015 • 448 PAGES • •
online
resources
