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Intermediate / Advanced Statistics
APPLIED MULTIVARIATE RESEARCH: Design and Interpretation THIRD EDITION
Lawrence S. Meyers, California State University, Sacramento • Glenn Gamst, University of La Verne • A.J.
Guarino, MGH Institute of Health Professions
Applied Multivariate Research: Design and Interpretation, Third Edition provides full coverage of the wide range of
multivariate topics that graduate students across the social and behavioral sciences encounter, using a conceptual,
non-mathematical approach. Addressing correlation, multiple regression, exploratory factor analysis, MANOVA, path
analysis, and structural equation modeling, it is geared toward the needs, level of sophistication, and interest in
multivariate methodology that serves students in applied programs in the social and behavioral sciences. Readers are
encouraged to focus on design and interpretation rather than the intricacies of specifi c computations.
CONTENTS PART I: AN INTRODUCTION TO MULTIVARIATE DESIGN / 1: An Introduction to Multivariate Design / 2: Some Fundamental Research
Design Concepts / 3A: Data Screening / 3B: Data Screening Using IBM SPSS / PART II: BASIC AND ADVANCED REGRESSION ANALYSIS / 4A: Bivariate
Correlation and Simple Linear Regression / 4B: Bivariate Correlation and Simple Linear Regression Using IBM SPSS / 5A: Multiple Regression
Analysis / 5B: Multiple Regression Analysis Using IBM SPSS / 6A: Beyond Statistical Regression / 6B: Beyond Statistical Regression Using IBM
SPSS / 7A: Canonical Correlation Analysis / 7B: Canonical Correlation Analysis Using IBM SPSS / 8A: Multilevel Modeling / 8B: Multilevel Modeling
Using IBM SPSS / 9A: Binary and Multinomial Logistic Regression and ROC Analysis / 9B: Binary and Multinomial Logistic Regression and ROC
Analysis Using IBM SPSS / PART III: STRUCTURAL RELATIONSHIPS OF MEASURED AND LATENT VARIABLES / 10A: Principal Components Analysis
and Exploratory Factor Analysis / 10B: Principal Components Analysis and Exploratory Factor Analysis Using IBM SPSS / 11A: Confi rmatory Factor
Analysis / 11B: Confi rmatory Factor Analysis Using IBM SPSS AMOS / 12A: Path Analysis: Multiple Regression Analysis / 12B: Path Analysis: Multiple
Regression Analysis Using IBM SPSS / 13A: Path Analysis: Structural Equation Modeling / 13B: Path Analysis: Structural Equation Modeling
Using IBM SPSS AMOS / 14A: Structural Equation Modeling / 14B: Structural Equation Modeling Using IBM SPSS AMOS / 15A: Measurement
and Structural Equation Modeling Invariance: Applying a Model to Different Group / 15B: Assessing Measurement and Structural Invariance
for Confi rmatory Factor Analysis and Structural Equation Models Using IBM SPSS AMOS / PART IV: CONSOLIDATING STIMULI AND CASES / 16A:
Multidimensional Scaling / 16B: Multidimensional Scaling Using IBM SPSS / 17A: Cluster Analysis / 17B: Cluster Analysis Using IBM SPSS / PART V:
COMPARING SCORES / 18A: Between Subjects Comparisons of Means / 18B: Between Subjects ANCOVA, MANOVA, and MANCOVA Using IBM SPSS
/ 19A: Discriminant Function Analysis / 19B: Three-Group Discriminant Function Analysis Using IBM SPSS / 20A: Survival Analysis / 20B: Survival
Analysis Using IBM SPSS / Appendix A: Statistics Tables
HARDCOVER ISBN: 978-1-5063-2976-5 • NOVEMBER 2016 • 1120 PAGES • •
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PRACTICAL PROPENSITY SCORE METHODS USING R
Walter Leite, University of Florida
This book uses examples to guide students through the steps of using the R software to implement propensity score
methods, and also presents the theoretical background needed to be able to understand the methodological choices
being made at each step, and the advantages and disadvantages of competing methods.
CONTENTS 1: Overview of Propensity Score Analysis / 2: Propensity score estimation / 3: Propensity score weighting / 4: Propensity Score
Stratifi cation / 5: Propensity Score Matching / 6: Propensity score methods for multiple treatments / 7: Propensity Score Methods for Continuous
Treatment Doses / 8: Propensity score analysis with structural equation models / 9: Weighting methods for time-varying treatments / 10:
Propensity score methods with multilevel data
PAPERBACK ISBN: 978-1-4522-8888-8 • NOVEMBER 2016 • 232 PAGES • •
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REGRESSION & LINEAR MODELING: Best Practices and Modern Methods
Jason W. Osborne, Clemson University
In a conversational tone, Regression & Linear Modeling provides conceptual, user-friendly coverage of the generalized
linear model (GLM). Readers will become familiar with applications of ordinary least squares (OLS) regression, binary
and multinomial logistic regression, ordinal regression, Poisson regression, and loglinear models. The author returns
to certain themes throughout the text, such as testing assumptions, examining data quality, and, where appropriate,
nonlinear and non-additive effects modeled within different types of linear models.
CONTENTS 1: A Nerdly Manifesto / 2: Basic estimation and assumptions / 3: Simple linear models with continuous dependent variables:
Simple regression analyses / 4: Simple linear models with continuous dependent variables: Simple ANOVA analyses / 5: Simple linear models with
categorical Dependent Variables: Binary logistic regression / 6: Simple linear models with polytomous categorical Dependent Variables: Multinomial
and ordinal logistic regression / 7: Simple Curvilinear Models / 8: Multiple Independent Variables / 9: Interactions between Independent Variables:
Simple moderation / 10: Curvilinear Interactions between Independent Variables / 11: Poisson models: Low-frequency Count Data as Dependent
Variables / 12: Log-Linear Models: General Linear Models When All Your Variables Are Unordered Categorical / 13: A brief introduction to Hierarchical
Linear Modeling / 14: Missing Data in Linear Modeling / 15: Trustworthy Science: Improving Statistical Reporting / 16: Reliable Measurement
Matters / 17: Prediction in the Generalized Linear Model / 18: Modeling in Large, Complex Samples: The Importance of Using Appropriate Weights
and Design Effect Compensation
HARDCOVER ISBN: 978-1-5063-0276-8 • APRIL 2016 • 488 PAGES • •
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