You are here

The SAGE Handbook of Quantitative Methods in Psychology

The SAGE Handbook of Quantitative Methods in Psychology

Edited by:

August 2009 | 800 pages | SAGE Publications Ltd

Quantitative Psychology is arguably one of the oldest disciplines within the field of psychology and nearly all psychologists are exposed to quantitative psychology in some form. While textbooks in statistics, research methods, and psychological measurement exist, none offer a unified treatment of quantitative psychology. The SAGE Handbook of Quantitative Methods in Psychology does just that. Each chapter covers a methodological topic with equal attention paid to established theory and the challenges facing methodologists as they address new research questions using that particular methodology. The reader will come away from each chapter with a greater understanding of the methodology being addressed as well as an understanding of the directions for future developments within that methodological area.

Drawing on a global scholarship the Handbook is divided into seven parts:

Part I: Measurement Theory: Begins with a chapter on classical test theory, followed by the common factor analysis model as a model for psychological measurement. The models for continuous latent variables in item response theory are covered next, followed by a chapter on discrete latent variable models as represented in latent class analysis.

Part II: Structural equation models: Addresses topics in general structural equation modeling, modeling mean structures, multiple-group models, nonlinear structural equation models, mixture models, and multilevel structural equation models.

Part III: Longitudinal models: Covers the analysis of longitudinal data via mixed modeling, repeated measures ANOVA, growth modeling, time series analysis, and event history analysis.

Part IV: Data analysis: Includes chapters on regression models, categorical data analysis, multilevel or hierarchical models, resampling methods, robust data analysis, meta-analysis, Bayesian data analysis, and cluster analysis.

Part V: Design and inference: Addresses issues in the inference of causal relations from experimental and non-experimental research, along with the design of true experiments and quasi-experiments, and the problem of missing data due to various influences such as attrition or non-compliance.

Part VI: Scaling methods: Covers metric and non-metric scaling methods as developed in multidimensional scaling, followed by consideration of the scaling of discrete measures as found in dual scaling and correspondence analysis. Models for preference data such as those found in random utility theory are covered next.

Part VII: Specialized methods: Covers specific topics including the analysis of social network data, the analysis of neuro-imaging data, and functional data analysis.

This volume is an excellent reference and resource for advanced students, academics, and professionals studying or using quantitative psychological methods in their research.

Michael Sobel
Causal Inference in Randomized and Non-randomized Studies
The Definition, Identification and Estimation of Causal Parameters

Roger Kirk
Experimental Design
Charles Reichardt
Quasi-Experimental Design
Paul Allison
Missing Data
James Algina and Randall D Penfield
Classical Test Theory
Robert C MacCallum
Factor Analysis
David Thissen and Lynne Steinberg
Item Response Theory
Michael Edwards and Maria Orlando Edelen
Special Topics in Item Response Theory
David Rindskopf
Latent Class Analysis
Yoshio Takane et al
Multidimensional Scaling
Heungsun Hwang et al
Correspondence Analysis, Multiple Correspondence Analysis and Recent Developments
Albert Maydeu-Olivares and Ulf B[um]ockenholt
Modeling Preference Data
Razia Azen and David Budescu
Applications of Multiple Regression in Psychological Research
Carolyn Anderson
Categorical Data Analysis with a Psychometric Twist
Jee-Seon Kim
Multilevel Analysis
An Overview and Some Contemporary Issues

William H Beasley and Joseph L Rodgers
Resampling Methods
Rand R Wilcox
Robust Data Analysis
Andy Field
Herbert Hoijtink
Bayesian Data Analysis
Lawrence Hubert et al
Cluster Analysis
A Toolbox for MATLAB

Robert Cudeck
General SEM
Melanie Wall
Maximum Likelihood And Bayesian Estimation For Nonlinear Structural Equation Models
Conor Dolan
Structural Equation Mixture Modeling
David Kaplan et al
Multilevel Latent Variable Modeling
Current Research and Recent Developments

Suzanne Graham, Judy Singer and John Willett
Modeling Individual Change over Time
Emilio Ferrer and Guangjian Zhang
Time Series Models for Examining Psychological Processes
Applications and New Developments

Jeroen Vermunt
Event History Analysis
Josep Marco-Pallarés et al
Neuroimaging Analysis (I)

Estela Camara et al
Neuroimaging Analysis (II)
Magnetic Resonance Imaging

James O Ramsay
Functional Data Analysis

Sample Materials & Chapters

Chapter One

Chapter Two

Select a Purchasing Option

Rent or Buy eBook
ISBN: 9781446206676

ISBN: 9781412930918

This title is also available on SAGE Knowledge, the ultimate social sciences online library. If your library doesn’t have access, ask your librarian to start a trial.

This title is also available on SAGE Research Methods, the ultimate digital methods library. If your library doesn’t have access, ask your librarian to start a trial.