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The SAGE Handbook of Quantitative Methods in Psychology
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The SAGE Handbook of Quantitative Methods in Psychology

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July 2009 | 800 pages | SAGE Publications Ltd
`I often... wonder to myself whether the field needs another book, handbook, or encyclopedia on this topic. In this case I think that the answer is truly yes. The handbook is well focused on important issues in the field, and the chapters are written by recognized authorities in their fields. The book should appeal to anyone who wants an understanding of important topics that frequently go uncovered in graduate education in psychology' - David C Howell, Professor Emeritus, University of Vermont

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 One: 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 Two: 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 Three: 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 Four: 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 Five: Structural Equation Models: addresses topics in general structural equation modeling, nonlinear structural equation models, mixture models, and multilevel structural equation models.

Part Six: Longitudinal Models: covers the analysis of longitudinal data via mixed modeling, time series analysis and event history analysis.

Part Seven: Specialized Models: covers specific topics including the analysis of neuro-imaging data and functional data-analysis.

 
PART ONE: DESIGN AND INFERENCE
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
 
PART TWO: MEASUREMENT THEORY
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
 
PART THREE: SCALING
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
 
PART FOUR: DATA ANALYSIS
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
Meta-Analysis
Herbert Hoijtink
Bayesian Data Analysis
Lawrence Hubert et al
Cluster Analysis
A Toolbox for MATLAB  
 
PART FIVE: STRUCTURAL EQUATION MODELS
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  
 
PART SIX: LONGITUDINAL MODELS
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
 
PART SEVEN: SPECIALIZED METHODS
Josep Marco-Pallarés et al
Neuroimaging Analysis (I)
Electroencephalography  
Estela Camara et al
Neuroimaging Analysis (II)
Magnetic Resonance Imaging  
James O Ramsay
Functional Data Analysis

Sample Materials & Chapters

Chapter One

Chapter Two


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ISBN: 9781412930918
£120.00

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