The SAGE Handbook of Quantitative Methods in Psychology
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Description
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.
Contents
PART ONE: DESIGN AND INFERENCE
- Causal Inference in Randomized and Non-randomized Studies
- The Definition, Identification and Estimation of Causal Parameters
- Experimental Design
- Quasi-Experimental Design
- Missing Data
PART TWO: MEASUREMENT THEORY
- Classical Test Theory
- Factor Analysis
- Item Response Theory
- Special Topics in Item Response Theory
- Latent Class Analysis
PART THREE: SCALING
- Multidimensional Scaling
- Correspondence Analysis, Multiple Correspondence Analysis and Recent Developments
- Modeling Preference Data
PART FOUR: DATA ANALYSIS
- Applications of Multiple Regression in Psychological Research
- Categorical Data Analysis with a Psychometric Twist
- Multilevel Analysis
- An Overview and Some Contemporary Issues
- Resampling Methods
- Robust Data Analysis
- Meta-Analysis
- Bayesian Data Analysis
- Cluster Analysis
- A Toolbox for MATLAB
PART FIVE: STRUCTURAL EQUATION MODELS
- General SEM
- Maximum Likelihood And Bayesian Estimation For Nonlinear Structural Equation Models
- Structural Equation Mixture Modeling
- Multilevel Latent Variable Modeling
- Current Research and Recent Developments
PART SIX: LONGITUDINAL MODELS
- Modeling Individual Change over Time
- Time Series Models for Examining Psychological Processes
- Applications and New Developments
- Event History Analysis
PART SEVEN: SPECIALIZED METHODS
- Neuroimaging Analysis (I)
- Electroencephalography
- Neuroimaging Analysis (II)
- Magnetic Resonance Imaging
- Functional Data Analysis
Additional materials
Description
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.
Contents
PART ONE: DESIGN AND INFERENCE
- Causal Inference in Randomized and Non-randomized Studies
- The Definition, Identification and Estimation of Causal Parameters
- Experimental Design
- Quasi-Experimental Design
- Missing Data
PART TWO: MEASUREMENT THEORY
- Classical Test Theory
- Factor Analysis
- Item Response Theory
- Special Topics in Item Response Theory
- Latent Class Analysis
PART THREE: SCALING
- Multidimensional Scaling
- Correspondence Analysis, Multiple Correspondence Analysis and Recent Developments
- Modeling Preference Data
PART FOUR: DATA ANALYSIS
- Applications of Multiple Regression in Psychological Research
- Categorical Data Analysis with a Psychometric Twist
- Multilevel Analysis
- An Overview and Some Contemporary Issues
- Resampling Methods
- Robust Data Analysis
- Meta-Analysis
- Bayesian Data Analysis
- Cluster Analysis
- A Toolbox for MATLAB
PART FIVE: STRUCTURAL EQUATION MODELS
- General SEM
- Maximum Likelihood And Bayesian Estimation For Nonlinear Structural Equation Models
- Structural Equation Mixture Modeling
- Multilevel Latent Variable Modeling
- Current Research and Recent Developments
PART SIX: LONGITUDINAL MODELS
- Modeling Individual Change over Time
- Time Series Models for Examining Psychological Processes
- Applications and New Developments
- Event History Analysis
PART SEVEN: SPECIALIZED METHODS
- Neuroimaging Analysis (I)
- Electroencephalography
- Neuroimaging Analysis (II)
- Magnetic Resonance Imaging
- Functional Data Analysis
Additional materials
The SAGE Handbook of Quantitative Methods in Psychology
August 2009 | 800 pages | Sage UK
| Format | Published Date | ISBN | Price |
|---|---|---|---|
| Hardcover | 31/03/2026 | 9781412930918 | $220.00 |
| 180 Day Ebook | 07/05/2024 | 9781446206676 | $91.00 |
| Lifetime | 07/05/2024 | 9781446206676 | $132.00 |
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.
Table Of Contents:
- PART ONE: DESIGN AND INFERENCE
- Causal Inference in Randomized and Non-randomized Studies
- The Definition, Identification and Estimation of Causal Parameters
- Experimental Design
- Quasi-Experimental Design
- Missing Data
- PART TWO: MEASUREMENT THEORY
- Classical Test Theory
- Factor Analysis
- Item Response Theory
- Special Topics in Item Response Theory
- Latent Class Analysis
- PART THREE: SCALING
- Multidimensional Scaling
- Correspondence Analysis, Multiple Correspondence Analysis and Recent Developments
- Modeling Preference Data
- PART FOUR: DATA ANALYSIS
- Applications of Multiple Regression in Psychological Research
- Categorical Data Analysis with a Psychometric Twist
- Multilevel Analysis
- An Overview and Some Contemporary Issues
- Resampling Methods
- Robust Data Analysis
- Meta-Analysis
- Bayesian Data Analysis
- Cluster Analysis
- A Toolbox for MATLAB
- PART FIVE: STRUCTURAL EQUATION MODELS
- General SEM
- Maximum Likelihood And Bayesian Estimation For Nonlinear Structural Equation Models
- Structural Equation Mixture Modeling
- Multilevel Latent Variable Modeling
- Current Research and Recent Developments
- PART SIX: LONGITUDINAL MODELS
- Modeling Individual Change over Time
- Time Series Models for Examining Psychological Processes
- Applications and New Developments
- Event History Analysis
- PART SEVEN: SPECIALIZED METHODS
- Neuroimaging Analysis (I)
- Electroencephalography
- Neuroimaging Analysis (II)
- Magnetic Resonance Imaging
- Functional Data Analysis