Statistical Modelling Methods
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Go to College Publishing WebsiteDescription
Looking to extend your understanding of statistical methods and data analysis skills? This text shows you how to apply advanced statistical models to data across the behavioural and social sciences.
This approachable book explains how to develop, estimate, and test statistical models with demonstrations using R. It gives you a strong grounding in key techniques such as multiple regression, multilevel modelling, factor analysis, and structural equation modelling, and helps you move beyond rigid statistical tests to confidently choose an appropriate model for reaching strong conclusions from your data.
This second edition includes:
- Two new chapters on logistic regression and missing data.
- Updated and improved content on causal models, mediation and modelling change over time with longitudinal data.
- Examples of real-world research from psychology, sociology, business and management, education, and more.
Accompanied by datasets and R code for you to practice techniques at your own pace, this book is an accessible guide for anyone looking to advance their statistical understanding.
Contents
Chapter 1: Foundations of Statistical Modelling Demonstrated with Simple Regression
Chapter 1: Foundations of Statistical Modelling Demonstrated with Simple Regression
Chapter 2: Multiple Regression with Continuous Regressors
Chapter 2: Multiple Regression with Continuous Regressors
Chapter 3: Multiple Regression with Categorical Regressors
Chapter 3: Multiple Regression with Categorical Regressors
Chapter 4: Interactions in Multiple Regression: Models for Moderation
Chapter 4: Interactions in Multiple Regression: Models for Moderation
Chapter 5: Using Regression to Model Causal Effects and Mediation
Chapter 5: Using Regression to Model Causal Effects and Mediation
Chapter 6: Logistic regression for categorical outcome variables
Chapter 6: Logistic regression for categorical outcome variables
Chapter 7: Introduction to Multilevel Modelling
Chapter 7: Introduction to Multilevel Modelling
Chapter 8: Exploratory Factor Analysis
Chapter 8: Exploratory Factor Analysis
Chapter 9: Structural Equation Modelling I: Confirmatory Factor Analysis
Chapter 9: Structural Equation Modelling I: Confirmatory Factor Analysis
Chapter 10: Structural Equation Modelling II: Structural Regression Models
Chapter 10: Structural Equation Modelling II: Structural Regression Models
Chapter 11: Estimating Models with Missing Data
Chapter 11: Estimating Models with Missing Data
Chapter 12: Modelling Longitudinal Data
Chapter 12: Modelling Longitudinal Data
Description
Looking to extend your understanding of statistical methods and data analysis skills? This text shows you how to apply advanced statistical models to data across the behavioural and social sciences.
This approachable book explains how to develop, estimate, and test statistical models with demonstrations using R. It gives you a strong grounding in key techniques such as multiple regression, multilevel modelling, factor analysis, and structural equation modelling, and helps you move beyond rigid statistical tests to confidently choose an appropriate model for reaching strong conclusions from your data.
This second edition includes:
- Two new chapters on logistic regression and missing data.
- Updated and improved content on causal models, mediation and modelling change over time with longitudinal data.
- Examples of real-world research from psychology, sociology, business and management, education, and more.
Accompanied by datasets and R code for you to practice techniques at your own pace, this book is an accessible guide for anyone looking to advance their statistical understanding.
Contents
Chapter 1: Foundations of Statistical Modelling Demonstrated with Simple Regression
Chapter 1: Foundations of Statistical Modelling Demonstrated with Simple Regression
Chapter 2: Multiple Regression with Continuous Regressors
Chapter 2: Multiple Regression with Continuous Regressors
Chapter 3: Multiple Regression with Categorical Regressors
Chapter 3: Multiple Regression with Categorical Regressors
Chapter 4: Interactions in Multiple Regression: Models for Moderation
Chapter 4: Interactions in Multiple Regression: Models for Moderation
Chapter 5: Using Regression to Model Causal Effects and Mediation
Chapter 5: Using Regression to Model Causal Effects and Mediation
Chapter 6: Logistic regression for categorical outcome variables
Chapter 6: Logistic regression for categorical outcome variables
Chapter 7: Introduction to Multilevel Modelling
Chapter 7: Introduction to Multilevel Modelling
Chapter 8: Exploratory Factor Analysis
Chapter 8: Exploratory Factor Analysis
Chapter 9: Structural Equation Modelling I: Confirmatory Factor Analysis
Chapter 9: Structural Equation Modelling I: Confirmatory Factor Analysis
Chapter 10: Structural Equation Modelling II: Structural Regression Models
Chapter 10: Structural Equation Modelling II: Structural Regression Models
Chapter 11: Estimating Models with Missing Data
Chapter 11: Estimating Models with Missing Data
Chapter 12: Modelling Longitudinal Data
Chapter 12: Modelling Longitudinal Data
Statistical Modelling Methods
A Guide for the Social and Behavioural Sciences
April 2026 | 328 pages | Sage UK
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Looking to extend your understanding of statistical methods and data analysis skills? This text shows you how to apply advanced statistical models to data across the behavioural and social sciences.
This approachable book explains how to develop, estimate, and test statistical models with demonstrations using R. It gives you a strong grounding in key techniques such as multiple regression, multilevel modelling, factor analysis, and structural equation modelling, and helps you move beyond rigid statistical tests to confidently choose an appropriate model for reaching strong conclusions from your data.
This second edition includes:
- Two new chapters on logistic regression and missing data.
- Updated and improved content on causal models, mediation and modelling change over time with longitudinal data.
- Examples of real-world research from psychology, sociology, business and management, education, and more.
Accompanied by datasets and R code for you to practice techniques at your own pace, this book is an accessible guide for anyone looking to advance their statistical understanding.
Table Of Contents:
- Chapter 1: Foundations of Statistical Modelling Demonstrated with Simple Regression
- Chapter 2: Multiple Regression with Continuous Regressors
- Chapter 3: Multiple Regression with Categorical Regressors
- Chapter 4: Interactions in Multiple Regression: Models for Moderation
- Chapter 5: Using Regression to Model Causal Effects and Mediation
- Chapter 6: Logistic regression for categorical outcome variables
- Chapter 7: Introduction to Multilevel Modelling
- Chapter 8: Exploratory Factor Analysis
- Chapter 9: Structural Equation Modelling I: Confirmatory Factor Analysis
- Chapter 10: Structural Equation Modelling II: Structural Regression Models
- Chapter 11: Estimating Models with Missing Data
- Chapter 12: Modelling Longitudinal Data