Statistical Modelling Methods
A Guide for the Social and Behavioural Sciences
2. Auflage
Erschienen am 25. April 2026
360 Seiten
978-1-0362-3851-3 (ISBN)
Beschreibung
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.
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.
Weitere Details
Auflage
Second Edition
Sprache
Englisch
Verlagsort
London
Großbritannien
Verlagsgruppe
SAGE Publishing (Digital)
Zielgruppe
Für höhere Schule und Studium
Dateigröße
5,24 MB
ISBN-13
978-1-0362-3851-3 (9781036238513)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
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04/2026
2. Auflage
SAGE Publications Ltd
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Buch
04/2026
2. Auflage
SAGE Publications Ltd
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Versand in 3-4 Wochen
Person
David B. Flora is a Professor in the Department of Psychology at York University in Toronto, Canada. Dr. Flora has also served as Coordinator of the Quantitative Methods Area in York's graduate program in psychology as Coordinator of York's Statistical Consulting Service. As a quantitative psychologist, Dr. Flora is a coauthor on numerous journal articles focused on quantitative methodology itself as well as on a wide range of articles in which advanced quantitative methods are applied to substantive research topics in psychology. Dr. Flora earned his PhD from the Quantitative Psychology program at the University of North Carolina at Chapel Hill. Although he is now a Canadian, Dr. Flora remains a Tar Heel born and a Tar Heel bred.
Inhalt
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
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
