Analysis of Multivariate Social Science Data

Statistical Machine Learning Methods

Irini Moustaki, Fiona Steele, Yunxiao Chen, David Bartholomew

• Publisher: Chapman and Hall/CRC
• Year: 2026
• eBook ISBN: 9781040631300
• Topic: Mathematics
• Index: Mathematics

Drawing on the authors' varied experiences researching and teaching in the field, Analysis of Multivariate Social Science Data: Statistical Machine Learning Methods, Third Edition enables a basic understanding of how to use key multivariate methods in the social sciences. With minimal mathematical and statistical knowledge required, this third edition expands its topics to include graphical modelling, models for longitudinal data, structural equation models for categorical variables, and latent class analysis for ordinal, nominal, and continuous variables. It also connects the topics to terminology and principles of machine learning, intended to help readers grasp the links between methods of multivariate analysis and advancements in the field of data science.

After describing methods for the summarisation of data in the first part of the book, the authors consider regression analysis. This chapter provides a link between the two halves of the book, signalling the move from descriptive to inferential methods. The remainder of the text deals with model-based methods that primarily make inferences about processes that generate data.

Relying heavily on numerical examples from a range of disciplines, the authors provide insight into the purpose and working of the methods as well as the interpretation of results from analyses. Many of the same examples are used throughout to illustrate connections between the methods. In most chapters, the authors present suggestions for further work that go beyond conventional practice, encouraging readers to explore new ground in social science research.

Features

• Contains new chapters on undirected graphical modelling and models for longitudinal data, as well as new material such as K-means, cross-validation, structural equation models for categorical variables, latent class analysis for categorical, nominal and continuous variables, and treatment of missing data.
• Connects topics with terminology and principles of machine learning.
• Presents numerous examples of real-world applications, including voting preferences, social attitudes, educational assessment, recidivism, and health.
• Covers methods that summarise, describe, and explore multivariate datasets, including longitudinal data.
• Establishes a unified approach to latent variable modelling by providing detailed coverage of methods such as item response theory, factor analysis for continuous and categorical data, and models for categorical latent variables.
• Covers models for hierarchical and longitudinal data and their connections to latent variable models.
• Offers a full version of the data sets in the text or the book's website, with software code for implementing the analyses on the website.

The book offers a balanced and accessible resource for students and researchers with limited mathematical and statistical training. It serves as a practical resource for courses in multivariate analysis and as a guide for applying these techniques in applied research.

Table of contents

• Cover Page
• Half-Title Page
• Series Page
• Title Page
• Copyright Page
• Contents
• Preface
• About the Authors
• 1 Setting the Scene
• 2 Cluster Analysis
• 3 Multidimensional Scaling
• 4 Correspondence Analysis
• 5 Principal Components Analysis
• 6 Regression Analysis
• 7 Factor Analysis
• 8 Factor Analysis for Binary Data
• 9 Factor Analysis for Ordered Categorical Variables
• 10 Models with Categorical Latent Variables
• 11 Confirmatory Factor Analysis and Structural Equation Models
• 12 Undirected Graphical Models
• 13 Multilevel Modelling
• 14 Longitudinal Data Analysis
• References
• Author Index
• Subject Index