Analysis of Multivariate Social Science Data: Statistical Machine Learning Methods [Paperback]

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 Structural Equation Models?12 Undirected Graphical Models?13 Multilevel Modelling?14 Longitudinal Data Analysis

Third Edition enables a basic understanding of how to use key multivariate methods in the social sciences. Authors focus on regression analysis. Provides link between two halves of the book, the move from descriptive to inferential methods, from interdependence to dependence.

Irini Moustaki is a professor of Statistics in the Department of Statistics at the London School of Economics and Political Science. She received her bachelors degree in Statistics and Computer Science from the Athens University of Economics and Business and her MSc and PhD in Statistics from the LSE. Her research interests are in latent variable models and structural equation models. Her methodological work includes treating missing data, longitudinal data, outlier detection, goodness-of-fit tests, and advanced estimation methods. Furthermore, she has made methodological and applied contributions to comparative cross-national studies and epidemiological studies on rare diseases. Irini received an honorary doctorate from the Faculty of Social Sciences, Uppsala University, in 2014. She is a Fellow of the British Academy. She was the Executive Editor of the journal Psychometrika from November 2014 to December 2018 and the President of the PsychometlK