Latent variable models comprise an important branch of multivariate analysis. In particular, they are used to create a framework for reducing data from large scale statistical enquiries to manageable proportions. Factor analysis, a key latent variable model, has been effectively applied to problems in sociology, geology, chemistry, and psychology. This book provides a latent-variable framework for psychometricians and statisticians in their specification and use of suitable research models. Several chapters explore fields where the data are categorical and the latent variables are metrical.

Latent Variable Models and Factor Analysis (Statistical Monograph)

Science & Math

David J. Bartholomew

Mathematics

Hodder Arnold

Social probability;: An inaugural lecture

London School of Economics and Political Science

Latent Variable Models and Factor Analysis: A Unified Approach

Irini Moustaki

Martin Knott

wiley

Analysis of Multivariate Social Science Data (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

Drawing on the authors’ varied experiences working and teaching in the field, Analysis of Multivariate Social Science Data, Second Editionenables a basic understanding of how to use key multivariate methods in the social sciences. With updates in every chapter, this edition expands its topics to include regression analysis, confirmatory factor analysis, structural equation models, and multilevel models. 
After emphasizing the summarization of data in the first several chapters, the authors focus on regression analysis. This chapter provides a link between the two halves of the book, signaling the move from descriptive to inferential methods and from interdependence to dependence. The remainder of the text deals with model-based methods that primarily make inferences about processes that generate data. 
Relying heavily on numerical examples, the authors provide insight into the purpose and working of the methods as well as the interpretation of data. 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 exercises, encouraging readers to explore new ground in social science research. 
Requiring minimal mathematical and statistical knowledge, this book shows how various multivariate methods reveal different aspects of data and thus help answer substantive research questions.

Fiona Steele

Jane Galbraith

Chapman and Hall/CRC

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Books by David J. Bartholomew