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This book presents recent developments in the theory and application of latent variable models (LVMs) by some of the most prominent researchers in the field. Topics covered involve a range of LVM frameworks including item response theory, structural equation modeling, factor analysis, and latent curve modeling, as well as various non-standard data structures and innovative applications. The book is divided into two sections, although several chapters cross these content boundaries. Part one focuses on complexities which involve the adaptation of latent variables models in research problems where real-world conditions do not match conventional assumptions. Chapters in this section cover issues such as analysis of dyadic data and complex survey data, as well as analysis of categorical variables. Part two of the book focuses on drawing real-world meaning from results obtained in LVMs. In this section there are chapters examining issues involving assessment of model fit, the nature of uncertainty in parameter estimates, inferences, and the nature of latent variables and individual differences. This book appeals to researchers and graduate students interested in the theory and application of latent variable models. As such, it serves as a supplementary reading in graduate level courses on latent variable models. Prerequisites include basic knowledge of latent variable models.
Factor analysis is one of the success stories of statistics in the
social sciences. The reason for its wide appeal is that it provides
a way to investigate latent variables, the fundamental traits and
concepts in the study of individual differences. Because of its
importance, a recent conference was held to mark the centennial of
the publication of Charles Spearman's seminal 1904 article which
introduced the major elements of this invaluable statistical tool.
This new book evolved from that conference. It provides a
retrospective look at major issues and developments as well as a
prospective view of future directions in factor analysis and
related methods. In so doing, it demonstrates how and why factor
analysis is considered to be one of the methodological pillars of
behavioral research.
Factor analysis is one of the success stories of statistics in the
social sciences. The reason for its wide appeal is that it provides
a way to investigate latent variables, the fundamental traits and
concepts in the study of individual differences. Because of its
importance, a recent conference was held to mark the centennial of
the publication of Charles Spearman's seminal 1904 article which
introduced the major elements of this invaluable statistical tool.
This new book evolved from that conference. It provides a
retrospective look at major issues and developments as well as a
prospective view of future directions in factor analysis and
related methods. In so doing, it demonstrates how and why factor
analysis is considered to be one of the methodological pillars of
behavioral research.
This book presents recent developments in the theory and application of latent variable models (LVMs) by some of the most prominent researchers in the field. Topics covered involve a range of LVM frameworks including item response theory, structural equation modeling, factor analysis, and latent curve modeling, as well as various non-standard data structures and innovative applications. The book is divided into two sections, although several chapters cross these content boundaries. Part one focuses on complexities which involve the adaptation of latent variables models in research problems where real-world conditions do not match conventional assumptions. Chapters in this section cover issues such as analysis of dyadic data and complex survey data, as well as analysis of categorical variables. Part two of the book focuses on drawing real-world meaning from results obtained in LVMs. In this section there are chapters examining issues involving assessment of model fit, the nature of uncertainty in parameter estimates, inferences, and the nature of latent variables and individual differences. This book appeals to researchers and graduate students interested in the theory and application of latent variable models. As such, it serves as a supplementary reading in graduate level courses on latent variable models. Prerequisites include basic knowledge of latent variable models.
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