Description
Factor analysis is a statistical tool that is used to investigate latent variables, which are the fundamental traits and concepts in the study of individual differences. The conference that was held to mark the centennial of the publication of Charles Spearman's seminal 1904 article introduced the major elements of this invaluable statistical tool. This book evolved from that conference and provides a retrospective look at major issues and developments as well as a prospective view of future directions in factor analysis and related methods. The book features an outstanding collection of contributors and will appeal to graduate students and researchers in the behavioral, social, health, and biological sciences who use this technique in their research. A basic knowledge of factor analysis is required and a working knowledge of linear algebra is helpful.
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 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. Thisbook 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. Featuring an outstanding collection of contributors, this volume offers unique insights on factor analysis and its related methods. Several chapters have a clear historical perspective, while others present new ideas along with historical summaries. In addition, the book reviews some of the extensions of factor analysis to such techniques as latent growth curve models, models for categorical data, and structural equation models. Factor Analysis at 100 will appeal to graduate students and researchers in the behavioral, social, health, and biological scienceswho use this technique in their research. A basic knowledge of factor analysis is required and a working knowledge of linear algebra is helpful.
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