Diverse developments in ability and motivation research, and in the derivations of new methodological techniques have often run on parallel courses. The editors of this volume felt that communication across domains could be vastly improved through intensive interaction between researchers. This interaction was realized in The Minnesota Symposium on Learning and Individual Differences, which directly addressed ability, motivation and methodology concerns. This book, compiled as a result of the Symposium, unites theoretical and empirical advances in learning and individual differences.
The resulting volume, divided in five parts, encompasses not only prepared papers that were presented at the symposium, but compiled and edited transcriptions of the spontaneous discussions that took place at the symposium.
"Part I" provides an orientation to the treatment of learning and individual differences from three major perspectives: experimental psychology, motivational psychology, and differential/ methodological psychology. "Part II" continues and expands the discussion of quantitative methodology and applications to learning and individual differences. "Part III" is devoted primarily to developments in the cognitive ability domain, while "Part IV" addresses the impact of non-cognitive, personal constructs on learning and performance. The volume concludes with "Part V" which contains chapters from the closing session of the conference.

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.
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 sciences that 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 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.
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 sciences that use this technique in their research. A basic knowledge of factor analysis is required and a working knowledge of linear algebra is helpful.