Factor Analysis at 100
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€72.99
advanced psychometric theory
behavioral data analysis
categorical data models
Category=JHBC
Category=JN
Category=PBT
Chapel Hill
Common Factor Model
Common Factor Scores
Confirmatory Factor Analysis
correlation
Curve Model
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Factor Analysis Model
Factor Loadings
Factor Scores
Factor Vector
factorial
Growth Curve Models
historical evolution of factor analysis
invariance
Invariant Factor
latent
Latent Curve Model
latent variable modeling
Latent Variable Models
Latent Variables
linear algebra applications
Linear Model
loadings
Longitudinal Factor Model
longitudinal measurement models
manifest
Manifest Variables
matrix
model
Nonlinear Factor Analysis
NORTH CAROLINA
Pe Rc
Population Correlation Matrix
Posterior Distribution
scores
Spearman's Theory
Spearman’s Theory
Unique Factor Variances
V2 V3 V4 V5 V6
variable
Product details
- ISBN 9780805862126
- Weight: 740g
- Dimensions: 152 x 229mm
- Publication Date: 06 Mar 2007
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Paperback
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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. This 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 who use this technique in their research. A basic knowledge of factor analysis is required and a working knowledge of linear algebra is helpful.
Robert Cudeck and Robert C. MacCallum
