Overview

A firm knowledge of factor analysis is key to understanding much published research in the social and behavioral sciences. This volume provides a solid foundation in exploratory factor analysis (EFA), which along with confirmatory factor analysis, represents one of the two major strands in this field. The book lays out the mathematical foundations of EFA; explores the range of methods for extracting the initial factor structure; explains factor rotation; and outlines the methods for determining the number of factors to retain in EFA. The concluding chapter addresses a number of other key issues in EFA, such as determining the appropriate sample size for a given research problem, and the handling of missing data. It also offers brief introductions to exploratory structural equation modeling, and multilevel models for EFA. Example computer code, and the annotated output for all of the examples included in the text are available on an accompanying website.

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9781544339887

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Reviews

Finch provides a well-written and well-organized introduction to the conceptual and quantitative topics of exploratory and confirmatory factor analysis within a single, concise text. -- (05/01/2019) This text is a perfect resource for individuals seeking guidance on applied factor analysis, covering the fundamentals as well as introductions to more advanced aspects of factor analytic techniques. -- (05/01/2019) This is a thorough and readable introduction to exploratory factor analysis --Michael D. Biderman Review (05/01/2019)

This is a thorough and readable introduction to exploratory factor analysis --Michael D. Biderman Review (05/01/2019) Finch provides a well-written and well-organized introduction to the conceptual and quantitative topics of exploratory and confirmatory factor analysis within a single, concise text. -- (05/01/2019) This text is a perfect resource for individuals seeking guidance on applied factor analysis, covering the fundamentals as well as introductions to more advanced aspects of factor analytic techniques. -- (05/01/2019)

Author Information

W. Holmes Finch (Ph.D. South Carolina) is the George and Frances Ball Distinguished Professor of Educational Psychology in the Department of Educational Psychology at Ball State University. Prior to coming to Ball State, he worked as a consultant in the Statistics Department at the University of South Carolina for 12 years advising faculty and graduate students on the appropriate statistical methods for their research. Dr. Finch teaches courses in statistical and research methodology as well as psychometrics and educational measurement. His research interests involve issues in psychometrics, including dimensionality assessment, differential item functioning, generalizability theory and unfolding models. In addition, he pursues research in multivariate statistics, particularly those involving nonparametric techniques. He is the co-author of Multilevel Modeling Using R (with Holden, J.E., & Kelley, K., CRC Press, 2014); Applied Psychometrics Using SAS (with French, B.F. & Immekus, J., Information Age, 2014); and Latent Variable Models in R (with French, B.F., Routledge, 2015).

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