Factor analysis is a powerful data reduction technique that has been widely used in the fields of psychology and education to explore personality, psychopathology, human abilities, and other facets of the human condition. More recently it has been applied to variables of interest in other fields of endeavor, including medicine, marketing, and geology. Factor analysis was designed to help researchers working with complex correlational datasets to identify a simpler set of latent, explanatory dimensions (factors) underlying a pattern of inter-correlations. Once identified, these factors were expected to improve our measurement strategies as well as our understanding of basic theoretical concepts. Despite this promise, the practical use of factor analysis has been limited to date not only by methodological disputes based on statistical grounds, but also by a pervasive belief that factor analysis is inherently mysterious and requires both psychometric intuition and the convergence of evidence from many statistical and analytical sources to correctly identify factor structures among a given group of variables. Not surprisingly, there has been little agreement over the factor structure of most questionnaires and scales developed to date using this approach. Our book addresses these roadblocks as the reader is walked through the practical steps used in conducting a factor analysis using simple worked examples. We provide an elegant yet logical approach to the practical use of factor analysis based on the work of Raymond B. Cattell that eschews the conventional wisdom, and is alternately based on the principal of factor replicability. Finally, we direct the interested reader to a new website that provides a user-friendly research tool (FACTOREP) that will help them identify replicable and therefore scientifically illuminating interpretations of their data.

Factor analysis is a powerful data reduction technique that has been widely used in the fields of psychology and education to explore personality, psychopathology, human abilities, and other facets of the human condition. More recently it has been applied to variables of interest in other fields of endeavor, including medicine, marketing, and geology. Factor analysis was designed to help researchers working with complex correlational datasets to identify a simpler set of latent, explanatory dimensions (factors) underlying a pattern of inter-correlations. Once identified, these factors were expected to improve our measurement strategies as well as our understanding of basic theoretical concepts. Despite this promise, the practical use of factor analysis has been limited to date not only by methodological disputes based on statistical grounds, but also by a pervasive belief that factor analysis is inherently mysterious and requires both psychometric intuition and the convergence of evidence from many statistical and analytical sources to correctly identify factor structures among a given group of variables. Not surprisingly, there has been little agreement over the factor structure of most questionnaires and scales developed to date using this approach. Our book addresses these roadblocks as the reader is walked through the practical steps used in conducting a factor analysis using simple worked examples. We provide an elegant yet logical approach to the practical use of factor analysis based on the work of Raymond B. Cattell that eschews the conventional wisdom, and is alternately based on the principal of factor replicability. Finally, we direct the interested reader to a new website that provides a user-friendly research tool (FACTOREP) that will help them identify replicable and therefore scientifically illuminating interpretations of their data.