Abstract
Item factor analysis is unexcelled as a technique of exploration and discovery in the study of behavior. From binary-scored responses to a multiple-item test or scale, it determines the dimensionality of individual variation among the respondents and reveals attributes of the items defining each dimension. In practical test construction and scoring, it is the best guide to item selection and revision, as well as an essential preliminary step in justifying unidimensional IRT item analysis. Because hundreds of items may be analyzed jointly, the detail and generality that may be achieved exceeds that of any other procedure for exploring relationships among responses.
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© 1997 Springer-Verlag New York, Inc.
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Bock, R.D., Schilling, S. (1997). High-dimensional Full-information Item Factor Analysis. In: Berkane, M. (eds) Latent Variable Modeling and Applications to Causality. Lecture Notes in Statistics, vol 120. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1842-5_8
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DOI: https://doi.org/10.1007/978-1-4612-1842-5_8
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