Abstract
Confirmatory factor analysis (CFA) is based on the premise that observable variables are imperfect indicators of certain underlying, or latent, constructs. For example, variables used in the regression and path analytical models of Chapter 1, such as father’s education (FaEd), degree aspirations (Degre Asp), and highest held academic degree (Degree), can be thought of as imperfect indicators of the latent constructs parents’ socioeconomic status (PaSES), general academic motivation (AcMotiv), and one’s own socioeconomic status (SES), respectively. If more than one observed indicator variable is available to measure a particular latent construct, CFA allows the researcher to cluster these variables in prespecified, theory-driven ways to evaluate to what extent a particular data set “confirms” what is theoretically believed to be its underlying structure. Thus, the CFA approach to multivariate data analysis does not let a particular data set dictate, identify, or discover underlying dimensions [as is the case with other variable reduction techniques such as exploratory factor analysis (EFA) or principal components analysis (PCA)]; rather, it requires the researcher to theorize an underlying structure and assess whether the observed data “fits” this a priori specified model. In doing so, CFA provides a framework for addressing some of the problems associated with traditional ways of assessing a measure’s validity and reliability.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Recommended Readings
Byrne, B.M. (1989). A Primer of LISREL: Basic Applications and Programming for Confirmatory Factor Analytic Models. New York: Springer-Verlag.
Byrne, B.M. (1994). Structural Equation Modeling with EQS and EQS/Windows: Basic Concepts, Applications, and Programming. Thousand Oaks, CA: Sage.
Bollen, K.A., and Long, J.S. (Eds.). (1993). Testing Structural Equation Models. Newbury Park, CA: Sage.
Long, J.S. (1983a). Confirmatory Factor Analysis. Beverly Hills: Sage.
Nesselroade, J.R., and Cattell, R.B. (Eds.). (1988). Handbook of Multivariate Experimental Psychology. (2nd ed.). New York: Plenum Press.
Pedhazur, E.J., and Schmelkin, L. (1991). Measurement, Design, and Analysis: An Integrated Approach. Hillsdale, NJ: Lawrence Erlbaum.
Allen, M.J., and Yen, W.M. (1979). Introduction to Measurement Theory. Belmont, CA: Wadsworth.
Crocker, L., and Algina, J. (1986). Introduction to Classical and Modern Test Theory. Orlando, FL: Holt, Rinehart and Winston.
Gorsuch, R.L. (1983). Factor Analysis. (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.
McDonald, R.P. (1985). Factor Analysis and Related Methods. Hillsdale, NJ: Lawrence Erlbaum.
Mulaik, S.A. (1972). The Foundations of Factor Analysis. New York: McGraw-Hill.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1996 Springer-Verlag New York, Inc.
About this chapter
Cite this chapter
Mueller, R.O. (1996). Confirmatory Factor Analysis. In: Basic Principles of Structural Equation Modeling. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-3974-1_2
Download citation
DOI: https://doi.org/10.1007/978-1-4612-3974-1_2
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4612-8455-0
Online ISBN: 978-1-4612-3974-1
eBook Packages: Springer Book Archive