A note on the relation between factor analytic and item response theory models

Structural Equation Modeling

Volumn 15, Issue 1, 2008, Pages 136-153

  Kamata, Akihito ^a   Bauer, Daniel J ^b  

^a FLORIDA STATE UNIVERSITY   (United States)

^b The University of North Carolina at Chapel Hill   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BINARY FACTORS; FACTOR ANALYSIS MODEL; GENERAL FORMULAS; ITEM RESPONSE MODELS; ITEM RESPONSE THEORY;

STATISTICAL METHODS;

EID: 41149173765     PISSN: 10705511     EISSN: None     Source Type: Journal    
DOI: 10.1080/10705510701758406     Document Type: Article

References (11)

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2. Bock, R. D., & Aitkin, M. (1981). Marginal maximum likelihood estimation of item, parameters: An application of the EM algorithm. Psychometrika, 46, 443-459.
3. Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York: Guilford.
4. Goldstein, H., & Wood, R. (1989). Five decades of item response modeling. British Journal of Mathematical and Statistical Psychology, 42, 139-167.
5. McDonald, R. P. (1999). Test theory: Unified treatment. Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
6. Millsap, R. E., & Yun-Tein, J. (2004). Assessing factorial invariance in ordered-categorical measures. Multivariate Behavioral Research, 39, 479-515.
7. Muthén, B. O. (1978). Contributions to factor analysis of dichotomous variables. Psychometrika, 43, 551-560.
8. Muthén, B. O., & Asparouhov, T. (2002). Latent variable analysis with categorical outcomes: Multiple-group and growth modeling in Mplus (Mplus Web Note No. 4). Retrieved April 28, 2005, from http://www.statmodel.com/mplus/examples/webnote.html
9. Rijmen, F., Tuerlinckx, F., De Boeck, P., & Kuppens, P. (2003). A nonlinear mixed model framework for item response theory. Psychological Methods, 8, 185-205.
10. Takane, Y., & de Leeuw, J. (1987). On the relationship between item response theory and factor analysis of discretized variables. Psychometrika, 52, 393-408.
11. Zimowski, M. F., Muraki, E., Mislevy, R. J., & Bock, R. D. (1999). BILOG-MG: Multiple-group IRT analysis and test maintenance for binary items. Chicago: Scientific Software International.