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Full-Information Item Factor Analysis
Open accessAbstract
A method of item factor analysis based on Thur stone's multiple-factor model and implemented by marginal maximum likelihood estimation and the EM algorithm is described. Statistical significance of suc cessive factors added to the model is tested by the likelihood ratio criterion. Provisions for effects of guessing on multiple-choice items, and for omitted and not-reached items, are included. Bayes constraints on the factor loadings are found to be necessary to suppress Heywood cases. Numerous applications to simulated and real data are presented to substantiate the accuracy and practical utility of the method. Index terms: Armed Services Vocational Aptitude Bat tery, Beta prior, EM algorithm, Item factor analysis, TESTFACT, Tetrachoric correlation.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
PROMAX: A QUICK METHOD FOR ROTATION TO OBLIQUE SIMPLE STRUCTURE
Hendrickson, A. E., & White, P. O. (1964) · British Journal of Statistical Psychology · doi:10.1111/j.2044-8317.1964.tb00244.x · Cited by 1,009
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mirt: A Multidimensional Item Response Theory Package for the R Environment
Chalmers, R. P. (2012) · Journal of Statistical Software · doi:10.18637/jss.v048.i06 · Cited by 2,643
A Generalized Partial Credit Model: Application of an EM Algorithm
Muraki, E. (1992) · Applied Psychological Measurement · doi:10.1177/014662169201600206 · Cited by 1,535
Self-citationThe Hamilton Depression Rating Scale: Has the Gold Standard Become a Lead Weight?
Bagby, R. M., Ryder, A. G., Schuller, D. R., & Marshall, M. B. (2004) · American Journal of Psychiatry · doi:10.1176/appi.ajp.161.12.2163 · Cited by 1,148
Item factor analysis: Current approaches and future directions.
Wirth, R. J., & Edwards, M. C. (2007) · Psychological Methods · doi:10.1037/1082-989x.12.1.58 · Cited by 910
The role of the bifactor model in resolving dimensionality issues in health outcomes measures
Reise, S. P., Morizot, J., & Hays, R. D. (2007) · Quality of Life Research · doi:10.1007/s11136-007-9183-7 · Cited by 747
Metadata
- Authors
- Bock, R. D., Gibbons, R., & Muraki, E.
- Venue
- Applied Psychological Measurement
- Publisher
- SAGE Publications
- Published
- 1988-09
- Volume
- 12
- Issue
- 3
- Pages
- 261-280
- ISSN
- 0146-6216 (ISSN-L)
- 1552-3497
- OpenAlex
- W2115265604
- Cited by
- 528
- References
- 2
- FWCI
- 11.27
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Bock, R. D., Gibbons, R., & Muraki, E. (1988). Full-Information Item Factor Analysis. Applied Psychological Measurement. https://doi.org/10.1177/014662168801200305
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