STUDIES OF A LATENT-CLASS SIGNAL-DETECTION MODEL FOR CONSTRUCTED-RESPONSE SCORING

ETS Research Report Series

Volumn 2008, Issue 2, 2008, Pages i-55

  DeCarlo, Lawrence T ^a  

^a Columbia University ^*  (United States)

Author keywords

agreement; classification; constructed responses; incomplete block design; latent class models; rater effects; signal detection theory

Indexed keywords

EID: 85167522866     PISSN: None     EISSN: 23308516     Source Type: Journal    
DOI: 10.1002/j.2333-8504.2008.tb02149.x     Document Type: Article

References (21)

1. nd ed.). Hoboken, NJ: Wiley.
2. Dayton, C. M. (1998). Latent class scaling analysis. Thousand Oaks, CA: Sage.
3. DeCarlo, L. T. (1998). Signal detection theory and generalized linear models. Psychological Methods, 3, 186–205.
4. DeCarlo, L. T. (2002). A latent class extension of signal detection theory, with applications. Multivariate Behavioral Research, 37, 423–451.
5. DeCarlo, L. T. (2005). A model of rater behavior in essay grading based on signal detection theory. Journal of Educational Measurement, 42, 53–76.
6. DeCarlo, L. T., & Kim, Y. K. (2008, March). Score resolution in essay grading: A view from a signal detection model of rater behavior. Paper presented at the annual meeting of the American Educational Research Association, New York.
7. Flora, D. B., & Curran, P. J. (2004). An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data. Psychological Methods, 9, 466–491.
8. Heinen, T. (1996). Latent class and discrete latent trait models: Similarities and differences. Thousand Oaks, CA: Sage.
9. Hosmer, D. W., & Lemeshow, S. (1989). Applied logistic regression. New York: Wiley.
10. Kendall, M. G., & Smith, B. B. (1939). The problem of m rankings. The Annals of Mathematical Statistics, 10, 275–287.
11. nd ed.). New York: Guilford Press.
12. McLachlan, G., & Peel, D. (2000). Finite mixture models. New York: John Wiley & Sons.
13. th ed.). Los Angeles: Muthén & Muthén.
14. Rindskopf, D. (2002). Infinite parameter estimates in logistic regression: Opportunities, not problems. Journal of Educational and Behavioral Statistics, 27, 147–161.
15. Rubin, D. B. (1976). Inference and missing data. Biometrika, 63, 581–592.
16. Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores Psychometrika Monograph No. 17.
17. Vermunt, J. K. (1997). LEM: A general program for the analysis of categorical data [Computer software and manual]. Retrieved from the Tilburg University Web site: http://www.uvt.nl/faculteiten/fsw/organisatie/departementen/mto/software2.html.
18. Vermunt, J. K., & Magidson, J. (2005). Technical guide for Latent Gold 4.0: Basic and advanced. Belmont, MA: Statistical Innovations.
19. Vermunt, J. K., & Magidson, J. (2007). LG-Syntax user's guide: Manual for Latent Gold 4.5 syntax module. Belmont, MA: Statistical Innovations.
20. Wickens, T. D. (2002). Elementary signal detection theory. New York: Oxford University Press.
21. Wilson, M., & Masters, G. N. (1993). The partial credit model and null categories. Psychometrika, 58, 87–99.