Why Sum Scores May Not Tell Us All About Test Takers

References 

1. Thorndike EL. Educational psychology. Mental work and fatigue and individual differences and their causes. Vol. III. New York (NY): Teachers College, Columbia University; 1914;
2. Nunnally J. Psychometric theory. New York (NY): McGraw-Hill; 1967;
3. Crowne DP, Marlowe DA. A new scale of social desirability independent of psychopathology. J Consult Psychol. 1964;24:349–354
   • MEDLINE
   • CrossRef
4. Edwards AL. The social desirability variable in personality assessment and research. New York (NY): Dryden Press; 1957;
5. Paulhus DL. Two-component models of socially desirable responding. J Pers Soc Psychol. 1984;46:598–609
   • CrossRef
6. Holland PW. The tyranny of continuous models in a world of discrete data. IHS-J. 1979;3:29–42
7. Haberman SJ. In: Analysis of qualitative data, I & II. New York (NY): Academic Press; 1978;p. 1979
8. Agresti A. Categorical data analysis. 2nd ed. New York: Wiley; 2002;
9. Andersen EB. The statistical analysis of categorical data. 3rd ed. Berlin, Germany: Springer-Verlag; 1994;
10. Lazarsfeld PF, Henry NW. Latent structure analysis. Boston: Houghton Mifflin; 1968;
11. Rasch G. Probabilistic models for some intelligence and attainment tests (expanded ed. Chicago (Ill): The University of Chicago Press; 1980;
12. Lord FM, Novick MR. Statistical theories of mental test scores. Reading (Mass): Addison-Welsley Publishing Company; 1968;
13. Rost J. Rasch models in latent classes: An integration of two approaches to item analysis. Appl Psychol Meas. 1990;14:271–282
14. von Davier M, Rost J. Polytomous mixed Rasch models. In:  Fischer GH,  Molenaar IW editor. Rasch models—Foundations, recent developments and applications. New York (NY): Springer-Verlag; 1995;p. 371–379
15. Kelderman H, Macready GB. The use of loglinear models for assessing differential item functioning across manifest and latent examinee groups. J Educ Meas. 1990;27:307–327
16. Mislevy RJ, Verhelst ND. Modeling item responses when different subjects employ different solution strategies. Psychometrika. 1990;55(2):195–215
    • CrossRef
17. von Davier M, Yamamoto K. Partially observed mixtures of IRT models: An extension of the generalized partial credit model. Appl Psychol Meas. 2004;28(6):389–406
18. Draney K, Wilson M. Application of the Saltus model to stage-like data: Some applications and current developments. In:  von Davier M,  Carstensen CH editor. Multivariate and mixture distribution Rasch models. New York: Springer; 2007;p. 119–130
19. Wilson M. Saltus: A psychometric model of discontinuity in cognitive development. Psychol Bull. 1989;105:276–289
    • CrossRef
20. Austin EJ, Deary IJ, Egan V. Individual differences in response scale use: Mixed Rasch modeling of responses to NEO-FFI items. Pers Individ Differ. 2006;40:1235–1245
21. Holden RR, Book AS. Using hybrid Rasch-latent class modeling to improve the detection of fakers on a personality inventory. Person Individ Differ. 2009;47(3):185–190
22. Rost J, Carstensen C, von Davier M. Applying the mixed Rasch model to personality questionnaires. In:  Rost J,  Langeheine R editor. Applications of latent trait and latent class models in the social sciences. New York (NY): Waxmann; 1997;p. 324–332Retrieved 1/22/2010 from: http://www.ipn.uni-kiel.de/aktuell/buecher/rostbuch/c31.pdf
23. Rost J, von Davier M. Measuring different traits in different populations with the same items. In:  Steyer R,  Wender KF,  Widaman KF editor. Psychometric methodology. Proceedings of the 7th European meeting of the Psychometric Society in Trier. Stuttgart, Germany: Gustav Fischer Verlag; 1993;p. 446–450
24. Strauss B, Buesch D, Tenenbaum G. Applications of generalized Rasch models in sport, exercise, and motor domains. In:  von Davier M,  Carstensen CH editor. Multivariate and mixture distribution Rasch models: Extensions and applications. New York: Springer; 2007;
25. Yamamoto KY, Everson HT. Modeling the effects of test length and test time on parameter estimation using the HYBRID model. In:  Rost J,  Langeheine R editor. Applications of latent trait and latent class models in the social sciences. Muenster, Germany: Waxmann; 1997;p. 89–98Retrieved 1/22/2010 from: http://www.ipn.uni-kiel.de/aktuell/buecher/rostbuch/c07.pdf
26. Costa PT, McCrae RR. NEO PI-R professional manual. Odessa (Fla): Psychological Assessment Resources; 1992;
27. Snyder M. Self-monitoring of expressive behavior. J Person Soc Psychol. 1974;30:526–537
28. von Davier M, Rost J. Self-monitoring—A class variable?. In:  Rost J,  Langeheine R editor. Applications of latent trait and latent class models in the social sciences. New York (NY): Waxmann; 1997;p. 296–304Retrieved 1/22/2010 from: http://www.ipn.uni-kiel.de/aktuell/buecher/rostbuch/c28.pdf
29. Snyder M, Gangestad S. On the nature of self-monitoring: Matters of assessment, matters of validity. J Person Soc Psychol. 1986;51(1):125–139
30. Boughton KA, Yamamoto K. A hybrid model for test speededness. In:  von Davier M,  Carstensen CH editor. Multivariate and mixture distribution Rasch models: Extensions and applications. New York (NY): Springer-Verlag; 2007;
31. Yamamoto K. A Hybrid model of IRT and latent class models, ETS Research Rep. No. RR-89-41. Princeton (NJ): ETS; 1989;
32. Bolt DM, Cohen AS, Wollack JA. Item parameter estimation under conditions of test speededness: Applications of a mixture Rasch model with ordinal constraints. J Educ Meas. 2002;39:331–348
33. von Davier M. Mixture distribution item response theory, latent class analysis, and diagnostic mixture models. In:  Embretson S editors. Measuring psychological constructs: Advances in model-based approaches. APA Press; 2009;p. 11–34
34. In:  von Davier M,  Carstensen CH editor. Multivariate and mixture distribution Rasch models: Extensions and applications. New York (NY): Springer-Verlag; 2007;
35. Eid M, Zickar MJ. Detecting response styles and faking in personality and organizational assessments by mixed Rasch models. In:  von Davier M,  Carstensen CH editor. Multivariate and mixture distribution Rasch models: Extensions and applications. New York (NY): Springer-Verlag; 2007;p. 255–270
36. Rijkes CPM, Kelderman H. Latent-response Rasch models for strategy shifts in problem-solving processes. In:  von Davier M,  Carstensen CH editor. Multivariate and mixture distribution Rasch models: Extensions and applications. New York (NY): Springer-Verlag; 2007;p. 311–328
37. Embretson SE. Mixed Rasch models for measurement in cognitive psychology. In:  von Davier M,  Carstensen CH editor. Multivariate and mixture distribution Rasch models: Extensions and applications. New York (NY): Springer-Verlag; 2007;
38. Smith JB, Batchelder WH. Assessing individual differences in categorical data. Psychon Bull Rev. 2008;15(4):713–731
    • CrossRef
39. von Davier M. WINMIRA — A program for analyses with the Rasch model, with the latent class analysis and with the mixed Rasch model. Kiel, Germany: IPN Software; 1994;[Computer software]
40. von Davier M. WINMIRA 2001. A Windows-program for analyses with the Rasch model, with the latent class analysis and with the mixed Rasch model. 2000;[Computer software]. Available from Assessment Systems Corporation: http://www.assess.com
41. Vermunt JK, Magidson J. Latent Gold [Computer software]. Available from http://www.statisticalinnovations.com; 2007.
42. von Davier M. mdltm. Software for the general diagnostic model and for estimating mixtures of multidimensional discrete latent traits, based on WINMIRA. Princeton (NJ): ETS; 2005;[Computer software]
43. Vermunt JK. LEM 1.0: A general program for the analysis of categorical data. Tilburg, The Netherlands: Tilburg University; 1997;[Computer software]