Applying the Mixed Rasch Model to the Runco Ideational Behavior Scale

REFERENCES

• Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716–723. doi:10.1109/TAC.1974.1100705
   Web of Science ®Google Scholar
• Batey, M. (2007). A psychometric investigation of everyday creativity ( Unpublished Doctoral Thesis). University of London, London, UK.
   Google Scholar
• Bozdogan, H. (1987). Model selection and Akaike’s information criterion (AIC): The general theory and its analytical extensions. Psychometrika, 52, 345–370. doi:10.1007/BF02294361
   Web of Science ®Google Scholar
• Carson, S. H., Peterson, J. B., & Higgins, D. M. (2005). Reliability, validity, and factor structure of the creative achievement questionnaire. Creativity Research Journal, 17, 37–50. doi:10.1207/s15326934crj1701_4
   Web of Science ®Google Scholar
• Davis, G. A. (1989). Testing for creative potential. Contemporary Educational Psychology, 14, 257–274. doi:10.1016/0361-476X(89)90014-3
   Web of Science ®Google Scholar
• Eid, M., & Rauber, M. (2000). Detecting measurement invariance in organizational surveys. European Journal of Psychological Assessment, 16, 20–30. doi:10.1027//1015-5759.16.1.20
   Web of Science ®Google Scholar
• Guilford, J. P. (1967). Creativity: Yesterday, today and tomorrow. The Journal of Creative Behavior, 1(1), 3–14.
   Web of Science ®Google Scholar
• Hocevar, D. (1981). Measurement of creativity: Review and critique. Journal of Personality Assessment, 45, 450–464. doi:10.1207/s15327752jpa4505_1
   PubMed Web of Science ®Google Scholar
• Holland, J. L. (1961). Creative and academic performance among talented adolescents. Journal of Educational Psychology, 52, 136–147. doi:10.1037/h0044058
   Web of Science ®Google Scholar
• Jonson, B. (2005). Design ideation: The conceptual sketch in the digital age. Design Studies, 26, 613–624. doi:10.1016/j.destud.2005.03.001
   Web of Science ®Google Scholar
• Kaufman, J. C., & Beghetto, R. A. (2009). Beyond big and little: The four c model of creativity. Review of General Psychology, 13, 1–12. doi:10.1037/a0013688
   Web of Science ®Google Scholar
• Kaufman, J. C., Plucker, J. A., & Baer, J. (2008). Essentials of creativity assessment. New York, NY: John Wiley & Sons.
   Google Scholar
• Lazarsfeld, P. F., Henry, N. W. (1968). Latent structure analysis. Boston, MA: Houghton Mifflin.
   Google Scholar
• Li, F., Cohen, A. S., Kim, S.-H., & Cho, S.-J. (2009). Model selection methods for mixture dichotomous IRT models. Applied Psychological Measurement, 33, 353–373. doi:10.1177/0146621608326422
   Web of Science ®Google Scholar
• Ludwig, A. M. (1992). The creative achievement scale. Creativity Research Journal, 5, 109–119. doi:10.1080/10400419209534427
   Google Scholar
• Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47, 149–174. doi:10.1007/BF02296272
   Web of Science ®Google Scholar
• Milgram, R. M., & Hong, E. (1999). Creative out-of-school activities in intellectually gifted adolescents as predictors of their life accomplishment in young adults: A longitudinal study. Creativity Research Journal, 12, 77–87. doi:10.1207/s15326934crj1202_1
   Web of Science ®Google Scholar
• Mislevy, R. J., & Verhelst, N. (1990). Modeling item responses when different subjects employ different solution strategies. Psychometrika, 55, 195–215. doi:10.1007/BF02295283
   Web of Science ®Google Scholar
• Muthén, B., & Asparouhov, T. (2006). Item response mixture modeling: Application to tobacco dependence criteria. Addictive Behaviors, 31, 1050–1066. doi:10.1016/j.addbeh.2006.03.026
   PubMed Web of Science ®Google Scholar
• Preinerstorfer, D., & Formann, A. K. (2011). Parameter recovery and model selection in mixed Rasch models. British Journal of Mathematical and Statistical Psychology, 65, 251–262. doi:10.1111/j.2044-8317.2011.02020.x
   PubMed Web of Science ®Google Scholar
• Puccio, G. J., & Murdock, M. C. (Eds.). (1999). Creativity assessment: Readings and resources. Buffalo, NY: Creative Education Foundation Press.
   Google Scholar
• Rost, J. (1990). Rasch models in latent classes: An integration of two approaches to item analysis. Applied Psychological Measurement, 14, 271–282. doi:10.1177/014662169001400305
   Web of Science ®Google Scholar
• Rost, J. (1991). A logistic mixture distribution model for polytomous item response. British Journal of Mathematical and Statistical Psychology, 44, 75–92. doi:10.1111/j.2044-8317.1991.tb00951.x
   Web of Science ®Google Scholar
• Runco, M. A. (1986). Divergent thinking and creative performance in gifted and nongifted children. Educational and Psychological Measurement, 46, 375–384. doi:10.1177/001316448604600211
   Web of Science ®Google Scholar
• Runco, M. A. (1991). Divergent thinking. Norwood, NJ: Ablex.
   Google Scholar
• Runco, M. A. (2003). Education for creative potential. Scandinavian Journal of Educational Research, 47, 317–324. doi:10.1080/00313830308598
   Google Scholar
• Runco, M. A. (2004). Everyone has creative potential. In R. J. Sternberg, E. L. Grigorenko, & J. L. Singer (Eds.), Creativity: From potential to realization (pp. 21–30). Washington, DC: American Psychological Association.
   Google Scholar
• Runco, M. A., & Acar, S. (2012). Divergent thinking as an indicator of creative potential. Creativity Research Journal, 24, 66–75. doi:10.1080/10400419.2012.652929
   Web of Science ®Google Scholar
• Runco, M. A., Plucker, J. A., & Lim, W. (2000-2001). Development and psychometric integrity of a measure of ideational behavior. Creativity Research Journal, 13, 393–400. doi:10.1207/S15326934CRJ1334_16
   Web of Science ®Google Scholar
• Runco, M. A., & Richards, R. (Eds.). (1997). Eminent creativity, everyday creativity, and health. Norwood, NJ: Ablex.
   Google Scholar
• Runco, M. A., Walczyk, J. J., Acar, S., Cowger, E. L., Simundson, M., & Tripp, S. (2014). The incremental validity of a short form of the ideational behavior scale and usefulness of distractor, contraindicative, and lie scales. Journal of Creative Behavior, 48, 185–197. doi:10.1002/jocb.47
   Web of Science ®Google Scholar
• Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461–464. doi:10.1214/aos/1176344136
   Web of Science ®Google Scholar
• Sen, S. (2014). Robustness of mixture IRT models to violations of latent normality ( Unpublished doctoral dissertation). University of Georgia, Athens, Georgia.
   Google Scholar
• Silvia, P. J., Kaufman, J. C., & Pretz, J. E. (2009). Is creativity domain-specific? Latent class models of creative accomplishments and creative self-descriptions. Psychology of Aesthetics, Creativity, and the Arts, 3, 139–148. doi:10.1037/a0014940
   Web of Science ®Google Scholar
• von Davier, M. (2001). WINMIRA 2001 [Computer software]. St. Paul, MN: Assessment Systems Corporation.
   Google Scholar
• Von Stumm, S., Chung, A., & Furnham, A. (2011). Creative ability, creative ideation and latent classes of creative achievement: What is the role of personality? Psychology of Aesthetics, Creativity, and the Arts, 5, 107–114. doi:10.1037/a0020499
   Web of Science ®Google Scholar