Batey, M., Chamorro-Premuzic, T. & Furnham, A. (2009). Intelligence and personality as predictors of divergent thinking: the role of general, fluid and crystallised intelligence. Thinking Skills and Creativity, 4(1), 60−69.
Bernstein, I. H., Garbin, C., & Teng, G. (1988). Applied multivariate analysis. New York: Springer‑Verlag.
Bernstein, I. H. & Teng, G. (1989). Factoring items and factoring scales are different: Spurious evidence for multidimensionality due to item categorization. Psychological Bulletin, 105, 467-477. [CrossRef]
Brown, T. A. (2006). Confirmatory factor analysis for applied research. London: The Guilford Press.
Browne, M. W. & Cudek, R. (1993). Alternate ways of assessing model fit. In K.A.Bollen. & J. S.Long (Eds.), Testing structural equation models (pp.136-162). Newbury Park, CA: Sage.
Cronbach, L. J. (1951). Coefficient Alpha and the internal structure of a test. Psychometrika, 16, 297-334. [CrossRef]
Fiske, D. W. & Butler, J. M. (1963). The experimental conditions for measuring individual differences. Educational and Psychological Measurement, 23, 249-266. [CrossRef]
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. [CrossRef] [PubMed]
Harkness, J. A. (2003). Questionnaire translation. In J. A. Harkness, F.van de Vijver, & P. Ph. Mohler (Eds.), Cross-cultural survey methods (pp.35-56). New Jersey: Hoboken.
Hu, L. & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55.
Muthén, L. K. & Muthén, B. O. (1998-2010). Mplus user’s guide, 6th Edition. Los Angeles, CA: Muthén & Muthén.
Muthén, L. K. & Muthén, B. O. (2002). Mplus, 2nd Edition. Los Angeles, CA: Muthen & Muthen.
Nunnaly, J. & Bernstein, I. (1994). Psychometric theory. New York: McGraw-Hill.
Plucker, J. A., Makel, M. C. (2010). Assessment of creativity. In J. C. Kaufman & R J. Sternberg (Eds.), TheCambridge handbook of creativity (pp.48-73).
Plucker, J. A., Runco, M. A. & Lim, W. (2006). Predicting ideational behavior from divergent thinking and discretionary time on task. Creativity Research Journal, 18(1), 55-63. [CrossRef]
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y. & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88, 879-903. [CrossRef]
R Development Core Team (2011). R: A language and environment for statistical computing. R foundationfor statistical computing. Vienna, Austria. Retrieved December 20, 2011, from http://www.R-project. org/.
Raykov, T. & Marcoulides, G. A. (2011). Introduction to psychometric theory. New York: Routledge Taylor & Francis Group.
Revelle, W. (2009-2010). Psych: procedures for personality and psychological research. R package version 1.0-92. Retrieved December 20, 2011, from http://personality-project.org/.
Runco, M. A. & Richards, R. (1998). Eminent creativity, everyday creativity, and health. Greenwich, CT: Ablex.
Runco, M. A., Plucker, J. A. & Lim, W. (2000-2001). Development and psychometric integrity of a measure of ideational behavior. Creativity Research Journal, 13(3&4), 393-400.
Sijtsma, K. (2008). On the use, the misuse, and the very limited usefulness of Cronbach’s alpha. Psychometrika,74(1), 107-120. [Web of Science]
Steiger, J. H. & Lind, J. M. (1980). Statistically based tests for the number of common factors. Paper presented 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(2), 107-114.
Thurstone, L. L. (1947). Multiple-factor analysis. Chicago: University of Chicago Press.
Tucker, L. R. & MacCallum, R. C. (1997). Exploratory factor analysis. Unpublished manuscript.
Yu, C. Y. (2002). Evaluating cutoff criteria of model fit indices for latent variable models with binary andcontinuous outcomes. Submitted doctoral dissertation, University of California, Los Angeles.
Zinbarg, R. E., Revelle, W., Yovel, I. & Li, W. (2005). Cronbach’s α, Revelle’s β, and McDonald’s ωH : Their relations with each other and two alternative conceptualizations of reliability. Psychometrika, 70(1), 123-133. at the meeting of the Psychometric Society, Iowa City, IA. [CrossRef]
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Adaptation of Runco Ideational Behavior Scale in Latvia
1Daugavpils University, Riga Teacher Training and Educational Management Academy, Latvia
2Riga Teacher Training and Educational Management Academy, Latvia
This content is open access.
Citation Information: Journal of Pedagogy and Psychology "Signum Temporis". Volume 4, Issue 1, Pages 36–45, ISSN (Online) , ISSN (Print) 1691-4929, DOI: 10.2478/v10195-011-0043-4, February 2013
- Published Online:
Runco Ideational Behavior Scale (RIBS) was developed as an alternative instrument to measure creativity in contrast with divergent thinking tests which have only moderate predictive validity. Measure of RIBS is based on the assumption that ideas are the products of original, divergent, and creative thinking behaviour which one can observe and report about his/herself. The aim of the study was to adapt RIBS in Latvia. For the estimation of psychometric properties and constructing validity with confirmatory factor analysis (CFA) two samples were involved - initial sample (N=107) and comparison sample (N=130). After evaluating the Latvian version of RIBS, two issues were identified: the first pertains to reversed items which have no sufficient shared variance with the total scale while the second issue points to serious problems of model fit challenging one factor solution of ideational behaviour.
The possible reasons of these problems are discussed and further steps for RIBS development are proposed, including a change of response scale in order to test the effect of reversed items, establishing the validity with other instruments and investigating the appropriate number of factors to reveal more valid structure of ideational behaviour. Similar to the original RIBS the adapted version also has low factorial validity and thus is temporarily recommended only for studies with purpose to develop this instrument.