References
- Abdulla, A. M., Runco, M. A., Alsuwaidi, H. N., & Alhindal, H. S. (2018). Obstacles to personal creativity among Arab women from the Gulf Cooperation Council countries. Creativity. Theories–Research-Applications, 5(1), 41–54. doi:https://doi.org/10.1515/ctra-2018-0003.
- Awofala, A. O., & Fatade, A. O. (2015). Validation of the domains of creativity scale for Nigerian preservice science, technology, and mathematics teachers. Electronic Journal of Research in Educational Psychology, 13(1), 131–150. doi:https://doi.org/10.14204/ejrep.35.14057.
- Baer, J., & Kaufman, J. C. (2017). The Amusement Park Theoretical Model of Creativity: An attempt to bridge the domain specificity/generality gap. In J. C. Kaufman, V. P. Glăveanu, & J. Baer (Eds.), Cambridge handbook of creativity across domains (pp. 8–17). Cambridge University Press.
- Baer, J., & Kaufman, J. C. (2005). Bridging generality and specificity: The amusement park theoretical (APT) model of creativity. Roeper Review, 27(3), 158–163. doi:https://doi.org/10.1080/02783190509554310.
- Bernaards, C. A., & Jennrich, R. I. (2005). Gradient projection algorithms and software for arbitrary rotation criteria in factor analysis. Educational and Psychological Measurement, 65(5), 676–696. doi:https://doi.org/10.1177/0013164404272507.
- Bolden, D. S., Harries, T. V., & Newton, D. P. (2010). Pre-service primary teachers’ conceptions of creativity in mathematics. Educational Studies in Mathematics, 73(2), 143–157. doi:https://doi.org/10.1007/s10649-009-9207-z.
- Brown, M. B., & Forsythe, A. B. (1974). Robust tests for the equality of variances. Journal of the American Statistical Association, 69(346), 364–367. doi:https://doi.org/10.1080/01621459.1974.10482955.
- Cattell, R. B., & Tsujioka, B. (1964). The importance of factor-trueness and validity, versus homogeneity and orthogonality, in test scales1. Educational and Psychological Measurement, 24(1), 3–30. doi:https://doi.org/10.1177/001316446402400101.
- Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14(3), 464–504. doi:https://doi.org/10.1080/10705510701301834.
- Christensen, A. P., Cotter, K. N., & Silvia, P. J. (2019). Reopening openness to experience: A network analysis of four openness to experience inventories. Journal of Personality Assessment, 101(6), 574–588. doi:https://doi.org/10.1080/00223891.2018.1467428.
- Coughlin, K. B. (2013). An analysis of factor extraction strategies: A comparison of the relative strengths of principal axis, ordinary least squares, and maximum likelihood in research contexts that include both categorical and continuous variables [Unpublished doctoral dissertation]. University of South Florida.
- DeYoung, C. G., Quilty, L. C., & Peterson, J. B. (2007). Between facets and domains: 10 aspects of the Big Five. Journal of Personality and Social Psychology, 93(5), 880–896. doi:https://doi.org/10.1037/0022-3514.93.5.880.
- Diedrich, J., Jauk, E., Silvia, P. J., Gredlein, J. M., Neubauer, A. C., & Benedek, M. (2018). Assessment of real-life creativity: The Inventory of Creative Activities and Achievements (ICAA). Psychology of Aesthetics, Creativity, and the Arts, 12(3), 304–316. doi:https://doi.org/10.1037/aca0000137.
- Ding, L., Velicer, W. F., & Harlow, L. L. (1995). Effects of estimation methods, number of indicators per factor, and improper solutions on structural equation modeling fit indices. Structural Equation Modeling: A Multidisciplinary Journal, 2(2), 119–143. doi:https://doi.org/10.1080/10705519509540000.
- Dollinger, S. J. (2003). Need for uniqueness, need for cognition, and creativity. The Journal of Creative Behavior, 37(2), 99–116. doi:https://doi.org/10.1002/j.2162-6057.2003.tb00828.x.
- Dostál, D., Plháková, A., & Záškodná, T. (2017). Domain‐specific creativity in relation to the level of empathy and systemizing. The Journal of Creative Behavior, 51(3), 225–239. doi:https://doi.org/10.1002/jocb.103.
- Dupuis, M., Meier, E., & Cuneo, F. (2019). Detecting computer-generated random responding in questionnaire-based data: A comparison of seven indices. Behavior Research Methods, 51(5), 2228–2237. doi:https://doi.org/10.3758/s13428-018-1103-y.
- Elisondo, R. C. (2020). Creative Actions Scale: A Spanish scale of creativity in different domains. Journal of Creative Behavior. Advance online publication. doi:https://doi.org/10.1002/jocb.447.
- Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. doi:https://doi.org/10.1037/1082-989X.4.3.272.
- Feist, G. J. (1998). A meta-analysis of personality in scientific and artistic creativity. Personality and Social Psychology Review, 2(4), 290–309. doi:https://doi.org/10.1207/s15327957pspr0204_5.
- Fleischer, A., Mead, A. D., & Huang, J. (2015). Inattentive responding in MTurk and other online samples. Industrial and Organizational Psychology, 8(2), 196–202. doi:https://doi.org/10.1017/iop.2015.25.
- Forero, C. G., Maydeu-Olivares, A., & Gallardo-Pujol, D. (2009). Factor analysis with ordinal indicators: A Monte Carlo study comparing DWLS and ULS estimation. Structural Equation Modeling: A Multidisciplinary Journal, 16(4), 625–641. doi:https://doi.org/10.1080/10705510903203573.
- Freedman-Doan, C., Wigfield, A., Eccles, J. S., Blumenfeld, P., Arbreton, A., & Harold, R. D. (2000). What am I best at? Grade and gender differences in children’s beliefs about ability improvement. Journal of Applied Developmental Psychology, 21(4), 379–402. doi:https://doi.org/10.1016/S0193-3973(00)00046-0.
- Glăveanu, V. P. (2014). The psychology of creativity: A critical reading. Creativity. Theories – Research – Applications, 1(1), 10–32. doi:https://doi.org/10.15290/ctra.2014.01.01.02.
- Holland, J. L. (1985). Making vocational choice: A theory of careers. Englewood Cliffs, NJ: Prentice Hall.
- Ivcevic, Z., & Mayer, J. D. (2009). Mapping dimensions of creativity in the life-space. Creativity Research Journal, 21(2–3), 152–165. doi:https://doi.org/10.1080/10400410902855259.
- Jauk, E., Benedek, M., & Neubauer, A. C. (2014). The road to creative achievement: A latent variable model of ability and personality predictors. European Journal of Personality, 28(1), 95–105. doi:https://doi.org/10.1002/per.1941.
- Kandemir, M. A., & Kaufman, J. C. (2020). The Kaufman Domains of Creativity Scale: Turkish validation and relationship to academic major. The Journal of Creative Behavior, 54(4), 1002–1012. doi:https://doi.org/10.1002/jocb.428.
- Kapoor, H., Reiter-Palmon, R., & Kaufman, J. C. (2021). Norming the muses: Establishing the psychometric properties of the Kaufman Domains of Creativity Scale. Journal of Psychoeducational Assessment, 073428292110083. Advance online publication. doi:https://doi.org/10.1177/07342829211008334.
- Kaufman, J. C. (2006). Self‐reported differences in creativity by ethnicity and gender. Applied Cognitive Psychology: The Official Journal of the Society for Applied Research in Memory and Cognition, 20(8), 1065–1082. doi:https://doi.org/10.1002/acp.1255.
- Kaufman, J. C. (2012). Counting the muses: Development of the Kaufman domains of creativity scale (K-DOCS). Psychology of Aesthetics, Creativity, and the Arts, 6(4), 298–308. doi:https://doi.org/10.1037/a0029751.
- Kaufman, J. C. (2019). Self-assessments of creativity: Not ideal, but better than you think. Psychology of Aesthetics, Creativity, and the Arts, 13(2), 187–192. doi:https://doi.org/10.1037/aca0000217.
- Kaufman, J. C., & Baer, J. (2004). Sure, I’m creative—but not in mathematics!: Self-reported creativity in diverse domains. Empirical Studies of the Arts, 22(2), 143–155. doi:https://doi.org/10.2190/26HQ-VHE8-GTLN-BJJM.
- Kaufman, J. C., & Beghetto, R. A. (2013). In praise of Clark Kent: Creative metacognition and the importance of teaching kids when (not) to be creative. Roeper Review, 35(3), 155–165. doi:https://doi.org/10.1080/02783193.2013.799413.
- Kaufman, J. C., Pumaccahua, T. T., & Holt, R. E. (2013). Personality and creativity in realistic, investigative, artistic, social, and enterprising college majors. Personality and Individual Differences, 54(8), 913–917. doi:https://doi.org/10.1016/j.paid.2013.01.013.
- Kaufman, J. C., Waterstreet, M. A., Ailabouni, H. S., Whitcomb, H. J., Roe, A. K., & Riggs, M. (2010). Personality and self-perceptions of creativity across domains. Imagination, Cognition and Personality, 29(3), 193–209. doi:https://doi.org/10.2190/IC.29.3.c.
- Kaufman, S. B. (2013). Opening up openness to experience: A four‐factor model and relations to creative achievement in the arts and sciences. The Journal of Creative Behavior, 47(4), 233–255. doi:https://doi.org/10.1002/jocb.33.
- Kaufman, S. B., Quilty, L. C., Grazioplene, R. G., Hirsh, J. B., Gray, J. R., Peterson, J. B., & DeYoung, C. G. (2016). Openness to experience and intellect differentially predict creative achievement in the arts and sciences. Journal of Personality, 84(2), 248–258. doi:https://doi.org/10.1111/jopy.12156.
- Kelley, K. (2018). MBESS: The MBESS R Package. R package version 4.4.3. https://CRAN.Rproject.org/package=MBESS
- Kelley, K., & Pornprasertmanit, S. (2016). Confidence intervals for population reliability coefficients: Evaluation of methods, recommendations, and software for composite measures. Psychological Methods, 21(1), 69–92. doi:https://doi.org/10.1037/a0040086.
- Kenny, D. A., & McCoach, D. B. (2003). Effect of the number of variables on measures of fit in structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 10(3), 333–351. doi:https://doi.org/10.1207/S15328007SEM1003_1.
- Kharkhurin, A. V., & Motalleebi, S. N. (2008). The impact of culture on the creative potential of American, Russian, and Iranian college students. Creativity Research Journal, 20(4), 404–411. doi:https://doi.org/10.1080/10400410802391835.
- Kirsch, C., Lubart, T., & Houssemand, C. (2015). Creative personality profile in social sciences: The leading role of autonomy. Creativity. Theories–Research-Applications, 2(2), 180–211. doi:https://doi.org/10.1515/ctra-2015-0020.
- Koo, T. K., & Li, M. Y. (2016). A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of Chiropractic Medicine, 15(2), 155–163. doi:https://doi.org/10.1016/j.jcm.2016.02.012.
- Korkmaz, S., Goksuluk, D., & Zararsiz, G. (2014). MVN: An R package for assessing multivariate normality. The R Journal, 6(2), 151–162. doi:https://doi.org/10.32614/RJ-2014-031.
- Kornilov, S. A., Kornilova, T. V., & Grigorenko, E. L. (2016). The cross‐cultural invariance of creative cognition: A case study of creative writing in US and Russian college students. New Directions for Child and Adolescent Development, (2016(151), 47–59. doi:https://doi.org/10.1002/cad.20149.
- Lai, K., & Green, S. B. (2016). The problem with having two watches: Assessment of fit when RMSEA and CFI disagree. Multivariate Behavioral Research, 51(2–3), 220–239. doi:https://doi.org/10.1080/00273171.2015.1134306.
- Lazarides, R., & Lauermann, F. (2019). Gendered paths into STEM-related and language-related careers: Girls’ and boys’ motivational beliefs and career plans in math and language arts. Frontiers in Psychology, 10, 1243. doi:https://doi.org/10.3389/fpsyg.2019.01243.
- Lebedeva, N., Schwartz, S. H., Van De Vijver, F. J., Plucker, J., & Bushina, E. (2019). Domains of everyday creativity and personal values. Frontiers in Psychology, 9(Article), 2681. doi:https://doi.org/10.3389/fpsyg.2018.02681.
- Lebedeva, N. M., & Bushina, E. V. (2015). Influence of personal values and motivation with creative behaviour and attitude to innovations. Psychology in Economics and Management, 7(1), 26–35. doi:https://doi.org/10.17150/2225-7845.2015.7(1).26-35.
- Lee, C. T., Zhang, G., & Edwards, M. C. (2012). Ordinary least squares estimation of parameters in exploratory factor analysis with ordinal data. Multivariate Behavioral Research, 47(2), 314–339. doi:https://doi.org/10.1080/00273171.2012.658340.
- Leikin, R., Subotnik, R., Pitta-Pantazi, D., Singer, F. M., & Pelczer, I. (2013). Teachers’ views on creativity in mathematics education: An international survey. ZDM, 45(2), 309–324. doi:https://doi.org/10.1007/s11858-012-0472-4.
- Leys, C., Ley, C., Klein, O., Bernard, P., & Licata, L. (2013). Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology, 49(4), 764–766. doi:https://doi.org/10.1016/j.jesp.2013.03.013.
- Lorenzo-Seva, U., Timmerman, M. E., & Kiers, H. A. (2011). The Hull method for selecting the number of common factors. Multivariate Behavioral Research, 46(2), 340–364. doi:https://doi.org/10.1080/00273171.2011.564527.
- Makarova, E. V., Kryukova, N. I., Sizova, Z. M., Grinenko, A. V., Erofeeva, M. A., & Bukalerova, L. A. (2019). Divergence of supreme values of Russian world and western civilization social and philosophical analysis. European Journal of Science and Theology, 15(3), 97–107.
- Maneesriwongul, W., & Dixon, J. K. (2004). Instrument translation process: A methods review. Journal of Advanced Nursing, 48(2), 175–186. doi:https://doi.org/10.1111/j.1365-2648.2004.03185.x.
- Markland, D. (2007). The golden rule is that there are no golden rules: A commentary on Paul Barrett’s recommendations for reporting model fit in structural equation modelling. Personality and Individual Differences, 42(5), 851–858. doi:https://doi.org/10.1016/j.paid.2006.09.023.
- Marsh, H. W., Hau, K. T., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler’s (1999) findings. Structural Equation Modeling: A Multidisciplinary Journal, 11(3), 320–341. doi:https://doi.org/10.1207/s15328007sem1103_2.
- McKay, A. S., Karwowski, M., & Kaufman, J. C. (2017). Measuring the muses: Validating the Kaufman Domains of Creativity Scale (K-DOCS). Psychology of Aesthetics, Creativity, and the Arts, 11(2), 216–230. doi:https://doi.org/10.1037/aca0000074.
- McKibben, W. B., & Silvia, P. J. (2017). Evaluating the distorting effects of inattentive responding and social desirability on self‐report scales in creativity and the arts. The Journal of Creative Behavior, 51(1), 57–69. doi:https://doi.org/10.1002/jocb.86.
- McNeish, D. (2018). Thanks coefficient alpha, we’ll take it from here. Psychological Methods, 23(3), 412–433. doi:https://doi.org/10.1037/met0000144.
- Meade, A. W., & Craig, S. B. (2012). Identifying careless responses in survey data. Psychological Methods, 17(3), 437–455. doi:https://doi.org/10.1037/a0028085.
- Motiejunaite, A., & Kravchenko, Z. (2008). Family policy, employment and gender-role attitudes: A comparative analysis of Russia and Sweden. Journal of European Social Policy, 18(1), 38–49. doi:https://doi.org/10.1177/0958928707084453.
- Muthén, L. K., & Muthén, B. O. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling: A Multidisciplinary Journal, 9(4), 599–620. doi:https://doi.org/10.1207/S15328007SEM0904_8.
- Niessen, A. S. M., Meijer, R. R., & Tendeiro, J. N. (2016). Detecting careless respondents in web-based questionnaires: Which method to use? Journal of Research in Personality, 63, 1–11. doi:https://doi.org/10.1016/j.jrp.2016.04.010.
- Nusbaum, E. C., & Silvia, P. J. (2011). Are openness and intellect distinct aspects of openness to experience? A test of the O/I model. Personality and Individual Differences, 51(5), 571–574. doi:https://doi.org/10.1016/j.paid.2011.05.013.
- Oleynick, V. C., DeYoung, C. G., Hyde, E., Kaufman, S. B., Beaty, R. E., & Silvia, P. J. (2017). Openness/intellect: The core of the creative personality. In G. J. Feist, R. Reiter-Palmon, & J. C. Kaufman (Eds.), Cambridge handbooks in psychology. The Cambridge handbook of creativity and personality research (pp. 9–27). New York, NY: Cambridge University Press. doi:https://doi.org/10.1017/9781316228036.002.
- Orekhovskaya, N. A., Chistyakov, A. A., Kryukova, N. I., Krokhina, J. A., Ospennikov, Y. V., & Makarova, E. V. (2019). Orthodoxy and modernity their contact facets in Russian society. European Journal of Science and Theology, 15(2), 67–77.
- Osborne, J. W. (2014). Best practices in exploratory factor analysis. Scotts Valley, CA: CreateSpace Independent Publishing.
- Paek, S. H. (2020). Implicit theories. In M. Runco & S. Pritzker (Eds.), Encyclopedia of Creativity (pp. 624–629). Cambridge, MA: Elsevier, Academic Press.
- Patston, T. J., Cropley, D. H., Marrone, R. L., & Kaufman, J. C. (2018). Teacher implicit beliefs of creativity: Is there an arts bias? Teaching and Teacher Education, 75, 366–374. doi:https://doi.org/10.1016/j.tate.2018.08.001.
- Perrine, N. E., & Brodersen, R. M. (2005). Artistic and scientific creative behavior: Openness and the mediating role of interests. The Journal of Creative Behavior, 39(4), 217–236. doi:https://doi.org/10.1002/j.2162-6057.2005.tb01259.x.
- Plháková, A., Dostál, D., & Záškodná, T. (2015). Hollandova typologie profesních zájmů ve vztahu k doménově specifické kreativitě [Holland’s typology of vocational interests in relation to domain-specific creativity]. Československá Psychologie, 59, 17–32.
- Pornprasertmanit, S., Miller, P., & Schoemann, A. (2013). simsem: SIMulated Structural Equation Modeling. R package version 0.5-3. http://CRAN.R-project.org/package=simsem
- Puccio, G. J., Miller, B., & Acar, S. (2019). Differences in creative problem‐solving preferences across occupations. The Journal of Creative Behavior, 53(4), 576–592. doi:https://doi.org/10.1002/jocb.241.
- Qin, S., Nelson, L., McLeod, L., Eremenco, S., & Coons, S. J. (2019). Assessing test–retest reliability of patient-reported outcome measures using intraclass correlation coefficients: Recommendations for selecting and documenting the analytical formula. Quality of Life Research, 28(4), 1029–1033. doi:https://doi.org/10.1007/s11136-018-2076-0.
- Reiter-Palmon, R., Illies, J. J., & Kobe-Cross, L. M. (2009). Conscientiousness is not always a good predictor of performance: The case of creativity. The International Journal of Creativity & Problem Solving, 19, 27–45.
- Reiter-Palmon, R., Robinson-Morral, E. J., Kaufman, J. C., & Santo, J. B. (2012). Evaluation of self-perceptions of creativity: Is it a useful criterion? Creativity Research Journal, 24(2–3), 107–114. doi:https://doi.org/10.1080/10400419.2012.676980.
- Revelle, W. (2019). psych: Procedures for psychological, psychometric, and personality research. R package version 1. 9.12. https://CRAN.R-project.org/package=psych.
- Rhemtulla, M., Brosseau-Liard, P. É., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological Methods, 17(3), 354–373. doi:https://doi.org/10.1037/a0029315.
- Rigdon, E. E. (1996). CFI versus RMSEA: A comparison of two fit indexes for structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 3(4), 369–379. doi:https://doi.org/10.1080/10705519609540052.
- Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. doi:https://doi.org/10.18637/jss.v048.i02.
- Ruscio, J., & Roche, B. (2012). Determining the number of factors to retain in an exploratory factor analysis using comparison data of known factorial structure. Psychological Assessment, 24(2), 282–292. doi:https://doi.org/10.1037/a0025697.
- Şahin, F. (2016). Kaufman Alanları Yaratıcılık Ölçeği’nin Türkçeye uyarlanması ve psikometrik özelliklerinin incelenmesi [Adaptation of the Kaufman Domains of Creativity Scale into Turkish and examination of its psychometric properties]. Ilköogretim Online, 15(3), 855–867. doi:https://doi.org/10.17051/io.2016.70479.
- Satorra, A., & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. Von Eye & C. C. Clogg (Eds.), Latent variable analysis: Applications for developmental research (pp. 399–419). Thousand Oaks, CA: Sage.
- Shchebetenko, S., Kalugin, A. Y., Mishkevich, A. M., Soto, C. J., & John, O. P. (2020). Measurement invariance and sex and age differences of the Big Five Inventory–2: Evidence from the Russian version. Assessment, 27(3), 472–486. doi:https://doi.org/10.1177/1073191119860901.
- Sijtsma, K. (2009). On the use, the misuse, and the very limited usefulness of Cronbach’s alpha. Psychometrika, 74(1), 107–120. doi:https://doi.org/10.1007/s11336-008-9101-0.
- Silvia, P., Rodriguez, R., Beaty, R., Frith, E., Kaufman, J. C., Loprinzi, P., & Reiter-Palmon, R. (2020). Measuring everyday creativity: A Rasch model analysis of the Biographical Inventory of Creative Behaviors (BICB) scale. PsyArXiv. doi:https://doi.org/10.31234/osf.io/3wq7c.
- 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(3), 139–148. doi:https://doi.org/10.1037/a0014940.
- Silvia, P. J., & Nusbaum, E. C. (2012). What’s your major? College majors as markers of creativity. The International Journal of Creativity and Problem Solving, 22(2), 31–43.
- Silvia, P. J., Wigert, B., Reiter-Palmon, R., & Kaufman, J. C. (2012). Assessing creativity with self-report scales: A review and empirical evaluation. Psychology of Aesthetics, Creativity, and the Arts, 6(1), 19–34. doi:https://doi.org/10.1037/a0024071.
- Snyder, H. T., Hammond, J. A., Grohman, M. G., & Katz-Buonincontro, J. (2019). Creativity measurement in undergraduate students from 1984–2013: A systematic review. Psychology of Aesthetics, Creativity, and the Arts, 13(2), 133–143. doi:https://doi.org/10.1037/aca0000228.
- Soto, C. J., & John, O. P. (2017). The next Big Five Inventory (BFI-2): Developing and assessing a hierarchical model with 15 facets to enhance bandwidth, fidelity, and predictive power. Journal of Personality and Social Psychology, 113(1), 117–143. doi:https://doi.org/10.1037/pspp0000096.
- Steinmetz, H. (2011). Estimation and comparison of latent means across cultures. In E. Davidov, P. Schmidt, & J. Billiet (Eds.), Cross-cultural analysis: Methods and applications (pp. 85–116). New York, NY: Routledge.
- Sternberg, R. J. (2018). A triangular theory of creativity. Psychology of Aesthetics, Creativity, and the Arts, 12(1), 50–67. doi:https://doi.org/10.1037/aca0000095.
- Tan, C. S., Tan, S. A., Cheng, S. M., Hashim, I. H. M., & Ong, A. W. H. (2019). Development and preliminary validation of the 20-item Kaufman Domains of Creativity Scale for use with Malaysian populations. Current Psychology. Advance online publication. doi:https://doi.org/10.1007/s12144-019-0124-8.
- Teo, T., & Fan, X. (2013). Coefficient alpha and beyond: Issues and alternatives for educational research. The Asia-Pacific Education Researcher, 22(2), 209–213. doi:https://doi.org/10.1007/s40299-013-0075-z.
- Timmerman, M. E., & Lorenzo-Seva, U. (2011). Dimensionality assessment of ordered polytomous items with parallel analysis. Psychological Methods, 16(2), 209–220. doi:https://doi.org/10.1037/a0023353.
- Torrance, E. P. (1995). Why fly? A philosophy of creativity. Norwood, NJ: Ablex Publishing Corporation.
- Tu, C., & Fan, F. (2015). Domain specificity of creativity: Conception and measurement. Advances in Psychology, 11(11), 648–656. doi:https://doi.org/10.12677/ap.2015.511084.
- Van der Eijk, C., & Rose, J. (2015). Risky business: Factor analysis of survey data–assessing the probability of incorrect dimensionalisation. PLOS ONE, 10(3), Article e0118900, e0118900. doi:https://doi.org/10.1371/journal.pone.0118900.
- Velicer, W. F., Eaton, C. A., & Fava, J. L. (2000). Construct explication through factor or component analysis: A review and evaluation of alternative procedures for determining the number of factors or components. In R. D. Goffin & E. Helmes (Eds.), Problems and solutions in human assessment: Honoring Douglas Jackson at seventy (pp. 41–71). Boston, MA: Kluwer.
- Warne, R. T., & Larsen, R. (2014). Evaluating a proposed modification of the Guttman rule for determining the number of factors in an exploratory factor analysis. Psychological Test and Assessment Modeling, 56, 104–123.
- Werner, C. H., Tang, M., Kruse, J., Kaufman, J. C., & Spörrle, M. (2014). The Chinese version of the Revised Creativity Domain Questionnaire (CDQ‐R): First evidence for its factorial validity and systematic association with the Big Five. The Journal of Creative Behavior, 48(4), 254–275. doi:https://doi.org/10.1002/jocb.51.
- Wolfradt, U., & Pretz, J. E. (2001). Individual differences in creativity: Personality, story writing, and hobbies. European Journal of Personality, 15(4), 297–310. doi:https://doi.org/10.1002/per.409.
- Xia, Y., & Yang, Y. (2019). RMSEA, CFI, and TLI in structural equation modeling with ordered categorical data: The story they tell depends on the estimation methods. Behavior Research Methods, 51(1), 409–428. doi:https://doi.org/10.3758/s13428-018-1055-2.
- Zaki, R., Bulgiba, A., Nordin, N., & Ismail, N. A. (2013). A systematic review of statistical methods used to test for reliability of medical instruments measuring continuous variables. Iranian Journal of Basic Medical Sciences, 16(6), 803–807.