The factorial structure of Ryff's Psychological Well-Being Scales in university students
The factorial structure of Ryff's Psychological Well-Being Scales in university students
Main Article Content
Abstract
The present investigation examines the factorial structure of Ryff's Scales of Psychological Well-Being in university students; 1,402 students took part in the study. Participants were randomized into two independent homogeneous (calibration and validation) subsamples. Various theoretical models proposed by previous research were subjected to confirmatory factor analysis. Our results indicate that the four factor model (self-acceptance, environmental mastery, purpose in life, and personal growth) with no latent factors show the best fit to the empirical data. These findings are discussed according to theoretical and empirical implications.
References
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Royer, R., & Royer, J. (2004). Comparing hand drawn and computer generated concept mapping. Journal of Computers in Mathematics and Science Teaching, 23(1), 67---81.
Ruiz-Primo, M. A., & Shavelson, R. J. (1996). Problems and issues in the use of concept maps in science assessment. Journal of Research in Science Teaching, 33, 569---600.
Schau, C., Mattern, N., Zeilik, M., Teague, K. W., & Weber, R. J. (2001). Select-and-fill-in concept map scores as a measure of students’ connected understanding of science. Educational & Psychology Measurement, 61(1), 136---158.
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Thomas, R., & Neilson, I. (1995). Harnessing simulations in the service of education: Interact simulation environment. Computers & Education, 25, 21---29.
Zeitz, L., & Anderson-Inman, L. (1992). The effects of computerbased formative concept mapping on learning high school science. Paper presented at the annual meeting of the American Educational Research Association, San Francisco.
Alonso, J. (2012). Psicología. Bachillerato. Madrid: McGraw-Hill.
Anderson-Inman, L., & Ditson, L. (1999). Computer based concept mapping: A tool for negotiating meaning. Learning and Leading with Technology, 26, 6---13.
Anderson-Inman, L., & Zeitz, L. (1993). Computer-based concept mapping: Active studying for active learner. The Computer Teacher, 6---8, 10---11.
Ausubel, D. P. (1963). The psychology of meaningful verbal learning. New York: Grune & Stratton.
Ausubel, D. P. (2002). Adquisición y retención del conocimiento. Una perspectiva cognitiva. Barcelona: Paidós.
Azevedo, R., Witherspoon, A., Chauncey, A., Burkett, C., & Fike, A. (2009). MetaTutor: A metacognitive tool for enhancing selfregulated learning. In R. Pirrone, R. Azevedo, & G. Biswas (Eds.), Proceedings of the AAI fall symposium on cognitive and metacognitive educational systems. Menlo Park, CA: AAAI Press, http://dx.org/995-4214-1-PB.pdf
Bai, X., Black, J. B., & Vitale, J. (2007). REAL: Learn with the assistance of a reflective agent. Agent-based systems for human learning conference, Hawaii.
Black, J. B. (1992). Types of knowledge representation. New York: CCTE Report Teachers College, Columbia University.
Cañas, A. (2004). CmapTools: A knowledge modelling and sharing environment. pp. 125---133. Pamplona: UPN., http://dx.doi.org/cmc.ihmc.us/papers/cmc2004-283.pdf
Chang, K., Sung, Y., & Chen, S. (2001). Learning through computerbased concept mapping with scaffolding aid. Journal of Computer Assisted Learning, 17, 21---33.
Charsky, D., & Mims, C. (2008). Integrating commercial off-theshelf video games into school curriculums. TechTrends, 52, 38---44.
Charsky, D., & Ressler, W. (2011). Games are made for fun: Lessons on the effect of concept maps in the classroom use of computer games. Computers & Education, 56, 604---615.
Crespi, L. P. (1942). Quantitative variation of incentive and performance in the white rat. The American Journal of Psychology, 55, 467---517.
Davis, J. M., Leelawong, K., Belynne, K., Bodenheimer, R., Biswas, G., Vye, N., et al. (2003). Intelligent user interface design for teachable agent systems. In ICIUI (pp. 26---33), doi:10.1.1.14.8457.pdf.
Del Bimbo, A., & Vicario, E. (1995). Specification by-example of virtual agents’ behavior. IEEE Transactions on Visualization and Computer Graphics, 1, 350---360.
Dunlosky, J., & Metcalfe, J. (2008). Metacognition. London: SAGE.
Finke, R. A. (1989). Interpretation of imagery-induced McCollough effects. Perception & Psychophysics, 30, 94---95.
Forbus, K., & Gentner, D. (1997). Qualitative mental models: Simulations or memories? In Proceedings of the 11th workshop on qualitative reasoning (pp. 1---8).
Gentner, D., & Stevens, A. (1983). Introduction. In D. Gentner, & A. Stevens (Eds.), Mental models (pp. 1---6). Hillsdale, NJ: LEA.
Glynn, S. M., Yeany, B. K., & Britton, R. H. (Eds.). (1991). The psychology of learning science. London: Routledge.
González Marqués, J., & Pelta, C. (2013). PSICO-A: A computational system for learning psychology. International Journal of Modern Education and Computer Science, 5, 10. http://dx.doi.org/ 10.5815/ijmecs.2013.10.01
Greenfield, P. M. (2010). Video games revisited. In R. van Eck (Ed.), Gaming and cognition: Theories and perspectives from the learning sciences (pp. 1---21). Hershey, PA: IGI Global.
Hagemans, M. G., van der Meij, H., & de Jong, T. (2013). The effects of a concept map-based support tool on simulationbased inquiry learning. Journal of Educational Psychology, 105, 1---24.
Hull, C. L. (1952). A behavior system: An introduction to behavior theory concerning the individual organism. North Haven, CT: Yale University Press.
Jonassen, D., & Land, S. (2012). Theoretical foundations of learning environments. London: Routledge.
Karpicke, J. D., & Blunt, J. R. (2011). Retrieval practice produces more learning than elaborative studying with concept mapping. Science, 331, 772---775. http://dx.doi.org/10.1126/ science.1199327
Lerdorf, R., Tatroe, K., & MacIntyre, P. (2006). Programming PHP (2nd ed.). New York: O’Reilly Media.
Mayer, R. E. (2004). Should there be a three-strikes rule against pure discovery learning. American Psychologist, 59, 14---19. http://dx.doi.org/10.1037/0003-066X.59.1.14
Mayor, J., Suengas, A., & González Marqués, J. (1993). (Metacognitive strategies) Estrategias metacognitivas. Madrid: Síntesis.
Novak, J. (1977). A theory of education. Ithaca, NY: Cornell University Press.
Novak, J. (1998). Learning, creating, and using knowledge: Concept maps as facilitative tools for schools and corporations. Mahwah, NJ: LEA.
Plotnick, E. (1997). Concept mapping: A graphical system for understanding the relationship between concepts (report No. EDO-IR-97-05). Syracuse, NY: Center for Science and Technology (ERIC Document Reproduction Service No. ED407938).
Roediger, H. L., Agarwal, P. K., McDaniel, M. A., & McDermott, K. (2011). Test-enhanced learning in the classroom: Long-term improvements from quizzing. Journal of Experimental Psychology: Applied, 17, 382---395.
Royer, R., & Royer, J. (2004). Comparing hand drawn and computer generated concept mapping. Journal of Computers in Mathematics and Science Teaching, 23(1), 67---81.
Ruiz-Primo, M. A., & Shavelson, R. J. (1996). Problems and issues in the use of concept maps in science assessment. Journal of Research in Science Teaching, 33, 569---600.
Schau, C., Mattern, N., Zeilik, M., Teague, K. W., & Weber, R. J. (2001). Select-and-fill-in concept map scores as a measure of students’ connected understanding of science. Educational & Psychology Measurement, 61(1), 136---158.
Slamecka, N. J., & Graf, P. (1978). The generation effect: Delineation of a phenomenon. Journal of Experimental Psychology: Human Learning & Memory, 4, 592---604, http//dx.doi.org/ worthylab.tamu.edu/Courses files/Generation %20EffectSlamecka
Thomas, R., & Neilson, I. (1995). Harnessing simulations in the service of education: Interact simulation environment. Computers & Education, 25, 21---29.
Zeitz, L., & Anderson-Inman, L. (1992). The effects of computerbased formative concept mapping on learning high school science. Paper presented at the annual meeting of the American Educational Research Association, San Francisco.