Current Psychology

, Volume 38, Issue 2, pp 367–375 | Cite as

The Bifactor Model of the Junior Metacognitive Awareness Inventory (Jr. MAI)

  • Hoi Kwan NingEmail author


Few self-report measures actually exist that were designed to assess school-aged children’s metacognition. This study examined the factor structure and validity of the Junior Metacognitive Awareness Inventory (Jr. MAI; Sperling et al. Contemporary Educational Psychology, 27, 51–79, 2002) in a sample of primary school children from Singapore. A bifactor model which includes one general factor of metacognition and two uncorrelated knowledge and regulation of cognition group factors yielded the best fit to the data. The general factor of metacognition was found to have high internal consistency and accounted for a greater amount of variance than the two specific factors of knowledge and regulation of cognition. Multigroup analyses offered empirical evidence for the measurement invariance of the bifactor model across gender and ethnic groups. The criterion validity of the model was also demonstrated by significant predictive associations with measures of learning strategies and mathematics achievement. These results provided initial support for the validity and reliability of the Jr. MAI for use with children in the Asian setting. The implications of the findings for future metacognitive research and assessment among children are discussed.


Metacognition Bifactor modeling Confirmatory factor analysis Measurement invariance 



The author would like to thank Professor David Hogan for allowing us to use the mathematics achievement test items from the Core 2 Research Programme.

Compliance with Ethical Standards


This study was funded by the Office of Education Research, National Institute of Education, Nanyang Technological University, Singapore (OER 38/12 NHK).

Conflict of Interest

Hoi Kwan Ning declares that she has no conflict of interest.

Ethical Approval

All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.


  1. 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.CrossRefGoogle Scholar
  2. Annevirta, T., & Vaurus, M. (2006). Developmental changes of metacognitive skill in elementary school students. The Journal of Experimental Education, 74, 197–225. doi: 10.3200/JEXE.74.3.195-226.CrossRefGoogle Scholar
  3. Artzt, A. F., & Armour-Thomas, E. (1992). Development of a cognitive-metacognitive framework for protocol analysis of mathematical problem solving in small groups. Cognition and Instruction, 9(2), 137–175. doi: 10.1207/s1532690xci0902_3.CrossRefGoogle Scholar
  4. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16, 76–94. doi: 10.1177/009207038801600107.CrossRefGoogle Scholar
  5. Baker, L. (1989). Metacognition, comprehension monitoring, and the adult reader. Educational Psychology Review, 1, 3–38. doi: 10.1007/BF01326548.CrossRefGoogle Scholar
  6. Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246. doi: 10.1007/BF01326548.CrossRefGoogle Scholar
  7. Bentler, P. M., & Bonett, D. G. (1980). Significant tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588–606. doi: 10.1037/0033-2909.88.3.588.CrossRefGoogle Scholar
  8. Biggs, J., Kember, D., & Leung, D. Y. P. (2001). The revised two-factor study process questionnaire: R-SPQ-2F. British Journal of Educational Psychology, 71, 133–149. doi: 10.1348/000709901158433.CrossRefGoogle Scholar
  9. Blank, L. (2000). A metacognitive learning cycle: A better warranty for student understanding? Science Education, 84, 486–506. doi: 10.1002/1098-237x(200007)84:4<486::aid-sce4>;2-u.CrossRefGoogle Scholar
  10. Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.CrossRefGoogle Scholar
  11. Brown, A. L. (1978). Knowing when, where, and how to remember: A problem of metacognition. In R. Glaser (Ed.), Advances in instructional psychology (pp. 367–406). New York: Halsted Press.Google Scholar
  12. Brown, M., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods and Research, 21, 230–258. doi: 10.1177/0049124192021002005.CrossRefGoogle Scholar
  13. Carr, M., Kurtz, B. E., Schneider, W., Turner, L. A., & Borkowski, J. G. (1989). Strategy acquisition and transfer among American and German children: Environmental influences on metacognitive development. Developmental Psychology, 25, 765–771. doi: 10.1037/0012-1649.25.5.765.CrossRefGoogle Scholar
  14. Chen, F. F. (2007). Sensitivity of goodness of fit indices to lack of measurement invariance. Structural Equation Modeling, 14, 464–504. doi: 10.1080/10705510701301834.CrossRefGoogle Scholar
  15. Chen, F. F., West, S. G., & Sousa, K. H. (2006). A comparison of bifactor and second-order models of quality of life. Multivariate Behavioral Research, 41, 189–225. doi: 10.1207/s15327906mbr4102_5.CrossRefGoogle Scholar
  16. Davidson, G. R., & Freebody, P. R. (1988). Cross-cultural perspectives on the development of metacognitive thinking. Hiroshima Forum for Psychology, 13, 21–31.Google Scholar
  17. Dufresne, A., & Kobasigawa, A. (1989). Children’s spontaneous allocation of study time: Differential and sufficient aspects. Journal of Experimental Child Psychology, 47, 274–296. doi: 10.1016/0022-0965(89)90033-7.CrossRefGoogle Scholar
  18. Elshout-Mohr, M., Meijer, J., van Daalen-Kapteijns, M. M., & Meeus, W. (2004). Joint Research into the AILI (Awareness of Independent Learning Inventory). Paper presented at the first EARLI-SIG on metacognition. Program and abstract book, p. 18. Amsterdam: University of Amsterdam.Google Scholar
  19. Flavell, J. H. (1978). Metacognitive development. In J. M. Scandura & C. J. Brainerd (Eds.), Structural process theories of complex human behavior (pp. 213–245). Ayphen & Rijn: Sijtoff & Noordhoff.Google Scholar
  20. Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive -developmental inquiry. American Psychologist, 34, 906–911. doi: 10.1037/0003-066X.34.10.906.CrossRefGoogle Scholar
  21. Graham, J. M. (2006). Congeneric and (essentially) tau-equivalent estimates of score reliability: What they are and how to use them. Educational and Psychological Measurement, 66(6), 930–944. doi: 10.1177/0013164406288165.CrossRefGoogle Scholar
  22. Güss, C. D., & Wiley, B. (2007). Metacognition of problem-solving strategies in Brazil, India, and the United States. Journal of Cognition and Culture, 7, 1–25. doi: 10.1163/156853707X171793.CrossRefGoogle Scholar
  23. Hair, J. F., Black, B., Babin, B., Anderson, R. E., & Tatham, R. L. (2005). Multivariate data analysis. Upper Saddle River: Prentice Hall.Google Scholar
  24. Jacobs, J. E., & Paris, S. G. (1987). Children’s metacognition about reading: Issues in definition, measurement, and instruction. Educational Psychologist, 22, 255–278. doi: 10.1080/00461520.1987.9653052.CrossRefGoogle Scholar
  25. Lockl, K., & Schneider, W. (2004). The effects of incentives and instructions on children’s allocation of study time. European Journal of Developmental Psychology, 1, 153–169. doi: 10.1080/17405620444000085.CrossRefGoogle Scholar
  26. Marton, F., & Säljö, R. (1976a). On qualitative differences in learning: I. Outcome and process. British Journal of Educational Psychology, 46, 4–11. doi: 10.1111/j.2044-8279.1976.tb02980.x.CrossRefGoogle Scholar
  27. Marton, F., & Säljö, R. (1976b). On qualitative differences in learning: II. Outcome as a function of the learner’s conception of the task. British Journal of Educational Psychology, 46, 115–127. doi: 10.1111/j.2044-8279.1976.tb02304.x.CrossRefGoogle Scholar
  28. Michalsky, T., Mevarech, Z. R., & Haibi, L. (2009). Elementary school children reading scientific texts: Effects of metacognitive instruction. The Journal of Educational Research, 102, 363–376. doi: 10.3200/JOER.102.5.363-376.CrossRefGoogle Scholar
  29. Muthén, L. K., & Muthén, B. O. (2014). Mplus user’s guide. Los Angeles: Muthén & Muthén.Google Scholar
  30. Ning, H. K. (2016). Examining heterogeneity in student metacognition: A factor mixture analysis. Learning and Individual Differences, 49, 373–377. doi: 10.1016/j.lindif.2016.06.004.CrossRefGoogle Scholar
  31. Ooi, G. L. (2006). The role of the developmental state and interethnic relations in Singapore. Asian Ethnicity, 6, 109–120. doi: 10.1080/14631360500135336.Google Scholar
  32. Pintrich, P. R., & Garcia, T. (1991). Student goal orientation and self-regulation in the college classroom. In M. L. Maehr & P. R. Pintrich (Eds.), Advances in Motivation and Achievement (Vol. 7) (pp. 371–402). Greenwich: JAI Press.Google Scholar
  33. Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1991). A manual for the use of the motivated strategies for learning questionnaire (MSLQ). Ann Arbor: National Centre for Research to Improve Postsecondary Teaching and Learning.Google Scholar
  34. Reise, S. P. (2012). The rediscovery of bifactor measurement models. Multivariate Behavioral Research, 47(5), 667–696. doi: 10.1080/00273171.2012.715555.CrossRefGoogle Scholar
  35. Roberts, M., & Erdos, G. (1993). Strategy selection and metacognition. Educational Psychology, 13, 259–266. doi: 10.1080/0144341930130304.CrossRefGoogle Scholar
  36. Schneider, W., & Lock, K. (2002). The development of metacognitive knowledge in children and adolescents. In T. J. Perfect & B. L. Schwartz (Eds.), Applied metacognition (pp. 224–257). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  37. Schraw, G., & Dennison, R. S. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19, 460–475. doi: 10.1006/ceps.1994.1033.CrossRefGoogle Scholar
  38. Schraw, G., Olafson, L., Weibel, M., & Sewing, D. (2012). Metacognitive knowledge and field-based science learning in an outdoor environmental education program. In A. Zohar & Y. J. Dori (Eds.), Metacognition in Science Education: Trends in Current Research, Contemporary Trends and Issues in Science Education (Vol. 40) (pp. 55–77). Netherlands: Springer. doi: 10.1007/978-94-007-2132-6_4.Google Scholar
  39. Sclove, S. L. (1987). Application of model-selection criteria to some problems in multivariate analysis. Psychometrika, 52, 333–343. doi: 10.1007/BF02294360.CrossRefGoogle Scholar
  40. Sperling, R. A., Howard, B. C., Miller, L. A., & Murphy, C. (2002). Measures of children’s knowledge and regulation of cognition. Contemporary Educational Psychology, 27, 51–79. doi: 10.1006/ceps.2001.1091.CrossRefGoogle Scholar
  41. Sperling, R. A., Howard, B. C., Staley, R., & DuBois, N. (2004). Metacognition and self-regulated learning constructs. Educational Research and Evaluation: An International Journal on Theory and Practice, 10, 117–139. doi: 10.1076/edre. Scholar
  42. Strohschneider, S., & Güss, C. D. (1998). Planning and problem solving: Differences between Brazilian and German students. Journal of Cross-Culture Psychology, 29, 695–716. doi: 10.1177/0022022198296002.CrossRefGoogle Scholar
  43. Vrugt, A., & Oort, F. J. (2008). Metacognition, achievement goals, study strategies and academic achievement: Pathways to achievement. Metacognition and Learning, 30, 123–146. doi: 10.1007/s11409-008-9022-4.CrossRefGoogle Scholar
  44. Wang, A. Y. (1993). Cultural-familial predictors of children’s metacognitive and academic performance. Journal of Research in Childhood Education, 7, 83–90. doi: 10.1080/02568549309594844.CrossRefGoogle Scholar
  45. Whitebread, D., Coltman, P., Pasternak, D. P., Sangster, C., Grau, V., Bingham, S., et al. (2009). The development of two observational tools for assessing metacognition and self-regulated learning in young children. Metacognition and Learning, 4, 63–85. doi: 10.1007/s11409-008-9033-1.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.National Institute of EducationNanyang Technological UniversitySingaporeSingapore

Personalised recommendations