Skip to main content
Log in

Person- and variable-centred quantitative analyses in educational research: insights concerning Australian students’ and teachers’ engagement and wellbeing

  • Published:
The Australian Educational Researcher Aims and scope Submit manuscript

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  • Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Thousand Oaks, CA: Sage.

    Google Scholar 

  • Angrist, J. D., & Pischke, J. S. (2008). Mostly harmless econometrics: An empiricist's companion. Princeton: Princeton University Press.

    Book  Google Scholar 

  • Bauer, D. J. (2007). Observations on the use of growth mixture models in psychological research. Multivariate Behavioral Research,42(4), 757–786.

    Article  Google Scholar 

  • Berger, N., Mackenzie, E., & Holmes, K. (2020). Positive attitudes towards mathematics and science are mutually beneficial for student achievement: A latent profile analysis of TIMSS 2015. Australian Educational Researcher. https://doi.org/10.1007/s13384-020-00379-8.

    Article  Google Scholar 

  • Chubb, I., Findlay, C., Du, L., Burmester, B., & Kusa, L. (2012). Mathematics, engineering and science in the national interest. Retrieved May 12, 2016 from https://www.chiefscientist.gov.au/wp-content/uploads/Office-of-the-Chief-Scientist-MES-Report-8-May-2012.pdf.

  • Crick, R. D. (2012). Deep engagement as a complex system: Identity, learning power, and authentic enquiry. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 675–694). New York: Springer.

    Chapter  Google Scholar 

  • Eccles, J. S. (2009). “Who am I and what am I going to do with my life?” Personal and collective identities as motivators of action. Educational Psychologist,44, 78–89. https://doi.org/10.1080/00461520902832368.

    Article  Google Scholar 

  • Eccles, J. S., Adler, T. F., Futterman, R., Goff, S. B., Kaczala, C. M., Meece, J., et al. (1983). Expectancies, values and academic behaviors. In J. T. Spence (Ed.), Achievement and achievement motives: Psychological and sociological approaches (pp. 75–146). San Francisco, CA: Freeman.

    Google Scholar 

  • Frenzel, A. C., Goetz, T., Pekrun, R., & Watt, H. M. G. (2010). Development of mathematics interest in adolescence: Influences of gender, family, and school context. Journal of Research on Adolescence,20(2), 507–537. https://doi.org/10.1111/j.1532-7795.2010.00645.x.

    Article  Google Scholar 

  • Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models (Vol. 1). New York: Cambridge University Press.

    Google Scholar 

  • Hair, J. F., Anderson, R. E., Tathan, R. L., & Black, W. C. (1995). Multivariate data analysis with readings (4th ed.). Englewood Cliffs, NJ: Prentice Hall.

    Google Scholar 

  • Hox, J. J., & Maas, C. J. M. (2001). The accuracy of multilevel structural equation modeling with pseudobalanced groups and small samples. Structural Equation Modeling: A Multidisciplinary Journal,8(2), 157–174. https://doi.org/10.1207/S15328007SEM0802_1.

    Article  Google Scholar 

  • Hyde, J. S. (2005). The gender similarities hypothesis. American Psychologist,60, 581–592. https://doi.org/10.1037/0003-066X.60.6.581.

    Article  Google Scholar 

  • Jacobs, J. E., Lanza, S., Osgood, D. W., Eccles, J. S., & Wigfield, A. (2002). Changes in children’s self-competence and values: Gender and domain differences across grades one through twelve. Child Development,73, 509–527. https://doi.org/10.1111/1467-8624.00421.

    Article  Google Scholar 

  • Lin, H., Werner, K. M., & Inzlicht, M. (2020, March 3). Promises and perils of experimentation: Big-I triangulation offers solutions. https://doi.org/10.31234/osf.io/hwubj

  • Lumley, T. (2011). Complex surveys: A guide to analysis using R (Vol. 565). Hoboken, NJ: Wiley.

    Google Scholar 

  • Maas, C. M. J., & Hox, J. J. (2004). Robustness issues in multilevel regression analysis. Statistica Nederlandica,58(2), 127–137.

    Article  Google Scholar 

  • Magnussen, D. (2000). The individual as the organizing principle in psychological inquiry: A holistic approach. In L. R. Bergman, R. B. Cairns, L. Nilsson, & L. Nystedt (Eds.), Developmental science and the holistic approach. Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Marsh, H. W., & Hau, K. T. (2007). Applications of latent-variable models in educational psychology: The need for methodological-substantive synergies. Contemporary Educational Psychology,32(1), 151–170.

    Article  Google Scholar 

  • Marsh, H. W., Lüdtke, O., Nagengast, B., Trautwein, U., Morin, A. J. S., Abduljabbar, A. S., et al. (2012). Classroom climate and contextual effects: Conceptual and methodological issues in the evaluation of group-level effects. Educational Psychologist,47, 106–124. https://doi.org/10.1080/00461520.2012.670488.

    Article  Google Scholar 

  • Marsh, H. W., Morin, A. J., Parker, P. D., & Kaur, G. (2014). Exploratory structural equation modeling: An integration of the best features of exploratory and confirmatory factor analysis. Annual Review of Clinical Psychology,10, 85–110.

    Article  Google Scholar 

  • Morgan, S. L., & Winship, C. (2015). Counterfactuals and causal inference. Cambridge: Cambridge University Press.

    Google Scholar 

  • Morin, A. J. S., & Litalien, D. (2017). Webnote: Longitudinal tests of profile similarity and latent transition analyses. Montreal, QC: Substantive Methodological Synergy Research Laboratory.

    Google Scholar 

  • Mostafa, T. (2018). How do science teachers teach science and does it matter? PISA in Focus, No. 90. Paris: OECD Publishing. https://doi.org/10.1787/f3ac3fd6-en.

  • Nagy, G., Watt, H. M. G., Eccles, J. S., Trautwein, U., Lüdtke, O., & Baumert, J. (2010). The development of students’ mathematics self-concept in relation to gender: Different countries, different trajectories? Journal of Research on Adolescence,20, 482–506. https://doi.org/10.1111/j.1532-7795.2010.00644.x.

    Article  Google Scholar 

  • Pearl, J. (2009). Causality. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Pino-Pasternak, D., & Volet, S. (2020). Starting and staying strong: Pre-service primary teachers’ attitudinal profiles towards science learning and their outcomes in an introductory science unit. Australian Educational Researcher. https://doi.org/10.1007/s13384-019-00372-w.

    Article  Google Scholar 

  • Rajendran, N., Watt, H. M. G., & Richardson, P. W. (2020). Teacher burnout and turnover intent. Australian Educational Researcher. https://doi.org/10.1007/s13384-019-00371-x.

    Article  Google Scholar 

  • Roeser, R. W., & Galloway, M. G. (2002). Studying motivation to learn in early adolescence: A holistic perspective. In T. Urdan & F. Pajares (Eds.), Academic motivation of adolescents: Adolescence and education. Greenwich, CT: Information Age Publishing.

    Google Scholar 

  • Smalley, R. T., & Hopkins, S. (2020). Social climate and avoidance of help-seeking in secondary mathematics classes. Australian Educational Researcher. https://doi.org/10.1007/s13384-020-00383-y.

    Article  Google Scholar 

  • Steinley, D., & Brusco, M. J. (2011a). Testing for validity and choosing the number of clusters in K-means clustering. Psychological Methods,16, 285–297.

    Article  Google Scholar 

  • Steinley, D., & Brusco, M. J. (2011b). K-means clustering and mixture model clustering: Reply to McLachlan (2011) and Vermunt (2011). Psychological Methods,16(1), 89–92. https://doi.org/10.1037/a0022679.

    Article  Google Scholar 

  • Vermunt, J. K. (2011). K-means may perform as well as mixture model clustering but may also be much worse: Comment of Steinley and Brusco (2011). Psychological Methods,16, 82–88.

    Article  Google Scholar 

  • Watt, H. M. G. (2004). Development of adolescents’ self-perceptions, values and task perceptions according to gender and domain in 7th through 11th grade Australian students. Child Development,75, 1556–1574. https://doi.org/10.1111/j.1467-8624.2004.00757.x.

    Article  Google Scholar 

  • Watt, H. M. G., Bucich, M., & Dacosta, L. (2019). Adolescents’ motivational profiles in mathematics and science: Associations with achievement striving, career aspirations and psychological wellbeing. Frontiers in Psychology,10, 244–266. https://doi.org/10.3389/fpsyg.2019.00990.

    Article  Google Scholar 

  • Watt, H. M. G., & Richardson, P. W. (2008). Motivations, perceptions, and aspirations concerning teaching as a career for different types of beginning teachers. Learning and Instruction,18, 408–428.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Helen M. G. Watt.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Watt, H.M.G., Parker, P.D. Person- and variable-centred quantitative analyses in educational research: insights concerning Australian students’ and teachers’ engagement and wellbeing. Aust. Educ. Res. 47, 501–515 (2020). https://doi.org/10.1007/s13384-020-00390-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13384-020-00390-z

Navigation