Abstract
Longitudinal structural equation modeling (SEM) can be a basis for making prescriptive statements on educational practice and offers yields over “traditional” statistical techniques under the general linear model. The extent to which prescriptive statements can be made will rely on the appropriate accommodation of key elements of research design, measurement, and theory. If these key elements are not adequately incorporated in educational SEM research, prescriptive statements become less justified, and in many cases, untenable. This is not to discount cross-sectional SEM as a basis for prescriptive considerations; however, it is more defensible to consider cross-sectional findings in terms of prescriptive possibilities and prescriptive inferences rather than prescriptive statements. This article examines what, when, and how SEM can contribute to prescriptive statements in education.
Similar content being viewed by others
References
Burkholder, G.J., Harlow, L.L. 2003. An illustration of a longitudinal cross-lagged design for larger structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 10, 465–486.
Burtless, G. 1996. Does money matter? The effect of school resources on student achievement and adult success. Washington, D.C.: Brookings Institution.
Byrne, B.M. 1984. The general/academic self-concept nomological network: A review of construct validation research. Review of Educational Research, 54, 427–456.
Cole, D.A., Maxwell, S.E. 2003. Testing meditational models with longitudinal data: Myths and tips in the use of structural equation modeling. Journal of Abnormal Psychology, 112, 558–577.
Grant, A.M., Wall, T.D. 2009. The neglected science and art of quasi-experimentation: Why-to, when-to, and how-to advice for organizational researchers. Organizational Research Methods, 12, 653–686.
Hancock, G.R., Mueller, R.O. (Eds.). 2006. Structural equation modeling: A second course. Greenwich, CO: Information Age Publishing.
Herzog, W., Boomsma, A. 2009. Small-sample robust estimators of noncentrality-based and incremental model fit. Structural Equation Modeling: A Multidisciplinary Journal, 16, 1–27.
Khoo, S.T. 2001. Assessing program effects in the presence of treatment-baseline interactions: A latent curve approach. Psychological Methods, 6, 234–257.
Little, T.D., Preacher, K.J., Selig, J.P., Card, N.A. 2007. New developments in latent variable panel analyses of longitudinal data. International Journal of Behavioral Development, 31, 357–365.
MacCallum, R.C., Austin, J.T. 2000. Applications of structural equation modeling in psychological research. Annual Review of Psychology, 51, 201–226.
Marsh, H.W., Byrne, B.M., Yeung, S.Y. 1999. Causal ordering of academic self-concept and achievement: Reanalysis of a pioneering study and revised recommendations. Educational Psychologist, 34, 155–167.
Marsh, H.W., Köller, O., Baumert, J. 2001. Reunification of East and West German school systems: Longitudinal multilevel modeling study of the big-fish-little-pond effect on academic self-concept. American Educational Research Journal, 38, 321–350.
Marsh, H.W., Martin, A.J., Hau, K.T. 2006a. A multiple method perspective on self-concept research in educational psychology: A construct validity approach. In M. Eid & E. Diener (Eds.), Handbook of multimethod measurement in psychology (pp. 441–456). Washington DC: American Psychological Association.
Marsh, H.W., Wen, Z., Hau, K.T. 2006b. Structural equation models of latent interaction and quadratic effects. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (pp. 225–265). Greenwich, CT: Information Age.
Marsh, H.W., Hau, K. T., Wen, Z. L. (2004) In search of golden rules: Comment on hypothesis testing approaches to setting cutoff values for fit indexes and dangers in overgeneralising Hu & Bentler (1999) findings. Structural Equation Modeling, 11, 320–341.
Martin, A.J. 2009. Age appropriateness and motivation, engagement, and performance in high school: Effects of age-within-cohort, grade retention, and delayed school entry. Journal of Educational Psychology, 101, 101–114.
Martin, A.J., Marsh, H.W. 2008. Academic buoyancy: Towards an understanding of students’ everyday academic resilience. Journal of School Psychology, 46, 53–83.
Martin, A.J., Liem, G.A. 2010. Academic Personal Bests (PBs), engagement, and achievement: A cross-lagged panel analysis. Learning and Individual Differences, 20, 265–270.
Martin, A.J., Marsh, H.W., Debus, R.L. 2001. A quadripolar need achievement representation of self-handicapping and defensive pessimism. American Educational Research Journal, 38, 583–610.
Martin, A.J., Marsh, H.W., Debus, R.L. 2003. Self-handicapping and defensive pessimism: A model of self-protection from a longitudinal perspective. Contemporary Educational Psychology, 28, 1–36.
Martin, A.J., Colmar, S.H., Davey, L.A., Marsh, H.W. 2010. Longitudinal modeling of academic buoyancy and motivation: Do the ‘5Cs’ hold up over time? The British Journal of Educational Psychology, 80, 473–496.
McArdle, J.J. 2009. Latent variable modeling of differences and changes with longitudinal data. Annual Review of Psychology, 60, 577–605.
Muthen, B.O., Curran, P.J. 1997. General longitudinal modeling of individual differences in experimental designs: A latent variable framework for analysis and power estimation. Psychological Methods, 2, 371–402.
Muthen, B.O., Khoo, S.-T. 1998. Longitudinal studies of achievement growth using latent variable modeling. Learning and Individual Differences, 10, 73–101.
Robinson, D.H., Levin, J.R., Thomas, G.D., Pituch, K.A., Vaughn, S. 2007. The incidence of ‘causal’ statements in teaching-and-learning research journals. American Educational Research Journal, 44, 400–413.
Russell, D.W., Kahn, J.H., Spoth, R., Altmaier, E.M. 1998. Analyzing data from experimental studies: A latent variable structural equation modeling approach. Journal of Counseling Psychology, 45, 18–29.
Schafer, J.L., Kang, J. 2008. Average causal effects from nonrandomized studies: A practical guide and simulated example. Psychological Methods, 13, 279–313.
Schneider, B., Carnoy, M., Kilpatrick, J., Schmidt, W.H., Shavelson, R.J. 2007. Estimating causal effects using experimental and observational designs. Washington, DC: American Educational Research Association.
Schumacker, R.E., Lomax, R.G. 2004. A beginner’s guide to structural equation modeling. Hillsdale, NJ: Erlbaum.
Schweizer, K. 2008. Investigating experimental effects within the framework of structural equation modeling: An example with effects on both error scores and reaction times. Structural Equation Modeling, 15, 327–345.
Shadish, W.R., Cook, T.D. 2009. The renaissance of field experimentation in evaluating interventions. Annual Review of Psychology, 60, 607–629.
Shavelson, R.J. 1996. Statistical reasoning for the behavioral sciences. Boston: Allyn & Bacon.
Temple, J.A., Reynolds, A.J., Ou, S.-R. 2004. Grade retention and school dropout: Another look at the evidence. In H. J. Walberg, A. J. Reynolds, & M. C. Wang (Eds.), Can unlike students learn together? Grade retention, tracking, and grouping (pp. 35–70). Greenwich: Information Age Publishing.
Tomarken, J.A., Waller, G.N. 2005. Structural equation modeling: Strengths, limitations, and misconceptions. Annual Review of Clinical Psychology, 1, 31–65.
Walls, T.A., Schafer, J.L. (Eds.). 2006. Models for intensive longitudinal data. Oxford: Oxford University Press.
West, S.G. 2009. Alternatives to randomized experiments. Current Directions in Psychological Science, 18, 299–304.
Wong, C.-S., Law, K.S. 1999. Testing reciprocal relations by nonrecursive structural equation models using cross-sectional data. Organizational Research Methods, 2, 69–87.
Acknowledgments
The author would like to thank Paul Ginns, Herb Marsh, Susan Colmar, Jasmine Green, and Gregory Liem for their input on perspectives presented in this article.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Martin, A.J. Prescriptive Statements and Educational Practice: What Can Structural Equation Modeling (SEM) Offer?. Educ Psychol Rev 23, 235–244 (2011). https://doi.org/10.1007/s10648-011-9160-0
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10648-011-9160-0