Methodological measurement fruitfulness of exploratory structural equation modeling (ESEM): New approaches to key substantive issues in motivation and engagement

Journal of Psychoeducational Assessment

Volumn 29, Issue 4, 2011, Pages 322-346

  Marsh, Herbert W ^a,b   Liem, Gregory Arief D ^a   Martin, Andrew J ^a   Morin, Alexandre J S ^a   Nagengast, Benjamin ^c  

^a UNIVERSITY OF SYDNEY   (Australia)

^b UNIVERSITY OF OXFORD   (United Kingdom)

^c KING SAUD UNIVERSITY   (Saudi Arabia)

Author keywords

differential item functioning; Factor analysis; measurement invariance; Motivation and Engagement

Indexed keywords

EID: 79961217818     PISSN: 07342829     EISSN: None     Source Type: Journal    
DOI: 10.1177/0734282911406657     Document Type: Article

References (25)

1. Asparouhov, T., & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling. Retrieved fromhttp://www.statmodel.com/download/EFACFA810.pdf.
2. Beauducel, A., & Herzberg, P.Y. (2006). On the performance of maximum likelihood versus means and variance adjusted weighted least squares estimation in CFA. Structural Equation Modeling: A Multidisciplinary Journal, 13(2), 186-203.
3. Chen, F.F. (2007). Sensitivity of goodness of fit indices to lack of measurement invariance. Structural Equation Modeling, 14, 464-504.
4. Cheung, G.W., & Rensvold, R.B. (2001). The effects of model parsimony and sampling error on the fit of structural equation models. Organizational Research Methods, 4, 236-264.
5. DiStefano, C. (2002). The impact of categorization with confirmatory factor analysis. Structural Equation Modeling, 9, 327-346.
6. Dolan, C.V. (1994). Factor analysis of variables with 2, 3, 5 and 7 response categories: A comparison of categorical variable estimators using simulated data. British Journal of Mathematical and Statistical Psychology, 47, 309-326.
7. Floyd, F.J., & Widaman, K.F. (1995). Factor analysis in the development and refinement of clinical assessment instruments. Psychological Assessment, 7, 286-299.
8. Halpern, D.F. (2006). Assessing gender gaps in learning and academic achievement. In P. A. Alexander & P. H. Winne (Eds.), Handbook of educational psychology (2nd ed., pp. 635-653). Mahwah, NJ: Lawrence Erlbaum.
9. Jöreskog, K.G., & Sörbom, D. (1979). Advances in factor analysis and structural equation models. Cambridge, MA: Abt Books.
10. Marsh, H.W., Balla, J.R., & Hau, K.T. (1996). An evaluation of incremental fit indices: A clarification of mathematical and empirical processes. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling techniques (pp. 315-353). Mahwah, NJ: Lawrence Erlbaum.
11. Marsh, H.W., Hau, K.-T., & Grayson, D. (2005). Goodness of fit evaluation in structural equation modeling. In A. Maydeu-Olivares & J. McCardle (Eds.), Psychometrics. A festschrift to Roderick P. McDonald. Mahwah, NJ: Lawrence Erlbaum.
12. Marsh, H.W., Hau, K.-T., & Wen, Z., (2004). In search of golden rules: Comment on hypothesis testing approaches to setting cut-off values for fit indices and dangers in overgeneralising Hu & Bentler's (1999) findings. Structural Equation Modeling, 11, 320-341.
13. Marsh, H.W., Lüdtke, O., Muthén, B., Asparouhov, T., Morin, A.J.S., Trautwein, U., & Nagengast, B. (2010). A new look at the big-five factor structure through exploratory structural equation modeling. Psychological Assessment, 22, 471-491.
14. Marsh, H.W., Lüdtke, O., Robitzsch, A., Nagengast, B., Morin, A.J.S., & Trautwein, U. (in press). Two wrongs do not make a right: Camouflaging misfit with item-parcels in CFA models.
15. Marsh, H.W., Muthén, B., Asparouhov, T., Lüdtke, O., Robitzsch, A., Morin, A.J.S., & Trautwein, U. (2009). Exploratory structural equation modeling, integrating CFA and EFA: Applications to students' evaluations of university teaching. Structural Equation Modeling, 16, 439-476.
16. Martin, A.J. (2007). Examining a multidimensional model of student motivation and engagement using a construct validation approach. British Journal of Educational Psychology, 77, 413-440.
17. Martin, A.J. (2009). Motivation and engagement across the academic lifespan: A developmental construct validity study of elementary school, high school, and university/college students. Educational and Psychological Measurement, 69, 794-824.
18. Martin, A.J. (2010). Building classroom success: Eliminating academic fear and failure. London: Continuum.
19. Meredith, W. (1964). Rotation to achieve factorial invariance. Psychometrika, 29, 187-206.
20. Meredith, W., & Teresi, J. (2006). An essay on measurement and factorial invariance. Medical Care, 44(Suppl. 3), 69-77.
21. Muthén, B.O., & Kaplan, D. (1985). A comparison of some methodologies for the factor analysis of non-normal Likert variables. British Journal of Mathematical & Statistical Psychology, 38, 171-189. Muthén, L.K., & Muthén, B. (2008). Mplus user's guide. Los Angeles: Author.
22. Pintrich, P.R. (2003). A motivational science perspective on the role of student motivation in learning and teaching contexts. Journal of Educational Psychology, 95, 667-686.
23. Reise, S.P., Waller, N.G., & Comrey, A.L. (2000). Factor analysis and scale revision. Psychological Assessment, 12, 287-297.
24. Rhemtulla, M., Brosseau-Liard, P., & Savalei, V. (2010). How many categories is enough to treat data as continuous? A comparison of robust continuous and categorical SEM estimation methods under a range of non-ideal situations. Retrieved fromhttp://www.2.psych.ubc.ca/~mijke/files/HowManyCategories.pdf.
25. Sass, D.A., & Schmitt, T.A. (2010). A comparative investigation of rotation criteria within exploratory factor analysis. Multivariate Behavioral Research, 45, 73-103.