Doubly latent multilevel analyses of classroom climate: An illustration

Journal of Experimental Education

Volumn 82, Issue 2, 2014, Pages 143-167

  Morin, Alexandre J S ^a   Marsh, Herbert W ^a   Nagengast, Benjamin ^b   Scalas, L Francesca ^c  

^a UNIVERSITY OF WESTERN SYDNEY   (Australia)

^b UNIVERSITY OF TÜBINGEN   (Germany)

^c UNIVERSITY OF CAGLIARI   (Italy)

Author keywords

achievement; classroom climate; doubly latent multilevel structural equation models; self efficacy

Indexed keywords

EID: 84893942677     PISSN: 00220973     EISSN: 19400683     Source Type: Journal    
DOI: 10.1080/00220973.2013.769412     Document Type: Article

References (66)

1. Adelman, H. S., and, Taylor, L. 2002. " Classroom climate ". In Encyclopedia of school psychology, Edited by: Lee, S. W., Lowe, P. A. and Robinson, E. 304-312. Thousand Oaks, CA: Sage.
2. Baraldi, A. N., and, Enders, C. K. 2010. An introduction to modern missing data analyses. Journal of School Psychology, 48: 5-37.
3. Beauducel, A., and, Herzberg, P. Y. 2006. On the performance of maximum likelihood versus means and variance adjusted weighted least squares estimation in CFA. Structural Equation Modeling, 13: 186-203.
4. Beretvas, S. N. 2011. " Cross-classified and multiple-membership models ". In Handbook of advanced multilevel modeling, Edited by: Hox, J. J. and Roberts, J. K. 313-334. New York, NY: Routledge.
5. Bliese, P. D. 2000. " Within-group agreement, non-independence, and reliability: Implications for data aggregation and analysis ". In Multilevel theory, research, and methods in organizations, Edited by: Klein, K. J. and Kozlowski, S. W. 349-381. San Francisco, CA: Jossey-Bass.
6. Boorsboom, D. 2006. The attack of the psychometricians. Psychometrika, 71: 425-440.
7. Ciani, K. D., Middleton, M. J., Summers, J. J., and, Sheldon, K. M. 2010. Buffering against performance classroom goal structures: The importance of autonomy support and classroom community. Contemporary Educational Psychology, 35: 88-99.
8. Cohen, J. 1988. Statistical power analysis for the behavioral sciences, Mahwah, NJ: Erlbaum.
9. Cronbach, L. J. 1976. Research on classrooms and schools: Formulations of questions, design and analysis, Stanford, CA: Stanford Evaluation Consortium.
10. DiStefano, C. 2002. The impact of categorization with confirmatory factor analysis. Structural Equation Modeling, 9: 327-346.
11. 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.
12. Enders, C. K. 2001. The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data. Psychological Methods, 6: 352-370.
13. Enders, C. K. 2010. Applied missing data analysis, New York, NY: Guilford Press.
14. Enders, C. K., and, Bandalos, D. L. 2001. The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Structural Equation Modeling, 8: 430-457.
15. Enders, C. K., Dietz, S., Montague, M., and, Dixon, J. 2006. " Modern alternatives for dealing with missing data in special education research ". In Advances in learning and behavioral disorders, Edited by: Scruggs, T. E. and Mastropieri, M. A. Vol. 19, 101-130. New York, NY: Elsevier.
16. Enders, C. K., and, Tofighi, D. 2007. Centering predictor variables in cross-sectional multilevel models: A new look at an old issue. Psychological Methods, 12: 121-138.
17. Fast, L. A., Lewis, J. L., Bryant, M. J., Bocian, K. A., Cardullo, R. A., Rettig, M., and, Hammond, K. A. 2010. Does math self-efficacy mediate the effect of the perceived classroom environment in standardized math test performance?. Journal of Educational Psychology, 102: 729-740.
18. Fraser, B. J. 1998. Classroom environment instruments: Development, validity, and applications. Learning Environments Research, 1: 7-33.
19. Graham, J. W. 2009. Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60: 549-576.
20. Graham, J. W., Hofer, S. M., Donaldson, S. I., MacKinnon, D. P., and, Schafer, J. L. 1997. " Analysis with missing data in prevention research ". In The science of prevention: Methodological advances from alcohol and substance abuse research, Edited by: Bryant, K. J., Windle, M. and West, S. G. 325-366. Washington, DC: American Psychological Association.
21. Hedges, L. V., and, Hedberg, E. C. 2007. Intraclass correlation values for planning group-randomized trials in education. Educational Evaluation and Policy Analysis, 29: 60-87.
22. Jöreskog, K. G. 1979. " Statistical estimation of structural models in longitudinal developmental investigations ". In Longitudinal research in the study of behaviour and development, Edited by: Nesselrode, J. R. and Baltes, P. B. 305-351. New York, NY: Academic Press.
23. Karabenick, S. A., and, Maehr, M. L. 2004. MSP-Motivation Assessment Program: First-year report to the National Science Foundation. Retrieved from http://ma.mspnet.org/media/data/mspmap.pdf?media-000000005860.pdf
24. Karabenick, S. A., and, Maehr, M. L. 2007. MSP-Motivation Assessment Program: Final report to the National Science Foundation. Retrieved from http://ma.mspnet.org/media/data/MSP-MAP-Final-Report.pdf?media-000000006004.pdf
25. Larsen, R. 2011. Missing data imputation versus full information maximum likelihood with second level dependencies. Structural Equation Modeling, 18: 649-662.
26. Lau, S., and, Nie, Y. 2008. Interplay between personal goals and classroom goal structures in predicting student outcomes: A multilevel analysis of person-context interactions. Journal of Educational Psychology, 100: 15-29.
27. Little, R. J. A., and, Rubin, D. B. 1987. Statistical analysis with missing data, New York, NY: Wiley.
28. Lüdtke, O., Marsh, H. W., Robitzsch, A., and, Trautwein, U. 2011. A 2 × 2 taxonomy of multilevel latent contextual models: Accuracy-bias trade-offs in full and partial error-correction models. Psychological Methods, 16: 444-467.
29. Lüdtke, O., Marsh, H. W., Robitzsch, A., Trautwein, U., Asparouhov, T., and, Muthén, B. 2008. The multilevel latent covariate model: A new, more reliable approach to group-level effects in contextual studies. Psychological Methods, 13: 203-229.
30. Lüdtke, O., Robitzsch, A., Trautwein, U., and, Kunter, M. 2009. Assessing the impact of learning environments: How to use student ratings of classroom or school characteristics in multilevel modeling. Contemporary Educational Psychology, 34: 120-131.
31. Marsh, H. W. 2007a. Self-concept theory, measurement and research into practice: The role of self concept in educational psychology. 25th Vernon-Wall Lecture Series, London, UK: British Psychological Society.
32. Marsh, H. W. 2007b. " Application of confirmatory factor analysis and structural equation modeling in sport and exercise psychology ". In Handbook of sport psychology (3rd ed., Edited by: Tenenbaum, G. and Eklund, R. C. 774-798. Hoboken, NJ: Wiley.
33. Marsh, H. W., and, Craven, R. G. 2006. Reciprocal effects of self-concept and performance from a multidimensional perspective: Beyond seductive pleasure and unidimensional perspectives. Perspectives on Psychological Science, 1: 133-163.
34. Marsh, H. W., and, Hau, K.-T. 1996. Assessing goodness of fit: Is parsimony always desirable?. Journal of Experimental Education, 64: 364-390.
35. Marsh, H. W., and, Hau, K.-T. 2007. Applications of latent-variable models in Educal Psych: The need for methodological-substantive synergies. Contemporary Educational Psychology, 32: 151-171.
36. Marsh, H. W., and, OMara, A. 2008. Reciprocal effects between academic self-concept, self-esteem, achievement, and attainment over seven adolescent years: Unidimensional and multidimensional perspectives of self-concept. Personality and Social Psychology Bulletin, 34: 542-552.
37. Marsh, H. W., Hau, K.-T., and, Grayson, D. 2005. " Goodness of fit evaluation in structural equation modeling ". In Psychometrics. A Festschrift to Roderick P. McDonald, Edited by: Maydeu-Olivares, A. and McCardle, J. 275-340. Hillsdale, NJ: Erlbaum.
38. Marsh, H. W., Hau, K.-T., and, Wen, Z. 2004. In search of golden rules: Comment on hypothesis testing approaches to setting cutoff values for fit indexes and dangers in overgeneralising Hu & Bentler's (1999) findings. Structural Equation Modeling, 11: 320-341.
39. Marsh, H. W., Lüdtke, O., Nagengast, B., Trautwein, U., Morin, A. J. S., Abduljabbar, A. S., and, Köller, O. 2012. Classroom climate effects: Methodological issues in the evaluation of group-level effects. Educational Psychologist, 47: 106-124.
40. Marsh, H. W., Lüdtke, O., Robitzsch, A., Trautwein, U., Asparouhov, T., Muthén, B. O., and, Nagengast, B. 2009. Doubly-latent models of school contextual effects: Integrating multilevel and structural equation approaches to control measurement and sampling error. Multivariate Behavioral Research, 44: 764-802.
41. Marsh, H. W., Seaton, M., Kuyper, H., Dumas, F., Huguet, P., Régner, I., and, Gibbons, F. X. 2010. Phantom behavioral assimilation effects: Systematic biases in social comparison choice studies. Journal of Personality, 78: 671-710.
42. Marsh, H. W., Trautwein, U., Lüdtke, O., Köller, O., and, Baumert, J. 2005. Academic self-concept, interest, grades and standardized test scores: Reciprocal effects models of causal ordering. Child Development, 76: 297-416.
43. McDonald, R. P. 1970. Theoretical foundations of principal factor analysis, canonical factor analysis, and alpha factor analysis. British Journal of Mathematical & Statistical Psychology, 23: 1-21.
44. Mehta, P. D., and, Neale, M. C. 2005. People are variables too: Multilevel structural equations modeling. Psychological Methods, 10: 259-284.
45. Midgley, C., Maehr, M., Hruda, L., Anderman, E., Anderman, L., Freeman, K., and, Urdan, T. 2000. Manual for the Patterns of Adaptive Learning Scales (PALS), Ann Arbor, MI: University of Michigan.
46. Miller, A. D. Teacher-student relationships in classroom motivation: A critical review of goal structures. Paper presented at the 2006 meeting of the American Psychological Association, New Orleans, LA
47. Miller, A. D., and, Murdock, T. B. 2007. Modeling latent true scores to determine the utility of aggregate student perceptions as classroom indicators in HLM: The case of classroom goal structures. Contemporary Educational Psychology, 32: 83-104.
48. Morin, A. J. S., Maïano, C., Marsh, H. W., Nagengast, B., and, Janosz, M. in press. School life and adolescents self-esteem trajectories. Child Development
49. Muthén, B. O., and, Asparouhov, T. 2011. " Beyond multilevel regression modeling: Multilevel analysis in a general latent variable framework ". In Handbook of advanced multilevel modeling, Edited by: Hox, J. J. and Roberts, J. K. 15-40. New York, NY: Routledge.
50. Muthén, B. O., and, 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.
51. Muthén, L. K., and, Muthén, B. O. 2010. Mplus user's guide, Los Angeles, CA: Author.
52. Papaioannou, A., Marsh, H. W., and, Theodorakis, Y. 2004. A multilevel approach to motivational climate in physical education and sport settings: An individual or a group level construct?. Journal of Sport and Exercise Psychology, 26: 90-118.
53. Preacher, K. J., Zhang, Z., and, Zyphur, M. J. 2011. Alternative methods for assessing mediation in multilevel data: The advantages of multilevel SEM. Structural Equation Modeling, 18: 161-182.
54. Preacher, K. J., Zyphur, M. J., and, Zhang, Z. 2010. A general multilevel SEM framework for assessing multilevel mediation. Psychological Methods, 15: 209-233.
55. R Development Core Team. 2011. R: A language and environment for statistical computing, Vienna, Austria: R Foundation for Statistical Computing.
56. Rabe-Hesketh, S., Skrondal, A., and, Pickles, A. 2004. GLLAMM Manual (Tech Report 160), Berkeley, CA: University of California Berkeley Division of Biostatistics Working Paper Series.
57. Raudenbush, S. W., and, Bryk, A. S. 2002. Hierarchical linear models (2nd ed.), Thousand Oaks, CA: Sage.
58. Rhemtulla, M., Brosseau-Liard, P., and, 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 from http://www2.psych.ubc.ca/~mijke/files/ HowManyCategories.pdf
59. Robinson, W. S. 1950. Ecological correlations and the behavior of individuals. American Sociological Review, 15: 351-357.
60. Schafer, J. L. 1997. Analysis of incomplete multivariate data, Boca Raton, FL: Chapman & Hall.
61. Schwartz, S. 1994. The fallacy of the ecological fallacy: The potential misuse of a concept and the consequences. American Journal of Public Health, 84: 819-824.
62. Shin, T., Davidson, M. L., and, Long, J. D. 2009. Effects of missing data methods in structural equations modeling with nonnormal data. Structural Equation Modeling, 16: 70-98.
63. Sijtsma, K. 2009. On the use, misuse, and the very limited usefulness of Cronbach's alpha [Introduction to a special issue]. Psychometrika, 74: 107-120.
64. Skrondal, A., and, Laake, P. 2001. Regression among factor scores. Psychometrika, 66: 563-576.
65. Trickett, E. J., and, Moos, R. H. 1995. Classroom Environment Scale Manual (3rd ed.), Palo Alto, CA: Consulting Psychologists Press.
66. Valentine, J. C., DuBois, D. L., and, Cooper, H. 2004. The relation between self-beliefs and academic achievement: A meta-analytic review. Educational Psychologist, 39: 111-133.