Data modeling: Structural equation modeling

Handbook of Complementary Methods in Education Research

Volumn , Issue , 2012, Pages 161-175

  Kim, Kevin H ^a   Bentler, Peter M ^b  

^a UNIVERSITY OF PITTSBURGH   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85084586270     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4324/9780203874769-14     Document Type: Chapter

References (53)

1. Arbuckle, J. L. (1996). Full information estimation in the presence of incomplete data. In G. A. Marcoulides& R. E. Schumacker (Eds.), Advanced structural equation modeling: Issues and techniques (pp. 243-277). Hillsdale, NJ: Lawrence Erlbaum Associates.
2. Bentler, P. M. (1990). Comparative fix indexes in structural models. Psychological Bulletin, 107(2), 238-246.
3. Bentler, P. M. (2004). EQS 6 structural equations program manual. Encino, CA: Multivariate Software.
4. Bentler, P. M., & Yuan, K. -H. (1999). Structural equation modeling with small samples: Test statistics. Multivariate Behavioral Research, 34, 181-197.
5. Berry, W. D. (1984). Nonrecursive causal models. Newbury Park, Calif.: Sage Publications.
6. Bollen, K. A. (1989). Structural equation modeling with latent variables. New York, NY: John Wiley & Sons.
7. Bollen, K. A. (2002). Latent variables in psychology and the social sciences. Annual Review of Psychology, 53, 605-634.
8. Browne, M. W. (1984). Asymptotically distribution-free methods for the analysis of covariance structures. British Journal of Mathematical and Statistical Psychology, 37, 62-83.
9. Browne, M. W., MacCallum, R. C., Kim, C.-T., Andersen, B. L., & Glaser, R. (2002).When tit indices and residuals are incompatible. Psychological Methods, 7(4), 403–421.
10. Byrne, B. M., & Worth Gavin, D. A. (1996). The Shavelson model revisited: Testing for the structure of academic self-concept across pre-, early, and late adolescents. Journal of Educational Psychology, 88(2), 215-228.
11. Chou, C.-P., & Bentler, P. M. (1990). Model modification in covariance structure modeling: A comparison among likelihood ratio, Lagrange multiplier, and Wald tests. Multivariate Behavioral Research, 25(1), 115-136.
12. Chou, C. P., & Bentler, P. M. (2002). Model modification in structural equation modeling by imposing constraints. Computational Statistics & Data Analysis, 41, 271-287.
13. Cliff, N. (1983). Some cautions concerning the application of causal modeling methods. Multivariate Behavioral Research, 18, 115-126.
14. Green, S. B., Thompson, M. S., & Poirier, J. (1999). Exploratory analyses to improve model fit: Errors due to misspecification and strategy to reduce their occurrence. Structural Equation Modeling, 6, 113-126.
15. Green, S. B., Thompson, M. S., & Poirier, J. (2001). An adjusted Bonferroni method for elimination of parameters in specification addition searches. Structural Equation Modeling, 8, 18-39.
16. Hancock, G. R. (1999). A sequential Scheffe-type respecification procedure for controlling Type 1 error in exploratory structural equation model modification. Structural Equation Modeling, 6, 158-168.
17. Hershberger, S. L. (2003). The growth of structural equation modeling: 1994-2001. Structural Equation Modeling, 70(1), 35–46.
18. Hoyle, R. H. (Ed.). (1995). Structural equation modeling: Concepts, issues, and applications. Thousand Oaks, CA: Sage Publications, Inc.
19. Hoyle, R. H., & Panter, A. T. (1995). Writing about structural equation models. In R. H. Hoyle (Ed.), Structural Equation Modeling: Concepts, Issues, and Applications (pp. xxii. 158-116, 289).
20. Hu, L.-T., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424-453.
21. Hu, L.-T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55.
22. Jackson, D. L. (2003). Revisiting sample size and number of parameter estimates: Some support for the N:q hypothesis. Structural Equation Modeling, 10, 128-141.
23. Jamshidian, M., & Bentler, P. M. (1999). ML estimation of mean and covariance structures with missing data using complete data routines. Journal of Educational and Behavioral Statistics, 24(1), 21-41
24. Joreskog, K. G., & Sorbom, D. (1984). LISREL VI users's guide. Mooresville, IN: Scientific Software International.
25. Joreskog, K. C., & Sorbom, D. (2001). LISREL 8 users's reference guide. Lincolnwood, IL: Scientific Software International.
26. Kenny, D. A., & McCoach, D. B. (2003). Effect of the number of variables on measures of fit in structural equation modeling. Structural Equation Modeling, 10(3), 333-351.
27. Kim, K. H. (2005). The relationship among fit indices, power, and sample size in structural equation modeling. Structural Equation Modeling, 12(3), 368-390.
28. Kim, K. H., & Bentler, P. M. (2002). Tests of homogeneity of means and covariance matrices for multivariate incomplete data. Psychometrika, 67, 609-624.
29. Kline, R. B. (1998). Principles and practice of structural equation modeling. New York: Guilford Publications, Inc.
30. Little, R. J. A., & Rubin, D. B. (1987). Statistical analysis with missing data. New York: Wiley.
31. Loehlin, J. C. (2004). Latent variable models: An introduction to factor, path, and structural equation analysis. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
32. MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130-149.
33. MacCallum, R. C., Roznowski, M., & Necowitz, L. B. (1992). Model modifications in covariance structure analysis: The problem of capitalization on chance. Psychological Bulletin, 11/(3), 490-504.
34. MacCallum, R. C., Widaman, K. F., Preacher, K. J., & Hong, S. (2001). Sample size in factor analysis: The role of model error. Multivariate Behavioral Research, 36, 611-637.
35. Marsh, H. W. (1992). Self Description Questionnaire (SDQ) P. a theoretical and empirical basis for the measurement of multiple dimensions of preadolescent self-concept: A test manual and research monograph. Macarthur, New South Wales: University of Western Sydney, Faculty of Education.
36. McDonald, R. P., & Ho, M.-H. R. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7(1), 64-82.
37. Mooijaart, A. (2003). Estimating the statistical power in small samples by empirical distributions. In H. Yanai, A. Okada, K. Shigemasu, Y. Kano, & J. J. Meulman (Eds.), New Development in Psychometrics. Tokyo: Springer-Verlag.
38. Mueller, R. O. (1996). Basic principles of structural equation modeling: An introduction to LISREL and EQS. New York: Springer-Verlag.
39. Muthén, L. K., & Muthén, B. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling, 9, 599-620.
40. Satorra, A. (2003). Power of 2 goodness-of-fit tests in structural equation models: The case of non-normal data. In H. Yanai, A. Okada, K. Shigemasu, Y. Kano, & J. J. Meulman (Eds.), New developments in psychometrics (pp. 57-68). Tokyo: Springer-Verlag.
41. Satorra, A., & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399419). Thousand Oaks, CA: Sage.
42. Satorra, A., & Bentler, P. M. (2001). A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66(4), 507-514.
43. Satorra, A., & Saris, W. E. (1985). Power of the likelihood ratio test in covariance structure analysis. Psychometrika, 50(1), 83-90.
44. Schumacker, R. E., & Lomax, R. G. (2004). A beginner's guide to structural equation modeling (2nd Ed.). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
45. Shavelson, R.I., Hubner, J. J., & Stanton, G. C. (1976). Self-concept: Validation of construct interpretations. Review of Educational Research, 46, 407441.
46. Steiger, J. H., & Lind, J. (1980, MONTH). Statistically based tests for the number of common factors. Paper presented at the meeting of the Psychometric Society, Iowa City, IA.
47. Tremblay, P. F., & Gardner, R. C. (1996). On the growth of structural equation modeling in psychological journals. Structural Equation Modeling, 3(2), 93-104.
48. von Eye, A., & Clogg, C. C. (Eds.). (1994). Latent variables analysis: Applications for developmental research. Thousand Oaks, CA: Sage Publications, Inc.
49. Yuan, K. H., & Bentler, P. M. (1997). Mean and covariance structure analysis: Theoretical and practical improvements. Journal of the American Statistical Association, 92, 767-774.
50. Yuan, K. H., & Bentler, P. M. (1998). Structural equation modeling with robust covariances. In A. Raftery (Ed.), Sociological methodology (pp. 363-396). Malden, MA: Blackwell.
51. Yuan, K. H., & Bentler, P. M. (2000). Three likelihood-based methods for mean and covariance structure analysis with nonnormal missing data. Sociological Methodology 2000 (pp. 165-200). Washington, DC: American Sociological Association.
52. Yuan, K. H., & Bentler, P. M. (2001). A unified approach to multigroup structural equation modeling with nonstandard samples. In G. A. Marcoulides & R. E. Schumacker (Eds.), New developments and techniques in structural equation modeling (pp. 35-56). Mahwah, NJ: Erlbaum.
53. Yuan, K. H., & Hayashi, K. (2003). Bootstrap approach to interference and power analysis based on three test statistics for covariance structure models. British Journal of Mathematical and Statistical Psychology, 56(1), 93-110.